Het voorgestelde Low-Voltage Acceptance Automation Platform integreert het Internet of Things (IoT), visuele workflow-orkestratie, edge- en cloudcomputing, feature engineering en machine learning om de acceptatietests van laagspanningssystemen (LV) te automatiseren. Het platform maakt intelligente foutdetectie, real-time besluitvorming, geautomatiseerde acceptatie, hoge nauwkeurigheid, lage operationele latentie en schaalbare implementatie mogelijk.
Onderzoeksartikel
Een procesgestuurd automatiseringsplatform voor laagspanningsacceptatie met visuele workflow-orkestratie en validatie via machine learning
In dit artikel
Samenvatting
Samenvatting
De snelle uitbreiding van laagspanningsnetten (LV-netten) en hun integratie met gedistribueerde energiebronnen vereisen intelligente en geautomatiseerde beheeroplossingen. Cloud-edge collaboratieve Internet of Things (IoT)-platforms ondersteunen real-time monitoring, besturing en gegevensverwerving. Bestaande platforms missen echter over het algemeen workflowautomatisering, visuele procesorchestratie, gebruikersgestuurde beslissingsondersteuning en uitgebreide validatie. Bijgevolg bieden ze geen procesgestuurde oplossingen voor de geautomatiseerde acceptatietests van LV-netten. Deze studie presenteert het ontwerp en de evaluatie van een Low-Voltage Acceptance Automation Platform op basis van een gevisualiseerd procescanvas. Het voorgestelde platform hanteert een procesgestuurde architectuur waarin acceptatieworkflows visueel worden gecreëerd, beheerd en uitgevoerd. Het visuele procescanvas transformeert conventionele statische monitoring in dynamische workflowautomatisering door real-time uitvoering, validatie en besluitvorming van de workflow mogelijk te maken. Het framework maakt gebruik van de Open LV Network & Smart Meter-dataset om realistische modellering van het elektrische lastgedrag te ondersteunen. De workflow omvat IoT-gebaseerde gegevensverwerving, gegevensvoorbewerking, feature engineering, workfloworchestratie met behulp van het visuele procescanvas, validatie op basis van machine learning en real-time visualisatie via een dashboard. Het voorgestelde framework behaalde een foutdetectieratio van 98,4%, een receiver operating characteristic area under the curve (ROC-AUC) van 0,968 en een operationele beslissingslatentie van 31 ms, waarmee het ongeveer 30%–40% beter presteerde dan traditionele cloud-centrische en IoT-gebaseerde baseline-benaderingen.
Inleiding
Industrie 4.0 (I4.0)1 wordt mogelijk gemaakt door verschillende opkomende technologieën, waaronder het Industrial Internet of Things (IIoT) een van de belangrijkste is. IIoT verwijst naar de toepassing van Internet of Things (IoT)-technologieën in industriële omgevingen, waar onderling verbonden apparaten en machines voornamelijk communiceren via machine-to-machine-interacties. Omdat storingen in industriële systemen kunnen leiden tot aanzienlijke operationele verliezen, vereisen IIoT-applicaties een zeer betrouwbare communicatie, terwijl ze aanzienlijk grotere volumes aan gegevens genereren dan conventionele IoT-systemen2. Daarnaast beschouwen sommige studies IIoT als een concept dat nauw verwant is aan Industrie 4.03. IoT-technologieën transformeren traditionele hiërarchische architecturen door flexibelere systemen met naadloze gegevensuitwisseling mogelijk te maken4. Talrijke recente studies hebben bestaande IIoT-architecturen beoordeeld en nieuwe raamwerkontwerpen voorgesteld5,6,7,8,9. Ondanks deze ontwikkelingen blijven er twee belangrijke beperkingen bestaan. Ten eerste worden er frequent nieuwe raamwerken voorgesteld om de tekortkomingen van eerdere architecturen te overwinnen, wat resulteert in een groeiend aantal alternatieve modellen die de architecturale diversiteit en de implementatiecomplexiteit verhogen. Ten tweede beperkt het hoge abstractieniveau dat gepaard gaat met veel voorgestelde architecturen de praktische adoptie ervan in industriële omgevingen10.
