La plataforma propuesta de automatización de aceptación de baja tensión integra el Internet de las Cosas (IoT), la orquestación visual de flujos de trabajo, computación perimetral y en la nube, ingeniería de características y aprendizaje automático para automatizar las pruebas de aceptación de sistemas de baja tensión (LV). La plataforma permite la detección inteligente de fallas, toma de decisiones en tiempo real, aceptación automatizada, alta precisión, baja latencia operativa y despliegue escalable.
Artículo de investigación
Una plataforma de automatización de aceptación de bajo voltaje impulsada por procesos con orquestación de flujo de trabajo visual y validación mediante aprendizaje automático
En este artículo
Resumen
Resumen
The rapid expansion of low-voltage (LV) networks and their integration with distributed energy resources requires intelligent and automated management solutions. Cloud-edge collaborative Internet of Things (IoT) platforms support real-time monitoring, control, and data acquisition. However, existing platforms generally lack workflow automation, visual process orchestration, user-guided decision support, and comprehensive validation. Consequently, they do not provide process-driven solutions for automated LV network acceptance testing. This study presents the design and evaluation of a Low-Voltage Acceptance Automation Platform based on a visualized process canvas. The proposed platform adopts a process-driven architecture in which acceptance workflows are visually created, managed, and executed. The visual process canvas transforms conventional static monitoring into dynamic workflow automation by enabling real-time workflow execution, validation, and decision-making. The framework incorporates the Open LV Network & Smart Meter dataset to support realistic modeling of electrical load behavior. The workflow includes IoT-based data acquisition, data preprocessing, feature engineering, workflow orchestration using the visual process canvas, machine learning-based validation, and real-time dashboard visualization. The proposed framework achieved a fault detection rate of 98.4%, a receiver operating characteristic area under the curve (ROC-AUC) of 0.968, and an operational decision latency of 31 ms, outperforming traditional cloud-centric and IoT-based baseline approaches by approximately 30%–40%.
Introducción
La Industria 4.0 (I4.0)1 se posibilita mediante varias tecnologías emergentes, entre las cuales el Internet Industrial de las Cosas (IIoT) es una de las más importantes. El IIoT se refiere a la aplicación de tecnologías del Internet de las Cosas (IoT) en entornos industriales, donde dispositivos y máquinas interconectados se comunican principalmente mediante interacciones máquina a máquina. Debido a que los fallos en los sistemas industriales pueden provocar pérdidas operativas significativas, las aplicaciones de IIoT requieren una comunicación altamente confiable mientras generan volúmenes considerablemente mayores de datos que los sistemas convencionales de IoT2. Además, algunos estudios consideran al IIoT como un concepto estrechamente relacionado con la Industria 4.03. Las tecnologías IoT están transformando las arquitecturas jerárquicas tradicionales al permitir sistemas más flexibles con intercambio de datos continuo4. Numerosos estudios recientes han revisado las arquitecturas IIoT existentes y propuesto nuevos diseños de marcos5,6,7,8,9. A pesar de estos avances, persisten dos limitaciones importantes. Primero, se proponen con frecuencia nuevos marcos para superar las deficiencias de arquitecturas anteriores, lo que resulta en un número creciente de modelos alternativos que aumentan la diversidad arquitectónica y la complejidad de implementación. Segundo, el alto nivel de abstracción asociado con muchas arquitecturas propuestas limita su adopción práctica en entornos industriales10.
Se han reportado varias arquitecturas de la IIoT en la literatura. Se presentó una arquitectura de IIoT basada en el estándar de Comunicaciones de Plataforma Abierta (OPC), junto con un middleware en Python para gestionar la comunicación OPC11. Se propuso una arquitectura de IIoT de siete capas; sin embargo, no se proporcionaron detalles de implementación ni experimentos de validación12. Se describió un marco que integra tecnologías 5G e IIoT, junto con un caso de uso representativo, aunque la validación experimental fue limitada13. Se introdujo un marco de diseño emocional para la Industria 4.0, y se discutieron sus posibles implicaciones sin verificación experimental14. Una plataforma de IIoT multicapa fue evaluada en un banco de pruebas del mundo real, con énfasis en consideraciones de telecomunicaciones15. De manera similar, se presentó un marco modular de IoT para el monitoreo de la calidad del aire interior y se evaluó su rendimiento utilizando datos recopilados de 84 hogares con niños16.
El Internet de las Cosas (IoT) se ha convertido en una tecnología de rápida evolución con un potencial considerable para su aplicación en el sector de la energía eléctrica17,18. La integración de tecnologías IoT en los nodos del sistema eléctrico permite una generación, transmisión, distribución, operación y utilización de energía más inteligentes y eficientes, mediante la toma autónoma de decisiones apoyada por el monitoreo continuo en tiempo real del flujo de electricidad dentro de la red inteligente. En consecuencia, estos nodos inteligentes mejoran el rendimiento operativo general de la red eléctrica19. La tecnología de comunicación representa un componente fundamental en la implementación de redes inteligentes, ya que deben transmitirse de forma confiable dos categorías principales de información: mediciones de sensores y señales de control. Además de una baja latencia en la comunicación, las aplicaciones de redes inteligentes requieren una calidad de servicio (QoS) adecuada y una seguridad de datos suficiente para garantizar un funcionamiento confiable20.
La selección de una tecnología de comunicación adecuada para aplicaciones de redes inteligentes requiere considerar los intervalos de comunicación, las tasas de transmisión de datos y los costos de comunicación21. Las redes de comunicación IoT suelen estar compuestas por capas de software, seguridad, servicios, sistema y conectividad. Dentro de la capa de conectividad, los protocolos de comunicación generalmente se clasifican en tres categorías. Los protocolos de largo alcance incluyen Long Range (LoRa) y Narrowband-IoT (NB-IoT); los protocolos de mediano alcance incluyen Wi-Fi, 4G/LTE y 5G; y los protocolos de corto alcance incluyen Bluetooth y ZigBee. A medida que los mercados eléctricos se han desregulado cada vez más, la previsión de carga se ha vuelto más importante para evaluar los efectos de las condiciones climáticas, fallas de equipos, cambios regulatorios y otros factores operativos sobre el costo de los servicios eléctricos. En consecuencia, la previsión precisa de carga residencial se ha vuelto esencial para la operación y supervisión de redes inteligentes, microrredes y edificios inteligentes22.
El funcionamiento confiable de las redes de baja tensión (LV) depende de mantener una alta calidad de la energía23. La calidad de la energía está determinada por la eficacia de la regulación de voltaje, el equilibrio de corriente y la mitigación de perturbaciones técnicas bajo condiciones de operación desbalanceadas24. Entre estos factores, el balance de fases es particularmente importante porque determina la distribución uniforme de voltajes y corrientes entre las fases25. Un mal balance de fases puede aumentar el calentamiento de los conductores, las desviaciones de voltaje y las corrientes en el neutro, reduciendo así la eficiencia del sistema y su confiabilidad operativa26. Por consiguiente, el balance de fases debe considerarse durante el proceso de diseño para mejorar el rendimiento del sistema, la estabilidad operativa y la confiabilidad a largo plazo. Sin embargo, este aspecto rara vez se incorpora en las metodologías convencionales de diseño de mini-redes.
