Zorientowana na procesy platforma automatyzacji odbiorów niskiego napięcia z wizualną orchestracją przepływu pracy i walidacją opartą na uczeniu maszynowym

Podsumowanie

Proponowana Platforma Automatyzacji Odbiorów Niskiego Napięcia integruje Internet Rzeczy (IoT), wizualną orchestrację przepływu pracy, obliczenia krawędziowe i chmurowe, inżynierię cech oraz uczenie maszynowe w celu zautomatyzowania testów odbiorczych systemów niskiego napięcia (LV). Platforma umożliwia inteligentne wykrywanie usterek, podejmowanie decyzji w czasie rzeczywistym, automatyczny odbiór, wysoką dokładność, niskie opóźnienia operacyjne oraz skalowalne wdrożenie.

Streszczenie

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%.

Wprowadzenie

Przemysł 4.0 (I4.0)¹ jest możliwy dzięki kilku wyłaniającym się technologiom, spośród których przemysłowy internet rzeczy (IIoT) jest jedną z najważniejszych. IIoT odnosi się do zastosowania technologii internetu rzeczy (IoT) w środowiskach przemysłowych, w których połączone urządzenia i maszyny komunikują się głównie poprzez interakcje typu maszyna-maszyna. Ponieważ awarie w systemach przemysłowych mogą prowadzić do znacznych strat operacyjnych, aplikacje IIoT wymagają wysoce niezawodnej komunikacji, generując jednocześnie znacznie większe wolumeny danych niż konwencjonalne systemy IoT². Ponadto niektóre badania traktują IIoT jako koncepcję ściśle powiązaną z Przemysłem 4.0³. Technologie IoT przekształcają tradycyjne architektury hierarchiczne, umożliwiając tworzenie bardziej elastycznych systemów z płynną wymianą danych⁴. Liczne niedawne badania poddały analizie istniejące architektury IIoT i zaproponowały nowe projekty ramowe (frameworki)^5,6,7,8,9. Pomimo tych osiągnięć, pozostają dwa główne ograniczenia. Po pierwsze, często proponowane są nowe ramy w celu przezwyciężenia niedoskonałości poprzednich architektur, co skutkuje rosnącą liczbą alternatywnych modeli, zwiększając różnorodność architektoniczną i złożoność wdrożenia. Po drugie, wysoki poziom abstrakcji wielu proponowanych architektur ogranicza ich praktyczne zastosowanie w środowiskach przemysłowych¹⁰.

W literaturze opisano kilka architektur IIoT. Architektura IIoT oparta na standardzie Open Platform Communications (OPC) została przedstawiona wraz z pośrednikiem (middleware) w języku Python do zarządzania komunikacją OPC¹¹. Zaproponowano siedmiowarstwową architekturę IIoT; jednak nie podano szczegółów implementacji ani eksperymentów walidacyjnych¹². Opisano framework integrujący technologie 5G i IIoT wraz z reprezentatywnym przypadkiem użycia, choć walidacja eksperymentalna była ograniczona¹³. Wprowadzono framework projektowania emocjonalnego dla Przemysłu 4.0 i omówiono jego potencjalne implikacje bez weryfikacji eksperymentalnej¹⁴. Wielowarstwowa platforma IIoT została oceniona w rzeczywistym środowisku testowym, z naciskiem na kwestie telekomunikacyjne¹⁵. Podobnie, przedstawiono modułowy framework IoT do monitorowania jakości powietrza wewnątrz pomieszczeń, a jego wydajność oceniono na podstawie danych zebranych z 84 gospodarstw domowych z dziećmi¹⁶.

Internet Rzeczy (IoT) stał się szybko rozwijającą się technologią o znacznym potencjale zastosowań w sektorze energetyki elektrycznej^17,18. Integracja technologii IoT w węzłach systemu energetycznego umożliwia bardziej inteligentną i efektywną generację, przesył, dystrybucję, eksploatację i wykorzystanie energii dzięki autonomicznemu podejmowaniu decyzji wspieranemu przez ciągły monitoring przepływu elektryczności w inteligentnej sieci (smart grid) w czasie rzeczywistym. W rezultacie inteligentne węzły te poprawiają ogólną wydajność operacyjną sieci elektrycznej¹⁹. Technologia komunikacyjna stanowi fundamentalny element wdrażania inteligentnych sieci, ponieważ dwie główne kategorie informacji muszą być przesyłane niezawodnie: pomiary z czujników oraz sygnały sterujące. Oprócz niskich opóźnień komunikacyjnych, aplikacje inteligentnych sieci wymagają odpowiedniej jakości usług (QoS) oraz właściwego bezpieczeństwa danych w celu zapewnienia niezawodnego działania²⁰.

