Die vorgeschlagene Automatisierungsplattform für die Niederspannungsabnahme integriert das Internet der Dinge (IoT), visuelle Workflow-Orchestrierung, Edge- und Cloud-Computing, Feature-Engineering sowie maschinelles Lernen, um die Abnahmeprüfung von Niederspannungsanlagen (LV) zu automatisieren. Die Plattform ermöglicht eine intelligente Fehlererkennung, Echtzeit-Entscheidungsfindung, automatisierte Abnahme, hohe Genauigkeit, geringe Betriebslatenz und skalierbare Bereitstellung.
Forschungsartikel
Eine prozessgesteuerte Automatisierungsplattform für die Niederspannungsabnahme mit visueller Workflow-Orchestrierung und Machine-Learning-Validierung
In diesem Artikel
Zusammenfassung
Zusammenfassung
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%.
Einleitung
Industrie 4.0 (I4.0)1 wird durch mehrere neu aufkommende Technologien ermöglicht, wobei das industrielle Internet der Dinge (IIoT) eine der wichtigsten darstellt. IIoT bezeichnet den Einsatz von Internet-der-Dinge-(IoT-)Technologien in industriellen Umgebungen, in denen vernetzte Geräte und Maschinen hauptsächlich über Maschine-zu-Maschine-Interaktionen kommunizieren. Da Ausfälle in industriellen Systemen erhebliche Betriebsverluste verursachen können, erfordern IIoT-Anwendungen eine äußerst zuverlässige Kommunikation und erzeugen dabei deutlich größere Datenmengen als herkömmliche IoT-Systeme2. Zudem betrachten einige Studien das IIoT als ein eng mit Industrie 4.0 verwandtes Konzept3. IoT-Technologien verändern traditionelle hierarchische Architekturen, indem sie flexiblere Systeme mit nahtlosem Datenaustausch ermöglichen4. Zahlreiche aktuelle Studien haben bestehende IIoT-Architekturen überprüft und neue Rahmenkonzepte vorgeschlagen5,6,7,8,9. Trotz dieser Entwicklungen bestehen zwei wesentliche Einschränkungen weiterhin. Erstens werden häufig neue Rahmenwerke vorgeschlagen, um die Mängel früherer Architekturen zu beheben, was zu einer wachsenden Zahl alternativer Modelle führt, die die architektonische Vielfalt und die Komplexität der Implementierung erhöhen. Zweitens schränkt das hohe Maß an Abstraktion, das vielen vorgeschlagenen Architekturen eigen ist, deren praktische Anwendung in industriellen Umgebungen ein10.
In der Literatur wurden bereits mehrere IIoT-Architekturen beschrieben. Eine auf dem Open Platform Communications (OPC)-Standard basierende IIoT-Architektur wurde zusammen mit einer Python-Middleware zur Verwaltung der OPC-Kommunikation vorgestellt11. In wurde eine siebenstufige IIoT-Architektur vorgeschlagen; jedoch wurden keine Implementierungsdetails und Validierungsexperimente bereitgestellt12. Ein Framework zur Integration von 5G- und IIoT-Technologien wurde zusammen mit einem repräsentativen Anwendungsbeispiel beschrieben, obwohl die experimentelle Validierung begrenzt war13. Ein Emotionsdesign-Framework für Industrie 4.0 wurde eingeführt, und seine möglichen Auswirkungen wurden ohne experimentelle Überprüfung diskutiert14. Eine mehrschichtige IIoT-Plattform wurde in einem realen Testfeld evaluiert, wobei der Schwerpunkt auf telekommunikativen Aspekten lag15. Ebenso wurde in ein modulares IoT-Framework zur Überwachung der Innenraumluftqualität vorgestellt, und seine Leistung wurde anhand von Daten bewertet, die aus 84 Haushalten mit Kindern gesammelt wurden16.