Los recientes avances en los sistemas de orquestación de flujos de trabajo han demostrado los beneficios de la automatización basada en eventos y la gestión visual de procesos en entornos de Internet Industrial de las Cosas. Sin embargo, su aplicación a la validación de redes de distribución y a las pruebas de aceptación en baja tensión sigue siendo limitada. La mayoría de los sistemas de automatización existentes en baja tensión se centran principalmente en el monitoreo y el control supervisorio, mientras que relativamente pocos estudios investigan la gestión de aceptación basada en flujos de trabajo o la verificación automática de cumplimiento. De manera similar, aunque las técnicas de aprendizaje automático han recibido considerable atención para la detección de anomalías en medidores inteligentes y la evaluación de la calidad del suministro eléctrico, estos enfoques generalmente se desarrollan como modelos analíticos independientes, en lugar de como componentes de un marco integrado de orquestación de flujos de trabajo.
En los últimos años se han propuesto diversas soluciones de medidores inteligentes de energía para la gestión de edificios inteligentes y la automatización del hogar27. En la referencia27 se presentó un prototipo de medidor inteligente para demostrar la integración de conceptos educativos universitarios en tecnologías de medición inteligente. El medidor inteligente desarrollado mostró un potencial significativo para la gestión y el control energético, reduciendo el consumo de energía y beneficiando tanto a las empresas de servicios como a los consumidores mediante comunicación bidireccional a través de Internet de las Cosas (IoT). Posteriormente, se introdujo un sistema de enchufe inteligente basado en microcontrolador para apoyar el monitoreo inteligente de energía28.
El sistema de enchufe inteligente propuesto muestra el consumo de energía en tiempo real mediante una interfaz gráfica de usuario basada en Android. Además, el dispositivo puede detectar la presencia de electrodomésticos conectados, una capacidad denominada medición invasiva de carga28. Para su implementación se utilizó un enchufe inteligente comercial de bajo costo. Las mejoras futuras incluyen el monitoreo de la corriente eficaz (RMS), la potencia activa, la potencia reactiva y el ángulo de fase entre voltaje y corriente. Como se describió en nuestros estudios previos, una microrred fotovoltaica parcialmente conectada a la red emplea los protocolos de comunicación ZigBee y LoRa para intercambiar información con dispositivos de medición inteligente, demostrando la viabilidad de la comunicación habilitada para Internet de las Cosas (IoT) en sistemas energéticos distribuidos29,30.
Para abordar las limitaciones de la planificación convencional de sistemas de distribución, la práctica de ingeniería y la investigación científica han avanzado en varias direcciones complementarias. Una línea de investigación se ha centrado en la planificación de alto nivel mediante sistemas de información geográfica (GIS) para la selección de emplazamientos y la evaluación preliminar de recursos31,32. Otra dirección se ha concentrado en mejorar componentes y subsistemas individuales, incluyendo la selección de conductores, el dimensionamiento óptimo de generación y almacenamiento de energía, y evaluaciones comparativas de sistemas de distribución de corriente alterna (AC) y corriente continua (DC)33,34,35,36. Una tercera línea de investigación ha aplicado algoritmos de optimización a la planificación de redes de distribución, al enrutamiento de alimentadores y al diseño de topologías de red. Los avances recientes incluyen marcos como la plataforma uGrid de los investigadores, que integra la colocación de dispositivos y la configuración de redes, pero carece de un análisis estándar de flujo de potencia para verificación y optimización. Otros enfoques dependen del ajuste manual de parámetros para la generación de topologías o se centran en aplicaciones especializadas, como microrredes de baja potencia en corriente continua37,38,39. Aunque estos enfoques representan un progreso significativo, se requiere más trabajo para establecer una metodología integrada para el diseño integral de sistemas de distribución de baja tensión. Además, las evaluaciones tecnoeconómicas exhaustivas de sistemas de baja tensión monofásicos, trifásicos e híbridos siguen siendo limitadas.
Los recientes avances en inteligencia artificial (AI) y aprendizaje automático han ampliado aún más las capacidades de los sistemas eléctricos inteligentes. Una revisión exhaustiva presentada resumió aplicaciones de inteligencia artificial y aprendizaje automático en redes eléctricas inteligentes, incluyendo tecnologías emergentes como el Aprendizaje Federado, la Inteligencia Artificial Generativa, los Modelos de Lenguaje Grande, la Inteligencia Artificial de las Cosas (AIoT) y los Gemelos Digitales40. La revisión destacó aplicaciones en la predicción de carga, mantenimiento predictivo, detección de anomalías, gestión por el lado de la demanda e integración de vehículos eléctricos, haciendo hincapié en los desafíos continuos relacionados con interoperabilidad, privacidad, escalabilidad, robustez y consideraciones éticas. Otra revisión reciente evaluó métodos de IA para la gestión distribuida de energía en infraestructuras de IoT-edge-cloud, centrándose en el aprendizaje automático, el aprendizaje por refuerzo y los sistemas multiagente para el control energético descentralizado41.
En conjunto, estudios previos han avanzado en el monitoreo habilitado por Internet de las Cosas (IoT), la medición inteligente, la computación en la nube y en el borde, la inteligencia artificial y la gestión inteligente de la energía para sistemas de distribución de baja tensión (LV). Sin embargo, estas tecnologías se han desarrollado en gran medida de forma independiente. La orquestación de flujos de trabajo, la gestión automatizada de aceptación, la validación basada en procesos y el apoyo integrado a la toma de decisiones siguen siendo limitados, lo que resalta la necesidad de un marco unificado de automatización orientado a flujos de trabajo.
Por lo tanto, este estudio presenta una Plataforma de Automatización de Aceptación de Bajo Voltaje basada en un Lienzo de Proceso Visualizado. El marco propuesto integra la adquisición de datos basada en IoT, el preprocesamiento de datos, la ingeniería de características, la orquestación de flujos de trabajo, la validación basada en aprendizaje automático, la computación nube-borde y la visualización en tiempo real dentro de una arquitectura unificada para apoyar pruebas automatizadas y confiables de aceptación de redes de bajo voltaje utilizando datos reales de medidores inteligentes. Figura 1 proporciona una visión general de la arquitectura orientada a flujos de trabajo de la plataforma propuesta. El flujo de trabajo comprende la adquisición de datos, el preprocesamiento, la ingeniería de características, la orquestación del flujo de trabajo, la toma inteligente de decisiones y la integración nube-borde, lo que permite la validación automatizada de redes de distribución de bajo voltaje mediante un marco basado en procesos. La plataforma demostró una alta precisión en la validación, al tiempo que redujo la latencia en las decisiones operativas y el tiempo de ejecución del flujo de trabajo en comparación con los enfoques convencionales.
Figura 1. Arquitectura orientada al flujo de trabajo de la plataforma propuesta de automatización de aceptación de baja tensión. Arquitectura de alto nivel de la plataforma propuesta de automatización de aceptación de baja tensión (LV) basada en procesos. El marco ilustra la integración secuencial de la adquisición de datos habilitada para IoT, el preprocesamiento de datos, la ingeniería de características, la orquestación del flujo de trabajo, la toma de decisiones inteligente, la colaboración entre la nube y el borde, y la aceptación automatizada para la validación de redes de distribución de baja tensión. IoT = Internet de las Cosas; LV = baja tensión; ML = aprendizaje automático. Haga clic aquí para ver una versión más grande de esta figura.