Wybór odpowiedniej technologii komunikacyjnej dla zastosowań w inteligentnych sieciach energetycznych wymaga rozważenia interwałów komunikacyjnych, prędkości przesyłu danych oraz kosztów komunikacji²¹. Sieci komunikacyjne IoT zazwyczaj składają się z warstw oprogramowania, bezpieczeństwa, usług, systemowej oraz łączności. W obrębie warstwy łączności protokoły komunikacyjne są zazwyczaj klasyfikowane w trzech kategoriach. Protokoły dalekiego zasięgu obejmują Long Range (LoRa) oraz Narrowband-IoT (NB-IoT); protokoły średniego zasięgu obejmują Wi-Fi, 4G/LTE i 5G; natomiast protokoły krótkiego zasięgu obejmują Bluetooth i ZigBee. W miarę postępującej deregulacji rynków energii elektrycznej, prognozowanie obciążenia stało się bardziej istotne dla oceny wpływu warunków pogodowych, awarii sprzętu, zmian regulacyjnych i innych czynników operacyjnych na koszt usług elektrycznych. W konsekwencji dokładne prognozowanie obciążenia w sektorze mieszkalnym stało się niezbędne dla funkcjonowania i nadzoru nad inteligentnymi sieciami energetycznymi, mikrosieciami oraz inteligentnymi budynkami²².

Niezawodna praca sieci niskiego napięcia (LV) zależy od utrzymania wysokiej jakości energii²³. Jakość energii jest określana przez skuteczność regulacji napięcia, zrównoważenie prądów oraz ograniczanie zakłóceń technicznych w niesymetrycznych warunkach pracy²⁴. Wśród tych czynników szczególnie istotne jest zrównoważenie faz, ponieważ determinuje ono jednolity rozkład napięć i prądów w poszczególnych fazach²⁵. Niewłaściwe zrównoważenie faz może prowadzić do zwiększonego nagrzewania przewodników, odchyłek napięcia oraz wzrostu prądów w przewodzie neutralnym, co obniża sprawność systemu i niezawodność eksploatacyjną²⁶. W związku z tym, podczas procesu projektowania należy uwzględnić zrównoważenie faz, aby poprawić wydajność systemu, stabilność pracy i długoterminową niezawodność. Niemniej jednak aspekt ten jest rzadko uwzględniany w konwencjonalnych metodologiach projektowania mikrosieci.

Ostatnie postępy w systemach orchestracji przepływu pracy wykazały korzyści płynące z automatyzacji sterowanej zdarzeniami oraz wizualnego zarządzania procesami w środowiskach przemysłowego IoT. Niemniej jednak, ich zastosowanie w walidacji sieci dystrybucyjnych i odbiorczych badaniach niskiego napięcia pozostaje ograniczone. Większość istniejących systemów automatyki niskiego napięcia koncentruje się głównie na monitorowaniu i nadzorowaniu, podczas gdy stosunkowo niewiele badań dotyczy zarządzania odbiorami w oparciu o przepływy pracy lub zautomatyzowanej weryfikacji zgodności. Podobnie, choć techniki uczenia maszynowego przyciągnęły znaczną uwagę w zakresie wykrywania anomalii w inteligentnych licznikach i oceny jakości energii, podejścia te są zazwyczaj opracowywane jako niezależne modele analityczne, a nie jako komponenty zintegrowanego frameworku orchestracji przepływu pracy.

W ostatnich latach zaproponowano różne rozwiązania inteligentnych liczników energii dla zarządzania inteligentnymi budynkami i automatyki domowej²⁷. W publikacji²⁷ zaprezentowano prototyp inteligentnego licznika, aby zademonstrować integrację koncepcji edukacyjnych na poziomie uniwersyteckim z technologiami inteligentnego pomiaru. Opracowany inteligentny licznik wykazał znaczny potencjał w zakresie zarządzania i kontroli energii, redukując zużycie energii i przynosząc korzyści zarówno przedsiębiorstwom energetycznym, jak i konsumentom dzięki dwukierunkowej komunikacji IoT. Następnie wprowadzono system inteligentnych gniazdek oparty na mikrokontrolerze w celu wsparcia inteligentnego monitorowania energii²⁸.