Das Internet der Dinge (IoT) hat sich zu einer sich rasant entwickelnden Technologie mit erheblichem Anwendungspotenzial im Elektrizitätssektor entwickelt17,18. Die Integration von IoT-Technologien in Netzknoten ermöglicht eine intelligentere und effizientere Stromerzeugung, -übertragung, -verteilung, -betrieb und -nutzung durch autonome Entscheidungsfindung, die durch kontinuierliche Echtzeitüberwachung des Stromflusses im intelligenten Netz (Smart Grid) unterstützt wird. Folglich verbessern diese intelligenten Knoten die gesamte Betriebsleistung des elektrischen Netzes19. Die Kommunikationstechnologie stellt eine grundlegende Komponente bei der Implementierung intelligenter Netze dar, da zwei Hauptkategorien von Informationen zuverlässig übertragen werden müssen: Sensormessungen und Steuersignale. Neben geringer Kommunikationslatenz erfordern Anwendungen in intelligenten Netzen eine angemessene Dienstgüte (QoS) und ausreichende Datensicherheit, um einen zuverlässigen Betrieb sicherzustellen20.
Die Auswahl einer geeigneten Kommunikationstechnologie für Smart-Grid-Anwendungen erfordert die Berücksichtigung von Kommunikationsintervallen, Datenübertragungsraten und Kommunikationskosten21. IoT-Kommunikationsnetzwerke bestehen typischerweise aus Software-, Sicherheits-, Dienstleistungs-, System- und Konnektivitätsschichten. Innerhalb der Konnektivitätsschicht werden Kommunikationsprotokolle gewöhnlich in drei Kategorien eingeteilt. Zu den Langstreckenprotokollen gehören Long Range (LoRa) und Narrowband-IoT (NB-IoT); zu den Mittelstreckenprotokollen zählen Wi-Fi, 4G/LTE und 5G; und zu den Kurzstreckenprotokollen gehören Bluetooth und ZigBee. Da die Strommärkte zunehmend dereguliert wurden, ist die Lastprognose wichtiger geworden, um die Auswirkungen von Wetterbedingungen, Geräteausfällen, regulatorischen Änderungen und anderen betrieblichen Faktoren auf die Kosten der Stromversorgung zu bewerten. Daher ist eine genaue Prognose des häuslichen Energieverbrauchs für den Betrieb und die Überwachung von Smart Grids, Mikrogrids und intelligenten Gebäuden unerlässlich22.
Ein zuverlässiger Betrieb von Niederspannungsnetzen (NS-Netzen) hängt von der Aufrechterhaltung einer hohen Spannungsqualität23 ab. Die Qualität der elektrischen Energie wird durch die Wirksamkeit der Spannungsregelung, die Stromsymmetrie und die Minderung technischer Störungen unter unsymmetrischen Betriebsbedingungen bestimmt24. Unter diesen Faktoren ist die Phasenausgleichung besonders wichtig, da sie die gleichmäßige Verteilung von Spannungen und Strömen über die Phasen bestimmt25. Eine schlechte Phasenausgleichung kann die Erwärmung der Leiter, Spannungsabweichungen und Neutralströme erhöhen, wodurch die Systemeffizienz und Betriebssicherheit verringert werden26. Daher sollte die Phasenausgleichung bereits im Planungsprozess berücksichtigt werden, um die Systemleistung, Betriebsstabilität und Langzeitverlässlichkeit zu verbessern. Dennoch wird dieser Aspekt selten in herkömmliche Minigrid-Planungsmethoden integriert.
Neuere Fortschritte bei Workflow-Orchestrierungssystemen haben die Vorteile der ereignisgesteuerten Automatisierung und der visuellen Prozessverwaltung in industriellen IoT-Umgebungen gezeigt. Dennoch ist ihre Anwendung auf die Validierung von Verteilnetzen und die Abnahmeprüfung von Niederspannungsanlagen bisher begrenzt. Die meisten bestehenden Niederspannungs-Automatisierungssysteme konzentrieren sich hauptsächlich auf Überwachung und leittechnische Steuerung, während vergleichsweise wenige Studien workflowbasiertes Abnahmemanagement oder automatisierte Konformitätsprüfungen untersuchen. Ebenso haben maschinelle Lernverfahren beträchtliche Aufmerksamkeit für die Anomalieerkennung bei intelligenten Zählern und die Beurteilung der Netzqualität erhalten, doch diese Ansätze werden im Allgemeinen als unabhängige analytische Modelle entwickelt, anstatt als Bestandteile eines integrierten Frameworks für die Workflow-Orchestrierung.