Verschillende IIoT-architecturen zijn in de literatuur beschreven. Een IIoT-architectuur gebaseerd op de Open Platform Communications (OPC)-standaard werd gepresenteerd in samen met een Python-middleware voor het beheren van OPC-communicatie11. Een zevenlaagse IIoT-architectuur werd voorgesteld in; implementatiedetails en validatie-experimenten werden echter niet verstrekt12. Een raamwerk dat 5G- en IIoT-technologieën integreert, werd beschreven in samen met een representatieve use case, hoewel de experimentele validatie beperkt was13. Een framework voor emotioneel ontwerp voor Industrie 4.0 werd geïntroduceerd, en de potentiële implicaties hiervan werden besproken zonder experimentele verificatie14. Een meerlaags IIoT-platform werd geëvalueerd in een real-world testbed in, met de nadruk op telecommunicatie-overwegingen15. Op soortgelijke wijze werd een modulair IoT-raamwerk voor het monitoren van de binnenluchtkwaliteit gepresenteerd in en werd de prestatie ervan geëvalueerd aan de hand van gegevens verzameld uit 84 huishoudens met kinderen16.
Het Internet of Things (IoT) is een snel evoluerende technologie geworden met aanzienlijk potentieel voor toepassing in de elektrosector17,18. De integratie van IoT-technologieën in knooppunten van energiesystemen maakt intelligentere en efficiëntere energieopwekking, transmissie, distributie, exploitatie en benutting mogelijk via autonome besluitvorming, ondersteund door continue real-time monitoring van de elektriciteitsstroom binnen het smart grid. Bijgevolg verbeteren deze intelligente knooppunten de algehele operationele prestaties van het elektrische netwerk19. Communicatietechnologie vormt een fundamenteel onderdeel van de implementatie van smart grids, omdat twee hoofdcategorieën informatie betrouwbaar moeten worden verzonden: sensormetingen en controlesignalen. Naast een lage communicatielatentie vereisen smart grid-toepassingen een passende kwaliteit van dienstverlening (QoS) en adequate gegevensbeveiliging om een betrouwbare werking te garanderen20.
Voor het selecteren van een geschikte communicatietechnologie voor smart grid-toepassingen moet rekening worden gehouden met communicatie-intervallen, datatransmissiesnelheden en communicatiekosten21. IoT-communicatienetwerken bestaan doorgaans uit software-, beveiligings-, service-, systeem- en connectiviteitslagen. Binnen de connectiviteitslaag worden communicatieprotocollen over het algemeen ingedeeld in drie categorieën. Langeafstandsprotocollen omvatten Long Range (LoRa) en Narrowband-IoT (NB-IoT); middellange afstandsprotocollen omvatten Wi-Fi, 4G/LTE en 5G; en korte afstandsprotocollen omvatten Bluetooth en ZigBee. Naarmate elektriciteitsmarkten steeds meer gedereguleerd zijn geworden, is belastingvoorspelling belangrijker geworden voor het evalueren van de effecten van weersomstandigheden, apparatuurstoringen, regelgevende wijzigingen en andere operationele factoren op de kosten van elektriciteitsdiensten. Bijgevolg is een nauwkeurige voorspelling van de residentiële belasting essentieel geworden voor de exploitatie en supervisie van smart grids, microgrids en slimme gebouwen22.