Protocolo
This study did not involve human participants, animals, or identifiable personal data. The analyses were performed using a publicly available dataset (UK Power Networks Open LV Network & Smart Meter dataset) together with synthetically generated data for validation; therefore, ethics committee approval and informed consent were not required. The research tools used in the protocol are listed in the Table of Materials.
1. Data acquisition
The Low-Voltage (LV) Acceptance Automation Platform was developed using electrical measurements acquired from smart meters, IoT sensors, and the Open LV Network & Smart Meter Dataset. Electrical parameters, including voltage, current, active power, reactive power, and energy consumption, were collected through Wi-Fi-, PLC-, and RS-485-based communication protocols. Real-time measurements were used for live system monitoring, whereas historical data supported model development and validation.
The proposed framework used the Open LV Network & Smart Meter Dataset provided by UK Power Networks42. The dataset comprises household smart meter consumption, feeder-level load profiles, and feeder voltage measurements. Time-series subsets containing load consumption and voltage data were selected for model training and evaluation. In addition, a simulated smart meter dataset was generated to represent operational conditions and fault scenarios required for workflow validation and machine learning model evaluation. The simulated dataset included timestamped measurements of voltage, current, active power, reactive power, energy consumption, total harmonic distortion (THD), and voltage imbalance. Fault samples were generated by simulating undervoltage, overvoltage, excessive harmonic distortion, and voltage imbalance based on established low-voltage operational thresholds. The dataset characteristics are summarized in Table 1.
| Attribute | Description |
| Dataset Name | Open LV Network & Smart Meter Dataset |
| Simulated Smart Meter Dataset | Geneated 1000 smaples for Fault simulation and workflow validation |
| Data Type | Time-series (historical smart meter data) |
| Data Granularity | Household-level and feeder-level measurements |
| Key Features | Voltage, current, active power, reactive power, energy consumption, timestamps |
| Data Frequency | High-resolution (e.g., half-hourly / minute-level depending on subset) |
| Coverage | Residential low-voltage distribution networks |
| Usage in Proposed Work | Model training, validation, load behavior analysis, and acceptance workflow evaluation |
Table 1: Dataset description. Summary of the Open LV Network & Smart Meter dataset used for model development and performance evaluation. The table presents the dataset components, measured electrical variables, sampling characteristics, and dataset partitioning for training, validation, and testing.
Because the Open LV Network & Smart Meter Dataset does not contain predefined fault labels, a rule-based labeling strategy was applied to both the real and simulated datasets. Samples satisfying normal operating limits were assigned a label of 0 (Normal), whereas samples exceeding operational thresholds (e.g., voltage deviations greater than ±10% of the nominal voltage, THD > 5%, or voltage unbalance > 2%) were assigned a label of 1 (Fault). These labels served as the ground truth during model training, validation, and testing. The proposed rule engine also allows threshold values to be configured in accordance with regional standards or utility-specific acceptance criteria.
The dataset was partitioned into training (70%), validation (15%), and testing (15%) subsets. Missing values were addressed using linear interpolation, mean imputation, and forward-fill techniques as described in the preprocessing stage. To ensure statistical robustness and reproducibility, all experiments were repeated five times using different random seeds.
2. Data preprocessing
Raw data from the Open LV Network & Smart Meter Dataset and IoT sensor measurements were preprocessed to improve data quality prior to feature extraction and model development. Missing values, noise, and inconsistencies caused by communication interruptions and sensor malfunctions were identified and corrected using interpolation, mean imputation, and forward-fill techniques. The detailed mathematical derivations are provided in Supplementary File 1 (Section S1).
The preprocessed time-series data were segmented into representative operational intervals (S₁–S₅ and Sk). Statistical and electrical features, including average load, peak load, minimum load, standard deviation, voltage deviation, and power consumption, were extracted from each segment. The extracted features are summarized in Table 2 and served as inputs for the subsequent feature engineering stage.
| Segment ID | Mean Load (kW) | Peak Load (kW) | Min Load (kW) | Std Deviation (kW) | Voltage Deviation (p.u.) | Power Consumption (kWh) | Time Interval |
| S₁ | 2.45 | 3.8 | 1.2 | 0.65 | 0.032 | 58.4 | 00:00–01:00 |
| S₂ | 2.9 | 4.1 | 1.5 | 0.72 | 0.028 | 64.2 | 01:00–02:00 |
| S₃ | 3.25 | 4.85 | 1.8 | 0.81 | 0.035 | 71.6 | 02:00–03:00 |
| S₄ | 3.8 | 5.2 | 2.1 | 0.9 | 0.041 | 78.9 | 03:00–04:00 |
| S₅ | 4.1 | 5.75 | 2.4 | 0.95 | 0.045 | 85.3 | 04:00–05:00 |
| Sk | 3.6 | 5.1 | 2 | 0.88 | 0.038 | 76.5 | tk |
Table 2: Representative feature representation extracted from time-series segments. Representative statistical, electrical, load behavior, voltage stability, and power quality features extracted from segmented LV time-series data during feature engineering. The values illustrate the engineered feature representation and are not experimental performance results. LF = Load Factor; PAR = Peak-to-Average Ratio; VSI = Voltage Stability Index; PF = Power Factor; THD = Total Harmonic Distortion.
3. Feature engineering
Feature engineering was performed to improve the representation of the preprocessed data for workflow orchestration and intelligent decision-making. Principal Component Analysis (PCA)^43 was applied to reduce data dimensionality by removing redundant and highly correlated variables while retaining the most informative feature components. The mathematical derivations are provided in Supplementary File 1 (Section S2).
Statistical descriptors, load behavior indicators, voltage stability indices, and power quality metrics were derived from the segmented time-series data. These engineered features included Load Factor (LF), Peak-to-Average Ratio (PAR), Voltage Stability Index (VSI), Voltage Unbalance Index, Power Factor (PF), and Total Harmonic Distortion (THD). The resulting feature set was used as the input to the workflow orchestration and intelligent decision-making modules.
The feature engineering workflow is summarized in Supplementary File 1 (Supplementary Algorithm 1). The procedure consisted of segmenting the preprocessed data, extracting statistical and electrical features, calculating load behavior, voltage stability, and power quality indicators, and applying PCA to generate the final feature vector used for model training and validation.
4. Visualized process canvas design
The Visualized Process Canvas was developed as a graph-based workflow orchestration framework for automating low-voltage (LV) acceptance procedures. The workflow was represented as a directed graph, G = (N, E), where N = {n1, n2, …, nm}denotes the workflow nodes corresponding to individual processing stages (e.g., data input, preprocessing, validation, and decision-making), and E = {eij}represents the directed edges defining the execution sequence between nodes. This graph-based representation enabled the creation of configurable workflows and their automated execution without manual coding.
Each workflow node performed a specific processing task by receiving input data, executing predefined operations, and forwarding the processed output to subsequent nodes. Acceptance criteria, including voltage and load thresholds, were implemented as configurable validation rules that could be updated without modifying the workflow structure. The workflow supported both sequential and parallel execution to improve computational efficiency. Machine learning models were integrated into designated workflow nodes to perform anomaly detection and automated acceptance validation (Figure 2).