In den letzten Jahren wurden verschiedene intelligente Zählerlösungen für das Management intelligenter Gebäude und die Hausautomatisierung vorgeschlagen27. In Referenz27 wurde ein Prototyp eines intelligenten Zählers vorgestellt, um die Integration von bildungsbezogenen Konzepten auf universitärem Niveau in Technologien zur intelligenten Zählerablesung zu demonstrieren. Der entwickelte intelligente Zähler zeigte ein erhebliches Potenzial für Energiemanagement und -kontrolle, verringerte den Energieverbrauch und profitierte sowohl von Seiten der Versorger als auch der Verbraucher durch bidirektionale IoT-Kommunikation. Anschließend wurde ein mikrocontrollerbasiertes System mit intelligenten Steckdosen zur Unterstützung einer intelligenten Energieüberwachung eingeführt28.
Das vorgeschlagene Smart-Plug-System zeigt den Stromverbrauch über eine auf Android basierende grafische Benutzeroberfläche in Echtzeit an. Zusätzlich kann das Gerät das Vorhandensein angeschlossener elektrischer Geräte erkennen, eine Fähigkeit, die als invasive Lastmessung bezeichnet wird28. Für die Implementierung wurde eine kostengünstige kommerzielle Smart-Plug verwendet. Zukünftige Erweiterungen beinhalten die Überwachung des Effektivwerts des Stroms (RMS), der Wirkleistung, der Blindleistung sowie des Phasenwinkels zwischen Spannung und Strom. Wie in unseren früheren Studien beschrieben, verwendet ein teilweise netzgekoppelter photovoltaischer Mikro-Netzverbund die Kommunikationsprotokolle ZigBee und LoRa, um Informationen mit intelligenten Zählergeräten auszutauschen, wodurch die Machbarkeit einer IoT-fähigen Kommunikation für dezentrale Energiesysteme gezeigt wird29,30.
Um die Einschränkungen der herkömmlichen Planung von Verteilnetzen zu überwinden, haben sich die ingenieurwissenschaftliche Praxis und die Forschung in mehreren komplementären Richtungen weiterentwickelt. Ein Forschungsansatz konzentrierte sich auf die strategische Planung mithilfe von Geoinformationssystemen (GIS) zur Standortauswahl und vorläufigen Ressourcenbewertung31,32. Eine weitere Richtung zielte darauf ab, einzelne Komponenten und Teilsysteme zu verbessern, einschließlich der Leiterauswahl, der optimalen Dimensionierung von Erzeugungseinheiten und Energiespeichern sowie des vergleichenden Leistungsvergleichs von Wechselstrom- (AC-) und Gleichstrom- (DC-) Verteilnetzen33,34,35,36. Ein dritter Forschungsansatz wandte Optimierungsalgorithmen auf die Planung von Verteilnetzen, die Leiterführung und die Gestaltung der Netztopologie an. Zu den jüngsten Entwicklungen zählen Frameworks wie die uGrid-Plattform der Forscher, die die Geräteplatzierung und Netzkonfiguration integriert, jedoch keine standardmäßige Lastflussanalyse zur Verifizierung und Optimierung bietet. Andere Ansätze basieren auf manueller Parametereinstellung zur Generierung der Topologie oder konzentrieren sich auf spezialisierte Anwendungen wie niederleistungsfähige DC-Mikronetze37,38,39. Obwohl diese Ansätze einen beträchtlichen Fortschritt darstellen, ist weiterer Forschungsbedarf erforderlich, um eine integrierte Methodik für das durchgängige Design von Niederspannungsverteilnetzen zu etablieren. Zudem sind umfassende techno-ökonomische Bewertungen von einphasigen, dreiphasigen und hybriden Niederspannungssystemen bisher begrenzt.