De betrouwbare werking van laagspanningsnetten (LV) is afhankelijk van het behoud van een hoge vermogenskwaliteit23. De vermogenskwaliteit wordt bepaald door de effectiviteit van de spanningsregeling, de stroombalans en de beperking van technische storingen onder ongebalanceerde bedrijfsomstandigheden24. Van deze factoren is fasebalancering bijzonder belangrijk, omdat deze de uniforme verdeling van spanningen en stromen over de fasen bepaalt25. Een slechte fasebalancering kan leiden tot een toename van geleideropwarming, spanningsafwijkingen en nulstroom, waardoor de systeemefficiëntie en de operationele betrouwbaarheid afnemen26. Bijgevolg moet fasebalancering tijdens het ontwerpproces worden overwogen om de systemprestaties, de operationele stabiliteit en de betrouwbaarheid op lange termijn te verbeteren. Desondanks wordt dit aspect zelden opgenomen in conventionele ontwerpmethodologieën voor mini-grids.
Recente vooruitgangen in workflow-orchestratiesystemen hebben de voordelen aangetoond van event-driven automatisering en visueel procesbeheer binnen industriële IoT-omgevingen. Desondanks blijft hun toepassing voor de validatie van distributienetwerken en acceptatietests voor laagspanningsinstallaties beperkt. De meeste bestaande laagspanningsautomatiseringssystemen richten zich primair op monitoring en supervisie, terwijl relatief weinig studies workflow-gestuurd acceptatiemanagement of geautomatiseerde complianceverificatie onderzoeken. In dezelfde zin hebben machinelearningtechnieken aanzienlijke aandacht gekregen voor de detectie van anomalieën in slimme meters en de beoordeling van de elektriciteitskwaliteit, maar deze benaderingen worden over het algemeen ontwikkeld als onafhankelijke analytische modellen in plaats van als componenten van een geïntegreerd workflow-orchestratieraamwerk.
In recente jaren zijn diverse slimme energiemeteroplossingen voorgesteld voor slim gebouwbeheer en huisautomatisering27. In referentie27 werd een prototype van een slimme meter gepresenteerd om de integratie van educatieve concepten op universitair niveau in slimme meteringtechnologieën te demonstreren. De ontwikkelde slimme meter vertoonde een aanzienlijk potentieel voor energiemanagement en -beheersing, waardoor het energieverbruik werd verminderd en zowel nutsbedrijven als consumenten profiteerden van bidirectionele IoT-communicatie. Vervolgens werd een op een microcontroller gebaseerd slim stekkersysteem geïntroduceerd om intelligente energiemonitoring te ondersteunen28.
Het voorgestelde smart plug-systeem toont het stroomverbruik in realtime via een grafische gebruikersinterface op basis van Android. Daarnaast kan het apparaat de aanwezigheid van aangesloten elektrische apparaten detecteren, een functionaliteit die invasieve belastingsmeting wordt genoemd28. Voor de implementatie werd een goedkope commerciële smart plug gebruikt. Toekomstige verbeteringen omvatten het monitoren van de effectieve stroom (RMS), het actieve vermogen, het reactieve vermogen en de fasehoek tussen spanning en stroom. Zoals beschreven in onze eerdere studies maakt een gedeeltelijk netgekoppeld fotovoltaïsch microgrid gebruik van ZigBee- en LoRa-communicatieprotocollen om informatie uit te wisselen met slimme meters, wat de haalbaarheid aantoont van IoT-ondersteunde communicatie voor gedistribueerde energiesystemen29,30.