Zaproponowany system inteligentnych gniazdek wyświetla zużycie energii w czasie rzeczywistym za pomocą graficznego interfejsu użytkownika opartego na systemie Android. Ponadto urządzenie potrafi wykryć obecność podłączonych urządzeń elektrycznych, co jest funkcją określaną jako inwazyjny pomiar obciążenia²⁸. Do implementacji wykorzystano niedrogi komercyjny inteligentny gniazdek. Przyszłe ulepszenia obejmują monitorowanie wartości skutecznej (RMS) prądu, mocy czynnej, mocy biernej oraz kąta przesunięcia fazowego między napięciem a prądem. Jak opisano w naszych poprzednich badaniach, częściowo przyłączona do sieci mikrosieć fotowoltaiczna wykorzystuje protokoły komunikacyjne ZigBee i LoRa do wymiany informacji z urządzeniami liczników inteligentnych, co wykazuje wykonalność komunikacji opartej na IoT dla rozproszonych systemów energetycznych^29,30.

Aby rozwiązać ograniczenia konwencjonalnego planowania systemów dystrybucyjnych, praktyka inżynierska oraz badania naukowe rozwijały się w kilku komplementarnych kierunkach. Jeden z kierunków badań koncentrował się na planowaniu wysokopoziomowym z wykorzystaniem systemów informacji geograficznej (GIS) w celu wyboru lokalizacji i wstępnej oceny zasobów^31,32. Inny kierunek skupiał się na ulepszaniu poszczególnych komponentów i podsystemów, w tym na wyborze przewodników, optymalnym doborze mocy generacji i pojemności magazynów energii oraz porównawczych ocenach systemów dystrybucji prądu przemiennego (AC) i prądu stałego (DC)^33,34,35,36. Trzeci kierunek badań zastosował algorytmy optymalizacji do planowania sieci dystrybucyjnych, wyznaczania tras linii zasilających i projektowania topologii sieci. Niedawne osiągnięcia obejmują rozwiązania takie jak platforma uGrid opracowana przez badaczy, która integruje rozmieszczenie urządzeń i konfigurację sieci, lecz nie posiada standardowej analizy przepływu mocy do weryfikacji i optymalizacji. Inne podejścia opierają się na ręcznym dostrajaniu parametrów w celu generowania topologii lub koncentrują się na specjalistycznych zastosowaniach, takich jak niskomocowe mikrosieci DC^37,38,39. Choć podejścia te reprezentują znaczący postęp, wymagane są dalsze prace w celu ustanowienia zintegrowanej metodologii kompleksowego projektowania niskonapięciowych systemów dystrybucyjnych. Ponadto kompleksowe oceny techniczno-ekonomiczne jednofazowych, trójfazowych i hybrydowych systemów niskonapięciowych pozostają ograniczone.

Ostatnie postępy w dziedzinie sztucznej inteligencji (AI) i uczenia maszynowego dodatkowo rozszerzyły możliwości inteligentnych systemów energetycznych. Kompleksowy przegląd przedstawiony w podsumowaniu omówił zastosowania AI i uczenia maszynowego w inteligentnych sieciach energetycznych, w tym technologie wschodzące, takie jak Federated Learning, Generative AI, Large Language Models, Artificial Intelligence of Things (AIoT) oraz Cyfrowe Bliźniaki (Digital Twins)⁴⁰. Przegląd ten wyróżnił zastosowania w prognozowaniu obciążenia, konserwacji predykcyjnej, detekcji anomalii, zarządzaniu stroną popytową oraz integracji pojazdów elektrycznych, kładąc jednocześnie nacisk na nieustające wyzwania związane z interoperacyjnością, prywatnością, skalowalnością, odpornością i kwestiami etycznymi. Inny niedawny przegląd oceniał metody AI w zakresie rozproszonego zarządzania energią w infrastrukturach typu IoT-edge-cloud, koncentrując się na uczeniu maszynowym, uczeniu ze wzmocnieniem i systemach wieloagentowych dla zdecentralizowanego sterowania energią⁴¹.

W ujęciu zbiorczym poprzednie badania przyczyniły się do rozwoju monitorowania w oparciu o IoT, inteligentnego pomiaru, obliczeń krawędziowo-chmurowych (cloud-edge computing), sztucznej inteligencji oraz inteligentnego zarządzania energią w niskonapięciowych (LV) systemach dystrybucyjnych. Technologie te były jednak w dużej mierze rozwijane niezależnie. Orkiestracja przepływu pracy, zautomatyzowane zarządzanie akceptacją, walidacja sterowana procesem oraz zintegrowane wsparcie decyzji pozostają ograniczone, co podkreśla potrzebę stworzenia ujednoliconego, zorientowanego na przepływ pracy frameworka automatyzacji.