Figure 2: Visualized process canvas architecture for LV acceptance automation. Overview of the visual workflow orchestration framework used for LV acceptance automation. The process canvas illustrates the graphical organization of workflow nodes for data processing, feature engineering, rule-based validation, intelligent decision-making, and output generation to support workflow design, execution, and monitoring. Please click here to view a larger version of this figure.
The mathematical formulation describing information flow through the workflow graph is provided in Supplementary File 1 (Section S3). Supplementary File 1 (Supplementary Algorithm 2) describes the workflow orchestration procedure, including workflow traversal, node execution, rule validation, decision generation, and output aggregation.
The Visualized Process Canvas was implemented using a microservice-oriented software architecture to support scalability and modular deployment. The graphical workflow interface was developed using React.js to provide drag-and-drop workflow configuration. Backend orchestration services were implemented using Flask/FastAPI, while Eclipse Mosquitto served as the MQTT message broker for communication between workflow modules. EdgeX Foundry provided edge device management and IoT integration. Machine learning services were implemented with Scikit-learn, TensorFlow, and PyTorch, and containerized with Docker and Kubernetes.
5. Workflow orchestration engine
The workflow orchestration engine coordinated execution of the configured acceptance workflow. A detailed description is provided in Supplementary File 1 (Section S4), and the execution sequence is summarized in Supplementary Figure 1.
The workflow execution process consisted of validating the workflow definition, initializing the execution environment, scheduling workflow nodes according to their dependencies, executing nodes sequentially or in parallel, applying rule-based validation, aggregating intermediate outputs, and generating the final acceptance or rejection decision based on rule-based and machine learning evaluation. Execution results were published through dashboards, reports, application programming interfaces (APIs), and data storage services. The overall workflow orchestration process is illustrated in Figure 3.
Figure 3: Comparison of classification performance metrics across different methods. Comparison of the classification performance of IoT-LV, RBM, Support Vector Machine (SVM), Random Forest (RF), Long Short-Term Memory (LSTM), and the proposed framework using the Open LV Network & Smart Meter dataset. Performance is evaluated using Accuracy, Precision, Recall, and F1-score. Results are presented as mean ± standard deviation (SD) from five independent experimental runs using a 70%/15%/15% training/validation/testing split. Error bars represent one standard deviation. Please click here to view a larger version of this figure.
6. Intelligent decision module
The Intelligent Decision Module was developed to automate low-voltage (LV) acceptance decisions using supervised machine learning models. Classification and regression algorithms were applied to the engineered feature vectors to support operational decision-making. Classification models identified normal and abnormal operating conditions, whereas regression models estimated future energy consumption and operational trends.
The input feature vector, X ∈ ℝd, comprised the statistical, voltage stability, load behavior, and power quality features extracted during the feature engineering stage. Standalone Support Vector Machine (SVM)44, Random Forest (RF)45, and Long Short-Term Memory (LSTM)46 models were implemented as baseline classifiers for comparative evaluation. The mathematical formulation of the learning models is provided in Supplementary File 1 (Section S5).
For classification, the output variable y ∈ {0,1} represented normal (0) and abnormal (1) operating conditions. When abnormal operating conditions or overload events were detected, the workflow generated a fault notification and flagged the acceptance process as invalid; otherwise, the process was classified as accepted.
The workflow orchestration engine integrated machine-learning predictions with rule-based validation, using a priority-based conflict-resolution strategy. Safety-critical operational rules and regulatory acceptance criteria were treated as hard constraints and therefore took precedence over machine-learning predictions when conflicts occurred. Final acceptance decisions were generated using the combined outputs of the rule-based validation and intelligent decision modules.
7. Cloud–edge integration and visualization dashboard
A cloud-edge computing architecture was implemented to support scalable data processing, storage, and visualization for the LV acceptance automation platform. Smart meters, SCADA systems, and IoT devices continuously collect electrical measurements, which are processed at the edge layer via filtering, normalization, feature extraction, and real-time inference to reduce communication overhead and enable low-latency decision-making. The mathematical derivations are provided in Supplementary File 1 (Section S6).
Processed data were transmitted to the cloud layer for long-term storage, advanced analytics, feature engineering, model training, and model management. Interactive dashboards provided real-time system monitoring, alerts, reporting, and operational decision support. The cloud-edge architecture is illustrated in Supplementary Figure 2, and the implementation details are summarized in Table 3.
| Layer | Component | Hardware / Software | Specification / Version | Purpose / Description |
| Edge Layer | Edge Device | Raspberry Pi 4 / NVIDIA Jetson Nano | Quad-core CPU, 4–8 GB RAM | Real-time data acquisition and local processing |
| IoT Gateway | Industrial IoT Gateway | MQTT / Modbus / OPC-UA support | Communication between sensors and edge/cloud | |
| Sensors / Smart Meters | Digital Smart Meters | Voltage, Current, Power Measurement | Data collection from LV network | |
| Edge Software | Python, EdgeX Foundry, MQTT Broker | Python 3.x | Data preprocessing, filtering, local inference | |
| Communication Protocols | Wi-Fi / PLC / RS-485 | Standard protocols | Data transmission to cloud layer | |
| Cloud Layer | Cloud Platform | AWS / Microsoft Azure / Google Cloud | Scalable cloud services | Data storage, analytics, and model training |
| Storage | AWS S3 / Azure Blob / InfluxDB | Time-series database | Storing historical and real-time data | |
| Compute & ML | Python, TensorFlow, PyTorch, Scikit-learn | ML libraries (latest versions) | Model training, evaluation, deployment | |
| Containerization | Docker, Kubernetes | Latest stable versions | Workflow orchestration and scalability | |
| Data Processing | Apache Spark / Pandas | Distributed processing | Large-scale data analytics | |
| Visualization Layer | Dashboard Tools | Grafana / Apache Superset / Plotly Dash | Web-based tools | Real-time monitoring and visualization |
| Web Framework | React.js / Angular / Flask / FastAPI | Modern web stack | UI development and API integration | |
| Notification System | Email / SMS Gateway / Push Notifications | Twilio / Firebase | Alert generation and communication | |
| API Services | REST APIs | JSON-based communication | Integration between modules |
Table 3: System implementation details. Summary of the hardware configuration, software environment, communication protocols, and cloud–edge computing components used for implementing the proposed LV acceptance automation platform.
Resultados
The performance of the proposed Low-Voltage (LV) Acceptance Automation Platform was evaluated using the Open LV Network & Smart Meter Dataset together with a synthetic smart meter dataset generated to represent additional operating conditions and fault scenarios. The synthetic dataset enabled controlled evaluation of workflow orchestration, intelligent decision-making, and fault detection under abnormal operating conditions. The Open LV Network & Smart Meter Dataset comprised household smart meter measurements, feeder-level load profiles, and voltage measurements. Time-series segments corresponding to load consumption and voltage variations were extracted for model development and evaluation.