Om de beperkingen van conventionele planning van distributiesystemen aan te pakken, zijn de technische praktijk en het wetenschappelijk onderzoek in verschillende complementaire richtingen gevorderd. Eén onderzoeksrichting heeft zich gericht op planning op hoog niveau met behulp van geografische informatiesystemen (GIS) voor locatiekeuze en voorlopige hulpbronnenbeoordeling31,32. Een andere richting heeft zich geconcentreerd op het verbeteren van individuele componenten en subsystemen, waaronder de selectie van geleiders, optimale dimensionering van opwekking en energieopslag, en vergelijkende evaluaties van wisselstroom- (AC) en gelijkstroom- (DC) distributiesystemen33,34,35,36. Een derde onderzoeksrichting heeft optimalisatiealgoritmen toegepast op de planning van distributienetwerken, de routering van voedingslijnen en het ontwerp van de nettopologie. Recente ontwikkelingen omvatten frameworks zoals het uGrid-platform van de onderzoekers, dat apparaatplaatsing en netwerkconfiguratie integreert, maar een standaard vermogensstroomanalyse voor verificatie en optimalisatie mist. Andere benaderingen vertrouwen op handmatige parameterinstelling voor topologiegeneratie of richten zich op gespecialiseerde toepassingen zoals laagvermogen DC-microgrids37,38,39. Hoewel deze benaderingen een aanzienlijke vooruitgang vertegenwoordigen, is verder werk nodig om een geïntegreerde methodologie vast te stellen voor het end-to-end ontwerp van laagspanningsdistributiesystemen. Bovendien blijven uitgebreide techno-economische evaluaties van éénfasige, driefasige en hybride laagspanningssystemen beperkt.
Recente vorderingen in kunstmatige intelligentie (AI) en machine learning hebben de mogelijkheden van intelligente energiesystemen verder uitgebreid. Een uitgebreid overzicht presenteerde samengevatte AI- en machine learning-toepassingen in slimme netten, inclusief opkomende technologieën zoals Federated Learning, Generatieve AI, Large Language Models, Artificial Intelligence of Things (AIoT) en Digital Twins40. Het overzicht belichtte toepassingen in lastvoorspelling, voorspellend onderhoud, anomaliedetectie, vraaggestuurd beheer en de integratie van elektrische voertuigen, terwijl de nadruk lag op aanhoudende uitdagingen met betrekking tot interoperabiliteit, privacy, schaalbaarheid, robuustheid en ethische overwegingen. Een andere recente review evalueerde AI-methoden voor gedistribueerd energiemanagement in IoT-edge-cloud-infrastructuren, met de focus op machine learning, reinforcement learning en multi-agent-systemen voor decentrale energiecontrole41.
Collectief hebben eerdere studies IoT-gestuurde monitoring, slimme metering, cloud-edge computing, kunstmatige intelligentie en intelligent energiemanagement voor laagspanningsdistributiesystemen (LV) verder ontwikkeld. Echter, deze technologieën zijn grotendeels onafhankelijk van elkaar ontwikkeld. Workflow-orchestratie, geautomatiseerd acceptatiemanagement, procesgestuurde validatie en geïntegreerde beslissingsondersteuning blijven beperkt, wat de noodzaak benadrukt voor een uniform, workflow-georiënteerd automatiseringsraamwerk.
Hiertoe presenteert deze studie een automatiseringsplatform voor de acceptatie van laagspanningsnetten op basis van een gevisualiseerd procescanvas. Het voorgestelde raamwerk integreert IoT-gebaseerde gegevensverwerving, gegevensvoorbewerking, feature engineering, workflow-orkestratie, op machine learning gebaseerde validatie, cloud-edge computing en real-time visualisatie binnen een uniforme architectuur om geautomatiseerde en betrouwbare acceptatietesten van LV-netten te ondersteunen met behulp van smart-metergegevens uit de praktijk. Figuur 1 geeft een overzicht van de workflow-georiënteerde architectuur van het voorgestelde platform. De workflow omvat gegevensverwerving, voorbewerking, feature engineering, workflow-orkestratie, intelligente besluitvorming en cloud-edge-integratie, wat geautomatiseerde validatie van LV-distributienetten mogelijk maakt via een procesgeoriënteerd raamwerk. Het platform demonstreerde een hoge validatienauwkeurigheid, terwijl de latentie van operationele besluitvorming en de uitvoeringstijd van de workflow werden verminderd in vergelijking met conventionele benaderingen.