Die jüngsten Fortschritte in der künstlichen Intelligenz (AI) und im maschinellen Lernen haben die Fähigkeiten intelligenter Stromnetze weiter erweitert. Eine umfassende Übersicht fasste Anwendungen von KI und maschinellem Lernen in intelligenten Stromnetzen zusammen, einschließlich neuartiger Technologien wie des Föderierten Lernens, Generativer KI, Großer Sprachmodelle, des KI-der-Dinge (AIoT) und Digitaler Zwillinge40. In der Übersicht wurden Anwendungen im Lastprognosebereich, in der vorausschauenden Wartung, der Anomalieerkennung, dem Lastmanagement auf der Nachfrageseite sowie der Integration von Elektrofahrzeugen hervorgehoben und gleichzeitig auf anhaltende Herausforderungen im Zusammenhang mit Interoperabilität, Datenschutz, Skalierbarkeit, Robustheit und ethischen Aspekten hingewiesen. Eine weitere aktuelle Übersicht bewertete KI-Methoden für das dezentrale Energiemanagement in IoT-Edge-Cloud-Infrastrukturen, wobei der Fokus auf maschinellem Lernen, verstärkendem Lernen und Multi-Agenten-Systemen für die dezentrale Energiekontrolle lag41.
Zusammenfassend haben bisherige Studien die IoT-gestützte Überwachung, intelligente Zähler, Cloud-Edge-Computing, künstliche Intelligenz und intelligentes Energiemanagement für Niederspannungsverteilnetze (LV) vorangebracht. Allerdings wurden diese Technologien weitgehend unabhängig voneinander entwickelt. Die Orchestrierung von Arbeitsabläufen, das automatisierte Annahmemanagement, die prozessgesteuerte Validierung und die integrierte Entscheidungsunterstützung sind weiterhin begrenzt, was die Notwendigkeit eines einheitlichen, arbeitsablauforientierten Automatisierungsrahmens unterstreicht.
Dementsprechend stellt diese Studie eine Automatisierungsplattform für die Niederspannungsabnahme basierend auf einer visualisierten Prozess-Canvas vor. Der vorgeschlagene Rahmen integriert datenbasierte Erfassung über das Internet der Dinge (IoT), Daten-Vorverarbeitung, Merkmalsengineering, Workflow-Orchestrierung, validierungsbasierte maschinelles Lernen, Cloud-Edge-Computing und Echtzeit-Visualisierung in einer einheitlichen Architektur, um automatisierte und zuverlässige Abnahmetests von Niederspannungsnetzen unter Verwendung realer Smart-Meter-Daten zu unterstützen. Abbildung 1 gibt einen Überblick über die workfloworientierte Architektur der vorgeschlagenen Plattform. Der Workflow umfasst Datenerfassung, Vorverarbeitung, Merkmalsengineering, Workflow-Orchestrierung, intelligente Entscheidungsfindung und Cloud-Edge-Integration und ermöglicht so die automatisierte Validierung von Niederspannungsverteilnetzen durch einen prozessorientierten Rahmen. Die Plattform zeigte im Vergleich zu herkömmlichen Ansätzen eine hohe Validierungsgenauigkeit sowie eine reduzierte Latenz bei betrieblichen Entscheidungen und eine verkürzte Workflow-Ausführungszeit.
Abbildung 1. Arbeitsablauforientierte Architektur der vorgeschlagenen Automatisierungsplattform für die Niederspannungsabnahme. Hochstufige Architektur der vorgeschlagenen prozessgesteuerten Automatisierungsplattform für die Niederspannungsabnahme (LV). Das Framework veranschaulicht die sequenzielle Integration von datengestützter Erfassung mittels Internet der Dinge (IoT), Datenpräprozessierung, Merkmalsengineering, Workflow-Orchestrierung, intelligenter Entscheidungsfindung, Cloud-Edge-Kollaboration und automatisierter Abnahme zur Validierung von Niederspannungsverteilnetzen. IoT = Internet der Dinge; LV = Niederspannung; ML = maschinelles Lernen. Bitte klicken Sie hier, um eine größere Version dieser Abbildung anzusehen.