The dataset was partitioned into training (70%), validation (15%), and testing (15%) subsets. The proposed LV Acceptance Automation Platform achieved high classification accuracy (approximately 96%–98%), effective fault detection capability, and reduced operational decision latency. To ensure statistical robustness and reproducibility, all experiments were repeated five independent times using different random seeds. The resulting performance distributions were subsequently analyzed using paired t-tests, one-way analysis of variance (ANOVA), Tukey's honestly significant difference (HSD) post-hoc analysis, and 95% confidence intervals. The simulation environment used for implementing the proposed framework is summarized in Table 4.
| Parameter | Value | Description |
| Dataset | Open LV Network & Smart Meter Dataset + Synthetic Smart Meter Dataset | Real-world and simulated LV network data |
| Total Samples | 1000 synthetic samples + selected Open LV subsets | Data used for validation and testing |
| Data Split | 70% Training / 15% Validation / 15% Testing | Model development and evaluation |
| Number of Independent Runs | 5 | Statistical reliability analysis |
| Random Seeds | 42, 52, 62, 72, 82 | Reproducible experimental setup |
| Total Statistical Observations | 30 observations (6 models × 5 runs) | Used for t-test, ANOVA, and Tukey HSD |
| Sampling Rate | 30-min / 1-min intervals | Time-series resolution |
| Features | Load, Voltage, Current, Active Power, Reactive Power, THD, Voltage Unbalance, Derived Features | Input features for ML models |
| ML Models | Random Forest, SVM, XGBoost, LSTM | Intelligent decision module |
| Hardware | NVIDIA RTX 3090 GPU, Intel Core i9 CPU, 64 GB RAM | Training and simulation environment |
| Edge Device | Raspberry Pi 4 / Jetson Nano | Edge inference and real-time processing |
| Software | Python 3.10, TensorFlow 2.12+, Scikit-learn 1.3+, PyTorch 2.0+ | Model implementation |
| Communication Protocols | MQTT / HTTP / RS-485 | Data transmission |
| Threshold Values | Voltage ±10%, THD > 5%, Voltage Unbalance > 2%, Feeder Load > 90% capacity | Fault labeling and validation rules |
| Statistical Tests | Paired t-test, One-way ANOVA, Tukey HSD, 95% Confidence Interval | Performance significance evaluation |
Table 4: Simulation environment. Configuration of the experimental environment, including dataset partitioning, machine learning models, hardware specifications, software environment, and validation protocol. Experiments were performed using a 70% training, 15% validation, and 15% testing split and repeated five times using different random seeds.
Classification performance
The proposed framework was evaluated using multiple performance metrics, including accuracy, precision, recall, F1-score, latency, and fault detection rate. Accuracy was used to assess overall classification performance, whereas precision, recall, and F1-score evaluated the effectiveness of fault detection. Latency measured the operational decision response time of the workflow, and the fault detection rate quantified the ability of the framework to identify abnormal operating conditions within the LV distribution network. Scalability was further assessed by progressively increasing the number of input data samples.
The proposed LV Acceptance Automation Platform was compared with five benchmark approaches, namely IoT-LV, Rule-Based Monitoring (RBM), Support Vector Machine (SVM), Random Forest (RF), and Long Short-Term Memory (LSTM). The IoT-LV framework primarily supports data acquisition and basic monitoring using predefined threshold criteria without intelligent decision-making capabilities. Similarly, the RBM approach relies on fixed operational thresholds and therefore lacks adaptive learning. Although SVM and RF effectively classify operational data through supervised learning, they do not incorporate workflow orchestration or integrated acceptance management. LSTM provides improved temporal learning for time-series analysis but does not support automated workflow execution. In contrast, the proposed framework combines workflow orchestration, hybrid rule-based and machine learning decision-making, feature engineering, and cloud-edge computing within a unified automation architecture.
To ensure a fair comparison, all baseline methods were evaluated using the same training, validation, and testing partitions (70%, 15%, and 15%, respectively). Hyperparameter optimization for the machine learning baselines was performed using the validation dataset, and all models employed identical preprocessing and feature-scaling procedures. Unlike the standalone SVM44, RF45, and LSTM46 classifiers, which were trained directly on the original feature space, the proposed framework incorporated advanced feature engineering, workflow validation, and acceptance-rule orchestration prior to classification.
The classification performance of all methods is summarized in Table 5. Among the conventional approaches, IoT-LV achieved the lowest classification accuracy (82.4%) because of its reliance on conventional monitoring without intelligent decision-making. RBM slightly improved performance, reaching an accuracy of 85.7%; however, its fixed validation thresholds limited adaptability. Machine learning approaches demonstrated progressively better performance, with SVM and RF achieving accuracies of 91.6% and 93.4%, respectively, owing to their ability to learn discriminative patterns from the data. LSTM further improved the classification accuracy to 95.2% by effectively capturing temporal dependencies within the smart meter time-series data.
The proposed LV Acceptance Automation Platform achieved the best overall classification performance, with an accuracy of 97.3%, precision of 96.8%, recall of 96.1%, and an F1-score of 96.4%. These improvements are attributable to the integration of advanced preprocessing, feature engineering, workflow orchestration, hybrid rule-based and machine learning decision-making, and cloud-edge collaborative processing rather than the application of the machine learning algorithms alone. Collectively, these components enhanced the separability between normal and abnormal operating conditions while improving the efficiency of automated acceptance validation.
The comparative classification performance of all evaluated methods is illustrated in Figure 3.
| Method | Accuracy (%) | Precision (%) | Recall (%) | F1-Score (%) |
| IoT-LV | 82.4 | 80.2 | 78.5 | 79.3 |
| RBM | 85.7 | 83.9 | 82.1 | 83 |
| SVM | 91.6 | 90.8 | 89.7 | 90.2 |
| RF | 93.4 | 92.6 | 91.8 | 92.2 |
| LSTM | 95.2 | 94.5 | 93.8 | 94.1 |
| Proposed Method | 97.3 | 96.8 | 96.1 | 96.4 |
Table 5: Comparison of classification metrics. Comparison of the classification performance of IoT-LV, RBM, SVM, RF, LSTM, and the proposed framework using Accuracy, Precision, Recall, and F1-score. Results are reported as mean ± standard deviation (SD) obtained from five independent experimental runs.
Statistical significance analysis
To further evaluate the reliability of the proposed LV Acceptance Automation Platform, statistical significance testing was performed to compare its performance with the benchmark methods, including IoT-LV, RBM, SVM, RF, and LSTM. Multiple experimental runs were conducted using different train-validation-test partitions of the Open LV Network & Smart Meter Dataset. The resulting classification accuracies were used to calculate the mean and standard deviation for each method, followed by paired t-tests to determine whether the observed performance differences were statistically significant. The mathematical formulation of the paired t-test is provided in Supplementary File 1 (Section S7).
A significance level of 0.05 was adopted for all statistical analyses. To ensure reproducibility, each experiment was repeated five independent times using different random seeds and data partitions. The performance metrics obtained from each run were subsequently used for statistical evaluation.