Figuur 1. Workflow-georiënteerde architectuur van het voorgestelde automatiseringsplatform voor laagspanningsacceptatie. Hoogwaardige architectuur van het voorgestelde procesgestuurde automatiseringsplatform voor laagspanningsacceptatie (LV). Het raamwerk illustreert de sequentiële integratie van IoT-gestuurde gegevensverwerving, gegevensvoorbewerking, feature engineering, workflow-orkestratie, intelligente besluitvorming, cloud-edge samenwerking en geautomatiseerde acceptatie voor de validatie van LV-distributienetwerken. IoT = Internet of Things; LV = laagspanning; ML = machine learning. Klik hier om een grotere versie van deze figuur te bekijken.
Protocol
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.
Resultaten
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.
Discussie
De experimentele evaluatie toonde aan dat het voorgestelde workflow-georiënteerde Low-Voltage (LV) Acceptance Automation Platform consistent beter presteerde dan de benchmark-benaderingen op het gebied van classificatie, voorspelling, foutdetectie, latentie van operationele besluitvorming en schaalbaarheid. Deze prestatieverbeteringen waren het resultaat van de integratie van geavanceerde feature engineering, workflow-orchestratie, cloud-edge-samenwerking en hybride regelgebaseerde en machine learning-besluitvorming, in plaats van het gebruik van een enkel machine learning-model. De bevindingen komen overeen met eerdere studies die aantonen dat workflow-georiënteerde automatisering en intelligente IoT-architecturen de operationele efficiëntie, interoperabiliteit en besluitvormingsondersteuning in industriële toepassingen en smart grids verbeteren2,3,9,10,18.
De statistische analyses hebben de betrouwbaarheid van de experimentele bevindingen verder versterkt. Paarsgewijze significantietoetsing, een-weg variantieanalyse (ANOVA), betrouwbaarheidsintervalanalyse en Receiver Operating Characteristic (ROC)-analyse bevestigden dat het voorgestelde raamwerk statistisch significante verbeteringen behaalde ten opzichte van de benchmarkmethoden, terwijl een uitstekend classificatievermogen over verschillende beslissingsdrempels werd gehandhaafd47,48,49. Deze statistische evaluaties leveren aanvullend bewijs dat de waargenomen prestatieverbeteringen niet te wijten waren aan willekeurige variatie.
Schaalbaarheids- en ablatieonderzoeken toonden verder de effectiviteit van de voorgestelde architectuur aan onder toenemende computationele werklasten en verschillende systeemconfiguraties. De schaalbaarheidsanalyse wees uit dat het raamwerk lagere verwerkingstijden behield dan de benchmarkmethoden naarmate de datasetgrootte toenam van 1.000–5.000 monsters. Evenzo bevestigde de ablatiestudie dat elk hoofdonderdeel bijdroeg aan de algehele systemprestaties. Feature engineering verbeterde de representatie van operationele kenmerken geëxtraheerd uit slimme metergegevens, de intelligente beslissingsmodule verbeterde de foutvoorspelling en automatische validatie, workflow-orchestratie optimaliseerde de taakplanning en -uitvoering, en cloud-edge-samenwerking verminderde de communicatie-overhead en de latentie bij operationele beslissingen. Deze observaties komen overeen met eerdere rapporten die de voordelen van cloud-edge-architecturen en procesorchestratie benadrukken voor het realiseren van schaalbare industriële IoT-systemen met een lage latentie9,15,19,20,21.
Omdat het voorgestelde raamwerk functioneert binnen een IoT-ondersteunde LV-omgeving, zijn cybersecurity en gegevensprivacy belangrijke overwegingen voor praktische implementatie. Veilige communicatie tussen slimme meters, edge-apparaten, gateways en clouddiensten kan worden gerealiseerd met behulp van Transport Layer Security (TLS)-ingeschakelde MQTT- en HTTPS-protocollen. Rolgebaseerde toegangscontrole (RBAC) biedt authenticatie en autorisatie voor workflowbeheer en systeembronnen, terwijl Advanced Encryption Standard (AES)-256-encryptie operationele gegevens tijdens transmissie en opslag beschermt. Auditlogging en veilige cloud-edge-synchronisatie ondersteunen verder de gegevensintegriteit, vertrouwelijkheid en traceerbaarheid gedurende de acceptatieworkflow. Deze beveiligingsmechanismen zijn in overeenstemming met de huidige aanbevelingen voor veilige Industrial Internet of Things (IIoT)- en smart grid-communicatieinfrastructuren6,7,19,20,21.