Protokoll
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.
Ergebnisse
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.
Diskussion
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.
Offenlegungen
Die Autoren erklären, dass sie keine konkurrierenden finanziellen oder nichtfinanziellen Interessen haben.
Danksagungen
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.
Materialien
| Name | Unternehmen | Katalognummer | Kommentare |
|---|---|---|---|
| Apache Spark | Apache Software Foundation | Version 3.4+ | Analysen großer Datenmengen |
| Apache Superset | Apache Software Foundation | Version 3.x | Analyse-Dashboard |
| Docker Engine | Docker Inc. | Version 24.x | Anwendungsbereitstellung |
| Eclipse Mosquitto MQTT Broker | Eclipse Foundation | Version 2.x | Datenaustausch |
| EdgeX Foundry (Jakarta Release oder später) | LF Edge (Linux Foundation) | Version Jakarta / Geneva | Verwaltung von Edge-Geräten |
| Flask / FastAPI | Pallets Projects / Sebastián Ramírez | Flask 2.3+ / FastAPI 0.100+ | Backend-API-Integration |
| Grafana | Grafana Labs | Version 10.x | Visualisierung und Überwachung |
| Industrieller IoT-Gateway | Advantech / Siemens | ECU-1251 / SIMATIC IOT2040 | Sensor-zu-Cloud-Kommunikation |
| IoT-Sensoren (Spannungs-/Stromsensoren) | Texas Instruments / Analog Devices | INA219 / ACS712 | Echtzeit-Netzwerküberwachung |
| Jetson Nano Developer Kit | NVIDIA Corporation | 945-13450-0000-100 | Edge-AI-Inferenz |
| Kubernetes | Cloud Native Computing Foundation (CNCF) | Version 1.27+ | Skalierbarkeit von Workflows |
| LSTM-Modell (Keras/TensorFlow) | Google (TensorFlow/Keras) | Enthalten in TF 2.12+ | Lastprognose und -vorhersage |
| PLC-Modul (Power Line Communication) | STMicroelectronics | ST7580 | Kommunikation im intelligenten Stromnetz |
| Plotly Dash | Plotly Inc. | Version 2.x | Interaktive Visualisierung |
| Python 3.10 | Python Software Foundation | Version 3.10.x | Datenverarbeitung und -analyse |
| PyTorch | Meta AI | Version 2.0+ | Implementierung von Deep Learning |
| Zufallswald-Algorithmus | Scikit-learn | Enthalten in v1.3+ | Fehlerklassifizierung |
| Raspberry Pi 4 Modell B (4 GB RAM) | Raspberry Pi Foundation | RPI4-MODBP-4GB | Edge-Verarbeitung |
| React.js / Angular | Meta / Google | React 18.x / Angular 16+ | Benutzeroberflächenentwicklung |
| RS-485-Transceiver-Modul | Maxim Integrated (Analog Devices) | MAX485 | Verbindbarkeit industrieller Geräte |
| Scikit-learn | Scikit-learn-Entwickler | Version 1.3+ | Machine-Learning-Modelle |
| Support-Vektor-Maschine (SVM) | Scikit-learn | Enthalten in v1.3+ | Klassifizierung und Validierung |
| TensorFlow | Google LLC | Version 2.12+ | Modelltraining |
| Twilio / Firebase Cloud Messaging | Twilio Inc. / Google | Nicht zutreffend | Warnungen und Benachrichtigungen |
| WLAN-Modul | Espressif Systems | ESP8266 / ESP32 | Datenübertragung |
| XGBoost | DMLC (Distributed ML Community) | Version 1.7+ | Intelligente Entscheidungsunterstützung |
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