The paired t-test results are summarized in Table 6. The proposed framework significantly outperformed all benchmark methods (p < 0.05), demonstrating statistically significant improvements in accuracy, precision, recall, and F1-score. Significant performance gains were observed not only over conventional approaches such as IoT-LV and RBM but also over the more advanced machine learning models, including SVM, RF, and LSTM. These findings indicate that the observed improvements were not attributable to random variation but resulted from the integrated workflow-oriented automation framework.
| Comparison | p-value | Significance |
| Proposed vs IoT-LV | 0.0008 | Significant |
| Proposed vs RBM | 0.0015 | Significant |
| Proposed vs SVM | 0.012 | Significant |
| Proposed vs RF | 0.021 | Significant |
| Proposed vs LSTM | 0.034 | Significant |
Table 6: Pairwise statistical significance analysis (p-values). Pairwise statistical comparison between the proposed framework and the benchmark methods. The table reports p-values obtained from statistical significance testing, where p < 0.05 indicates a statistically significant difference.
Receiver operating characteristic (ROC) analysis
Receiver operating characteristic (ROC) analysis was performed to further evaluate the classification capability of the proposed LV Acceptance Automation Platform in comparison with IoT-LV, RBM, SVM, RF, and LSTM. The area under the ROC curve (AUC) was used to quantify the discrimination capability of each classifier by evaluating the trade-off between the true positive rate (TPR) and false positive rate (FPR).
The IoT-LV framework achieved the lowest AUC (approximately 0.690), reflecting its reliance on conventional monitoring without intelligent decision-making. RBM produced a moderate AUC of approximately 0.738 because of its dependence on fixed threshold-based validation rules. The machine learning classifiers demonstrated progressively improved performance, with SVM and RF achieving AUC values of approximately 0.833 and 0.861, respectively, owing to their ability to learn discriminative patterns from operational data. LSTM further improved classification performance, achieving an AUC of approximately 0.885 by effectively modeling temporal dependencies in smart meter time-series data.
The proposed LV Acceptance Automation Platform achieved the highest AUC (approximately 0.910), demonstrating superior discrimination between normal and abnormal operating conditions. This improvement is attributed to the combined effects of advanced feature engineering, workflow orchestration, hybrid rule-based and machine learning decision-making, and cloud-edge collaborative processing. The comparative ROC curves for all evaluated methods are presented in Figure 4.
Figure 4: Receiver operating characteristic (ROC) curve comparison for LV acceptance Automation. Comparison of the Receiver Operating Characteristic (ROC) curves for IoT-LV, RBM, SVM, RF, LSTM, and the proposed framework. The Area Under the ROC Curve (ROC-AUC) evaluates classification performance across different decision thresholds. TPR denotes True Positive Rate, and FPR denotes False Positive Rate. Please click here to view a larger version of this figure.
ANOVA-based statistical significance analysis
To further validate the experimental results, a one-way analysis of variance (ANOVA) was performed to compare the performance of the proposed framework with IoT-LV, RBM, SVM, RF, and LSTM. Whereas the paired t-test evaluates differences between two methods, ANOVA determines whether statistically significant differences exist among multiple groups simultaneously. The ANOVA analysis was conducted using repeated experiments with different train-validation-test partitions of the Open LV Network & Smart Meter Dataset. The mathematical derivations are presented in Supplementary File 1 (Section S8).
The ANOVA results are presented in Table 7. A large F-statistic (approximately 18.7) with p < 0.001, obtained from 30 observations across five independent experimental runs, demonstrated statistically significant differences among the evaluated methods.
| Source of Variation | SS | df | MS | F-value | p-value |
| Between Groups | 412.6 | 5 | 82.52 | 18.7 | < 0.001 |
| Within Groups | 132.4 | 30 | 4.41 | — | — |
| Total | 545 | 35 | — | — | — |
Table 7: One-Way analysis of variance (ANOVA) results. Results of the one-way ANOVA performed to evaluate statistical differences among the compared methods. The table reports the F-statistic, degrees of freedom (df), and corresponding p-values.
To identify the specific methods responsible for these differences, Tukey's honestly significant difference (HSD) post-hoc analysis was subsequently performed. The results are summarized in Table 8 and showed that the proposed LV Acceptance Automation Platform significantly outperformed IoT-LV, RBM, SVM, and RF while also achieving statistically significant improvements over LSTM. These findings further confirm the effectiveness of integrating workflow orchestration, feature engineering, hybrid rule-based and machine learning decision-making, and cloud-edge computing within a unified acceptance automation framework.
| Comparison | p-value | Significance |
| Proposed vs IoT-LV | < 0.001 | Significant |
| Proposed vs RBM | < 0.001 | Significant |
| Proposed vs SVM | 0.009 | Significant |
| Proposed vs RF | 0.018 | Significant |
| Proposed vs LSTM | 0.041 | Significant |
Table 8: Tukey honestly significant difference (HSD) post-hoc analysis. Pairwise comparisons among the evaluated methods using the Tukey HSD post-hoc test following ANOVA. The table reports mean differences, confidence intervals (CI), adjusted p-values, and statistical significance.
Confidence interval analysis
The reliability and consistency of the proposed framework were further evaluated by calculating 95% confidence intervals (CI) for the classification accuracy obtained from repeated experiments. The computational procedure is described in Supplementary File 1 (Section S8).
The confidence interval analysis is summarized in Table 9. Conventional approaches, including IoT-LV and RBM, exhibited relatively wide confidence intervals, indicating greater variability in classification performance. In contrast, the machine learning-based methods (SVM, RF, and LSTM) demonstrated narrower confidence intervals, reflecting improved stability across repeated experiments.
| Method | Mean Accuracy (%) | Std. Dev (%) | 95% Confidence Interval (%) |
| IoT-LV | 82.4 | 1.8 | [81.0, 83.8] |
| RBM | 85.7 | 1.6 | [84.5, 86.9] |
| SVM | 91.6 | 1.3 | [90.6, 92.6] |
| RF | 93.4 | 1.2 | [92.5, 94.3] |
| LSTM | 95.2 | 1 | [94.5, 95.9] |
| Proposed Method | 97.3 | 0.8 | [96.7, 97.9] |
Table 9: Confidence intervals for classification accuracy. Mean classification accuracy, standard deviation (SD), and 95% confidence interval (CI) for each evaluated method based on five independent experimental runs. The table summarizes the statistical variability and reliability of the classification performance.
The proposed LV Acceptance Automation Platform achieved the highest mean classification accuracy (97.3%) together with the narrowest 95% confidence interval (96.7%–97.9%), indicating excellent consistency and robustness. Furthermore, the absence of overlap between the confidence interval of the proposed framework and those of the benchmark methods provides additional evidence of its statistically superior performance.
Forecasting performance
In addition to classification performance, the forecasting capability of the proposed LV Acceptance Automation Platform was evaluated using the root mean square error (RMSE) and mean absolute error (MAE). These metrics quantify the accuracy of energy consumption prediction by measuring the deviation between the predicted and observed values.