Het voorgestelde raamwerk draagt daarnaast bij aan de ontwikkeling van intelligente stroomdistributiesystemen door de introductie van een uniforme workflow-georiënteerde architectuur die cloud-edge-collaboratie, workflow-orkestratie, feature engineering en besluitvorming op basis van machine learning integreert binnen één enkel automatiseringsplatform. In tegenstelling tot conventionele IoT-gebaseerde monitoringbenaderingen die primair gericht zijn op gegevensverwerving en gecentraliseerde verwerking1,10,11,12, ondersteunt het voorgestelde raamwerk geautomatiseerde validatie, intelligente foutdetectie en procesgestuurde besluitvorming. De schaalbaarheids- en ablatiestudies tonen verder de voordelen aan van modulaire intelligente architecturen voor toekomstig slim energiemanagement en Industry 4.0-toepassingen3,9,14,15.
Vanuit een praktisch perspectief maakt het voorgestelde LV Acceptance Automation Platform real-time monitoring, geautomatiseerde validatie, intelligente foutdetectie en acceptabeslissingen mogelijk met minimale menselijke tussenkomst. De behaalde operationele beslissingslatentie van 31 ms en de foutdetectiegraad van 98,4% tonen het vermogen van het raamwerk aan om real-time industriële toepassingen te ondersteunen. Bovendien vermindert de cloud-edge collaboratieve architectuur de communicatie-overhead, terwijl het de schaalbaarheid biedt die nodig is om gegevens te verwerken die zijn verkregen van slimme meters, gedistribueerde energiebronnen en IoT-apparaten. Deze mogelijkheden sluiten aan bij de huidige trends in smart grid-automatisering en intelligente energiemanagementsystemen18,19,20,21,27,29,30.
Ondanks de veelbelovende resultaten moeten verschillende beperkingen worden erkend. De experimentele evaluatie is uitgevoerd met de Open LV Network & Smart Meter Dataset samen met synthetische foutgegevens, omdat openbaar beschikbare LV-datasets geen uitgebreide foutannotaties bieden. Bijgevolg weerspiegelen de gesimuleerde foutscenario's en regelgebaseerde labels mogelijk niet volledig de diversiteit van operationele omstandigheden in de echte wereld. Bovendien is de validatie uitgevoerd met een enkele dataset, en de praktische implementatie kan worden beïnvloed door communicatiebeperkingen, sensorfouten en de computationele beperkingen van edge-apparaten die werken onder heterogene netwerkomstandigheden. Hoewel cybersecuritymechanismen zijn overwogen binnen de voorgestelde architectuur, lagen hun implementatie en evaluatie buiten de reikwijdte van deze studie. Toekomstig werk zal zich daarom richten op het valideren van het raamwerk met onafhankelijke datasets uit de echte wereld, diverse LV-netwerktopologieën en scenario's voor implementatie in het veld. Aanvullend onderzoek zal adaptief intelligent energiemanagement, uitlegbare kunstmatige intelligentie, monitoring met behulp van digitale tweelingen en verbeterde cloud-edge-samenwerking onderzoeken om de robuustheid, schaalbaarheid en interpreteerbaarheid van intelligente LV-acceptatieautomatiseringssystemen verder te verbeteren40,41.
Openbaarmakingen
De auteurs verklaren dat zij geen concurrerende financiële of niet-financiële belangen hebben.
Dankbetuigingen
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.
Materialen
| Naam | Bedrijf | Catalogusnummer | Opmerkingen |
|---|---|---|---|
| Apache Spark | Apache Software Foundation | Versie 3.4+ | Grootschalige data-analyse |
| Apache Superset | Apache Software Foundation | Versie 3.x | Analytics-dashboard |
| Docker Engine | Docker Inc. | Versie 24.x | Implementatie van applicaties |
| Eclipse Mosquitto MQTT-broker | Eclipse Foundation | Versie 2.x | Gegevensuitwisseling |
| EdgeX Foundry (Jakarta Release of later) | LF Edge (Linux Foundation) | Versie Jakarta / Genève | Beheer van edge-apparaten |
| Flask / FastAPI | Pallets Projecten / Sebastián RamíSinds u geen brontekst heeft verstrekt, kan ik geen vertaling uitvoeren. Voer a.u.b. de Engelse tekst in die u vertaald wilt hebben naar het Nederlands. | Flask 2.3+ / FastAPI 0.100+ | Integratie van de backend-API |
| Grafana | Grafana Labs | Versie 10.x | Visualisatie en monitoring |
| Industriële IoT-gateway | Advantech / Siemens | ECU-1251 / SIMATIC IOT2040 | Sensor-naar-cloud-communicatie |
| IoT-sensoren (spannings-/stroomsensoren) | Texas Instruments / Analog Devices | INA219 / ACS712 | Real-time netwerkmonitoring |
| Jetson Nano Developer Kit | NVIDIA Corporation | 945-13450-0000-100 | Edge AI-inferentie |
| Kubernetes | Cloud Native Computing Foundation (CNCF) | Versie 1.27+ | Schaalbaarheid van de workflow |
| LSTM-model (Keras/TensorFlow) | Google (TensorFlow/Keras) | Inbegrepen in TF 2.12+ | Belastingsvoorspelling en prognose |
| PLC-module (Power Line Communication) | STMicroelectronics | ST7580 | Communicatie in slimme netwerken |
| Plotly Dash | Plotly Inc. | Versie 2.x | Interactieve visualisatie |
| Python 3.10 | Python Software Foundation | Versie 3.10.x | Gegevensverwerking en analyse |
| PyTorch | Meta AI | Versie 2.0+ | Implementatie van deep learning |
| Random Forest-algoritme | Scikit-learn | Inbegrepen in v1.3+ | Foutclassificatie |
| Raspberry Pi 4 Model B (4 GB RAM) | Raspberry Pi Foundation | RPI4-MODBP-4GB | Edge-verwerking |
| React.js / Angular | Meta / Google | React 18.x / Angular 16+ | Ontwikkeling van de gebruikersinterface |
| RS-485-transceivermodule | Maxim Integrated (Analog Devices) | MAX485 | Connectiviteit van industriële apparaten |
| Scikit-learn | Scikit-learn-ontwikkelaars | Versie 1.3+ | Machine learning-modellen |
| Support Vector Machine (SVM) | Scikit-learn | Inbegrepen in v1.3+ | Classificatie en validatie |
| TensorFlow | Google LLC | Versie 2.12+ | Modeltraining |
| Twilio / Firebase Cloud Messaging | Twilio Inc. / Google | Niet van toepassing | Waarschuwingen en meldingen |
| Wi-Fi-module | Espressif Systems | ESP8266 / ESP32 | Gegevensoverdracht |
| XGBoost | DMLC (Distributed ML Community) | Versie 1.7+ | Intelligente beslissingsondersteuning |
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- Babayigit B, Abubaker M. Industrial Internet of Things: A review of improvements over traditional SCADA systems for industrial automation. IEEE Syst J. 2023;18:120–133.
- Nakagawa EY, et al. Industry 4.0 reference architectures: State of the art and future trends. Comput Ind Eng. 2021;156:107241. https://doi.org/10.1016/j.cie.2021.107241
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