The forecasting performance of the proposed framework and the benchmark methods is summarized in Table 10. The corresponding confusion matrix heatmaps for the evaluated classification methods are presented in Figure 5, illustrating the distributions of true positive, true negative, false positive, and false negative predictions. The proposed framework achieved the lowest prediction errors, with an RMSE of 2.1 and an MAE of 1.6, demonstrating superior forecasting performance compared with IoT-LV, RBM, SVM, RF, and LSTM. Prediction errors progressively decreased from the conventional IoT-LV and RBM approaches to the machine learning-based SVM, RF, and LSTM models, highlighting the advantages of intelligent learning techniques. The proposed framework further reduced prediction errors by integrating advanced feature engineering, workflow orchestration, hybrid rule-based and machine learning decision-making, and cloud-edge collaborative processing, thereby improving the accuracy and reliability of energy consumption forecasting. Operational decision latency for the evaluated methods is compared in Figure 6. The proposed framework achieved the shortest decision latency, demonstrating the effectiveness of workflow orchestration and cloud-edge collaborative processing.
| Method | RMSE | MAE |
| IoT-LV | 8.9 | 7.2 |
| RBM | 7.6 | 6.1 |
| SVM | 5.1 | 4.3 |
| RF | 4.2 | 3.5 |
| LSTM | 3.4 | 2.8 |
| Proposed Method | 2.1 | 1.6 |
Table 10: Comparison of forecasting error metrics (RMSE and MAE). Comparison of the forecasting performance of IoT-LV, RBM, SVM, RF, LSTM, and the proposed framework using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Lower RMSE and MAE values indicate smaller prediction errors.
Figure 5: Comparison of confusion matrix heatmaps. Confusion matrices for IoT-LV, RBM, SVM, RF, LSTM, and the proposed framework showing classification outcomes in terms of True Positive (TP), True Negative (TN), False Positive (FP), and False Negative (FN). The heatmaps illustrate the distribution of correctly and incorrectly classified samples for each method. Please click here to view a larger version of this figure.
Figure 6: Comparison of operational decision latency across different methods. Comparison of the operational decision latency of IoT-LV, RBM, SVM, RF, LSTM, and the proposed framework during LV acceptance automation. Latency is measured in milliseconds (ms) and represents the operational decision time required for workflow execution, rule validation, and acceptance decision generation. Please click here to view a larger version of this figure.
Ablation study
The contribution of each major component of the proposed LV Acceptance Automation Platform was evaluated through an ablation study. The framework was progressively constructed by enabling feature engineering, intelligent machine-learning decision-making, workflow orchestration, and cloud-edge integration. The baseline configuration, in which none of these modules was enabled, achieved an accuracy of 82.6%, an F1-score of 80.4%, an RMSE of 8.7, and an operational latency of 96 ms, demonstrating the limitations of conventional processing without intelligent automation.
The ablation results are summarized in Table 11. Progressive incorporation of feature engineering substantially improved classification performance by enhancing the representation of operational characteristics extracted from the smart meter data. Adding the intelligent decision module further increased classification accuracy and reduced prediction errors through data-driven decision-making. Integration of workflow orchestration improved automation efficiency by coordinating validation, execution, and decision processes, while cloud-edge collaboration reduced operational decision latency through distributed processing. Collectively, these modules produced consistent improvements in accuracy and F1-score while simultaneously reducing RMSE and execution latency.
The complete LV Acceptance Automation Platform achieved the best overall performance, with a classification accuracy of 97.3%, an F1-score of 96.4%, an RMSE of 2.1, and an operational latency of 31 ms. These findings demonstrate that the observed performance improvements result from the integration of workflow orchestration, advanced feature engineering, hybrid rule-based and machine-learning decision-making, and cloud-edge collaboration, rather than from any individual component alone. The fault detection performance of the evaluated methods is presented in Figure 7, where the proposed framework achieved the highest fault detection rate. Figure 8 illustrates the scalability analysis, demonstrating that the proposed framework maintained the lowest processing time as the dataset size increased, confirming its suitability for large-scale LV acceptance automation.
| Configuration | Workflow Orchestration | Feature Engineering | Cloud–Edge Processing | Intelligent ML Decision Module | Accuracy (%) | F1-Score (%) | RMSE | Latency (ms) |
| Baseline System | ✗ | ✗ | ✗ | ✗ | 82.6 | 80.4 | 8.7 | 96 |
| + Feature Engineering | ✗ | ✓ | ✗ | ✗ | 88.9 | 87.5 | 6.2 | 84 |
| + ML Decision Module | ✗ | ✓ | ✗ | ✓ | 92.8 | 91.6 | 4.5 | 63 |
| + Workflow Orchestration | ✓ | ✓ | ✗ | ✓ | 95.1 | 94.2 | 3.3 | 48 |
| + Cloud–Edge Integration | ✓ | ✓ | ✓ | ✓ | 97.3 | 96.4 | 2.1 | 31 |
Table 11: Ablation study of the proposed framework. Experimental evaluation of the contribution of feature engineering, intelligent decision-making, workflow orchestration, and cloud-edge integration to the overall performance of the proposed framework. Performance is reported for each incremental configuration using Accuracy, F1-score, RMSE, and Operational Decision Latency.
Figure 7. Comparison of fault detection rate across different methods. Comparison of the fault detection performance of IoT-LV, RBM, SVM, RF, LSTM, and the proposed framework using the Open LV Network & Smart Meter dataset. Fault Detection Rate (%) represents the proportion of correctly identified fault conditions. Please click here to view a larger version of this figure.
Figure 8. Scalability analysis of different methods. Comparison of the scalability of IoT-LV, RBM, SVM, RF, LSTM, and the proposed framework under increasing dataset sizes. Scalability is evaluated using processing time (ms) as computational workload increases. Please click here to view a larger version of this figure.
DATA AVAILABILITY:
The Open LV Network & Smart Meter dataset used in this study is publicly available through the UK Power Networks Open Data Portal42 at: https://ukpowernetworks.opendatasoft.com/explore/assets/ukpn-smart-meter-consumption-lv-feeder/. All study-generated resources, including the simulated smart meter dataset, example workflow configurations, deployment artifacts, research prototype implementation, source code, and evaluation scripts, are publicly available through the Zenodo repository at: https://zenodo.org/records/21187749.
Supplementary Figure 1: Flowchart of the workflow orchestration engine. The diagram illustrates the sequence of operations performed by the workflow orchestration engine, including data acquisition, preprocessing, workflow execution, machine learning-based validation, decision generation, and result reporting for automated low-voltage acceptance testing.Please click here to download this file.
Supplementary Figure 2: Cloud–edge architecture for low-voltage acceptance automation. The architecture illustrates the interaction between edge devices and cloud services. Electrical measurements collected from smart meters, SCADA systems, and IoT devices are processed at the edge for preprocessing and real-time inference, then transmitted to the cloud for storage, analytics, model management, and visualization via an interactive monitoring dashboard.Please click here to download this file.
Discusión
The experimental evaluation demonstrated that the proposed workflow-oriented Low-Voltage (LV) Acceptance Automation Platform consistently outperformed the benchmark approaches in classification, forecasting, fault detection, operational decision latency, and scalability. These performance improvements resulted from the integration of advanced feature engineering, workflow orchestration, cloud-edge collaboration, and hybrid rule-based and machine learning decision-making rather than from the use of a single machine learning model. The findings are consistent with previous studies demonstrating that workflow-oriented automation and intelligent IoT architectures improve operational efficiency, interoperability, and decision support in industrial and smart grid applications2,3,9,10,18.
The statistical analyses further strengthened the reliability of the experimental findings. Pairwise significance testing, one-way analysis of variance (ANOVA), confidence interval analysis, and Receiver Operating Characteristic (ROC) analysis confirmed that the proposed framework achieved statistically significant improvements over the benchmark methods while maintaining excellent classification capability across different decision thresholds47,48,49. These statistical evaluations provide additional evidence that the observed performance improvements were not attributable to random variation.
The scalability and ablation studies further demonstrated the effectiveness of the proposed architecture under increasing computational workloads and different system configurations. The scalability analysis showed that the framework maintained lower processing times than the benchmark methods as the dataset size increased from 1,000–5,000 samples. Similarly, the ablation study confirmed that each major component contributed to the overall system performance. Feature engineering improved the representation of operational characteristics extracted from smart meter data, the intelligent decision module enhanced fault prediction and automated validation, workflow orchestration optimized task scheduling and execution, and cloud-edge collaboration reduced communication overhead and operational decision latency. These observations agree with previous reports highlighting the advantages of cloud-edge architectures and process orchestration for achieving scalable and low-latency industrial IoT systems9,15,19,20,21.
Because the proposed framework operates within an IoT-enabled LV environment, cybersecurity and data privacy are important considerations for practical deployment. Secure communication between smart meters, edge devices, gateways, and cloud services can be achieved using Transport Layer Security (TLS)-enabled MQTT and HTTPS protocols. Role-based access control (RBAC) provides authentication and authorization for workflow management and system resources, whereas Advanced Encryption Standard (AES)-256 encryption protects operational data during transmission and storage. Audit logging and secure cloud-edge synchronization further support data integrity, confidentiality, and traceability throughout the acceptance workflow. These security mechanisms are consistent with current recommendations for secure Industrial Internet of Things (IIoT) and smart grid communication infrastructures6,7,19,20,21.
The proposed framework also contributes to the development of intelligent power distribution systems by introducing a unified workflow-oriented architecture that integrates cloud-edge collaboration, workflow orchestration, feature engineering, and machine learning-based decision-making within a single automation platform. Unlike conventional IoT-based monitoring approaches that primarily focus on data acquisition and centralized processing1,10,11,12, the proposed framework supports automated validation, intelligent fault detection, and process-driven decision-making. The scalability and ablation studies further demonstrate the advantages of modular intelligent architectures for future smart energy management and Industry 4.0 applications3,9,14,15.
From a practical perspective, the proposed LV Acceptance Automation Platform enables real-time monitoring, automated validation, intelligent fault detection, and acceptance decision-making with minimal human intervention. The achieved operational decision latency of 31 ms and fault detection rate of 98.4% demonstrate the capability of the framework to support real-time industrial applications. Furthermore, the cloud-edge collaborative architecture reduces communication overhead while providing the scalability required to process data acquired from smart meters, distributed energy resources, and IoT-enabled devices. These capabilities align with current trends in smart grid automation and intelligent energy management systems18,19,20,21,27,29,30.
Despite the promising results, several limitations should be acknowledged. The experimental evaluation was conducted using the Open LV Network & Smart Meter Dataset together with synthetic fault data because publicly available LV datasets do not provide comprehensive fault annotations. Consequently, the simulated fault scenarios and rule-based labels may not fully represent the diversity of real-world operating conditions. Furthermore, validation was performed using a single dataset, and practical deployment may be affected by communication constraints, sensor errors, and the computational limitations of edge devices operating under heterogeneous network conditions. Although cybersecurity mechanisms were considered within the proposed architecture, their implementation and evaluation were beyond the scope of this study. Future work will therefore focus on validating the framework using independent real-world datasets, diverse LV network topologies, and field deployment scenarios. Additional research will investigate adaptive intelligent energy management, explainable artificial intelligence, digital twin-assisted monitoring, and enhanced cloud-edge collaboration to further improve the robustness, scalability, and interpretability of intelligent LV acceptance automation systems40,41.
Divulgaciones
Los autores declaran que no tienen intereses financieros ni de otro tipo en conflicto.
Agradecimientos
The authors thank the UK Power Networks Open Data Portal for providing the publicly available Open LV Network & Smart Meter dataset used in this study. This research received no external funding.
Materiales
| Nombre | Empresa | Número de catálogo | Comentarios |
|---|---|---|---|
| Apache Spark | Apache Software Foundation | Versión 3.4+ | Análisis de datos a gran escala |
| Apache Superset | Apache Software Foundation | Versión 3.x | Panel de análisis |
| Docker Engine | Docker Inc. | Versión 24.x | Despliegue de aplicaciones |
| Eclipse Mosquitto MQTT Broker | Eclipse Foundation | Versión 2.x | Intercambio de datos |
| EdgeX Foundry (Lanzamiento Jakarta o posterior) | LF Edge (Linux Foundation) | Versión Jakarta / Geneva | Gestión de dispositivos perimetrales |
| Flask / FastAPI | Pallets Projects / Sebastián Ramírez | Flask 2.3+ / FastAPI 0.100+ | Integración de API de backend |
| Grafana | Grafana Labs | Versión 10.x | Visualización y monitoreo |
| Puerta de enlace Industrial IoT | Advantech / Siemens | ECU-1251 / SIMATIC IOT2040 | Comunicación de sensores a la nube |
| Sensores IoT (Sensores de voltaje/corriente) | Texas Instruments / Analog Devices | INA219 / ACS712 | Monitoreo en tiempo real de red |
| Kit de desarrollo Jetson Nano | NVIDIA Corporation | 945-13450-0000-100 | Inferencia de IA en el borde |
| Kubernetes | Cloud Native Computing Foundation (CNCF) | Versión 1.27+ | Escalabilidad de flujo de trabajo |
| Modelo LSTM (Keras/TensorFlow) | Google (TensorFlow/Keras) | Incluido en TF 2.12+ | Predicción y pronóstico de carga |
| Módulo PLC (Comunicación por línea de alimentación) | STMicroelectronics | ST7580 | Comunicación de red inteligente |
| Plotly Dash | Plotly Inc. | Versión 2.x | Visualización interactiva |
| Python 3.10 | Python Software Foundation | Versión 3.10.x | Procesamiento y análisis de datos |
| PyTorch | Meta AI | Versión 2.0+ | Implementación de aprendizaje profundo |
| Algoritmo de Bosque Aleatorio | Scikit-learn | Incluido en v1.3+ | Clasificación de fallas |
| Raspberry Pi 4 Modelo B (4 GB de RAM) | Raspberry Pi Foundation | RPI4-MODBP-4GB | Procesamiento en el borde |
| React.js / Angular | Meta / Google | React 18.x / Angular 16+ | Desarrollo de interfaz de usuario |
| Módulo transceptor RS-485 | Maxim Integrated (Analog Devices) | MAX485 | Conectividad de dispositivos industriales |
| Scikit-learn | Desarrolladores de Scikit-learn | Versión 1.3+ | Modelos de aprendizaje automático |
| Máquina de vectores de soporte (SVM) | Scikit-learn | Incluido en v1.3+ | Clasificación y validación |
| TensorFlow | Google LLC | Versión 2.12+ | Entrenamiento de modelos |
| Twilio / Firebase Cloud Messaging | Twilio Inc. / Google | No aplicable | Alertas y notificaciones |
| Módulo Wi-Fi | Espressif Systems | ESP8266 / ESP32 | Transmisión de datos |
| XGBoost | DMLC (Comunidad de aprendizaje automático distribuido) | Versión 1.7+ | Soporte inteligente para decisiones |
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