W związku z tym niniejsze badanie przedstawia platformę automatyzacji odbiorów sieci niskiego napięcia opartą na wizualizowanym płótnie procesowym. Zaproponowana struktura integruje akwizycję danych w oparciu o IoT, wstępne przetwarzanie danych, inżynierię cech, orchestrację przepływu pracy, walidację opartą na uczeniu maszynowym, obliczenia chmurowo-krawędziowe oraz wizualizację w czasie rzeczywistym w ramach jednolitej architektury, aby wspierać zautomatyzowane i niezawodne testy odbiorowe sieci LV z wykorzystaniem rzeczywistych danych z inteligentnych liczników. Rycina 1 przedstawia ogólny schemat zorientowanej na przepływ pracy architektury proponowanej platformy. Przepływ pracy obejmuje akwizycję danych, wstępne przetwarzanie, inżynierię cech, orchestrację przepływu pracy, inteligentne podejmowanie decyzji oraz integrację chmurowo-krawędziową, co umożliwia zautomatyzowaną walidację sieci dystrybucyjnych LV poprzez ramy zorientowane procesowo. Platforma wykazała wysoką dokładność walidacji, jednocześnie redukując opóźnienia w podejmowaniu decyzji operacyjnych oraz czas wykonywania przepływu pracy w porównaniu z podejściami konwencjonalnymi.

Rysunek 1. Zorientowana na przepływ pracy architektura proponowanej platformy automatyzacji odbiorów niskiego napięcia. Architektura wysokiego poziomu proponowanej, sterowanej procesowo platformy automatyzacji odbiorów niskiego napięcia (LV). Schemat przedstawia sekwencyjną integrację akwizycji danych za pomocą IoT, preprocessingu danych, inżynierii cech, orchestracji przepływu pracy, inteligentnego podejmowania decyzji, współpracy w modelu chmura-krawędź (cloud-edge) oraz zautomatyzowanego odbioru dla walidacji sieci rozpdzielczej LV. IoT = Internet Rzeczy; LV = niskie napięcie; ML = uczenie maszynowe. Aby wyświetlić powiększoną wersję tego rysunku, kliknij tutaj.

Protokół

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 S_k). 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, …, n_m}denotes the workflow nodes corresponding to individual processing stages (e.g., data input, preprocessing, validation, and decision-making), and E = {e_ij}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 ∈ ℝ^d, 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.

Wyniki

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.

Dyskusja

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 applications^2,3,9,10,18.

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 thresholds^47,48,49. 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 systems^{9,15,19,20,21}.

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 infrastructures^6,7,19,20,21.

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 processing^1,10,11,12, 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 applications^3,9,14,15.

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 systems^{18,19,20,21,27,29,30}.

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 systems^40,41.

Materiały

Lista materiałów użytych w tym artykule

Nazwa	Firma	Numer katalogowy	Komentarze
Apache Spark	Apache Software Foundation	Wersja 3.4+	Wielkoskalowa analityka danych
Apache Superset	Apache Software Foundation	Wersja 3.x	Panel analityczny
Docker Engine	Docker Inc.	Wersja 24.x	Wdrażanie aplikacji
Eclipse Mosquitto MQTT Broker	Eclipse Foundation	Wersja 2.x	Wymiana danych
EdgeX Foundry (Jakarta Release lub nowsza)	LF Edge (Linux Foundation)	Wersja Jakarta / Geneva	Zarządzanie urządzeniami krawędziowymi
Flask / FastAPI	Pallets Projects / Sebastián Ramírez	Flask 2.3+ / FastAPI 0.100+	Integracja API backendu
Grafana	Grafana Labs	Wersja 10.x	Wizualizacja i monitorowanie
Bramka Industrial IoT	Advantech / Siemens	ECU-1251 / SIMATIC IOT2040	Komunikacja sensor-chmura
Czujniki IoT (czujniki napięcia/prądu)	Texas Instruments / Analog Devices	INA219 / ACS712	Monitorowanie sieci w czasie rzeczywistym
Jetson Nano Developer Kit	NVIDIA Corporation	945-13450-0000-100	Wnioskowanie AI na krawędzi
Kubernetes	Cloud Native Computing Foundation (CNCF)	Wersja 1.27+	Skalowalność przepływu pracy
Model LSTM (Keras/TensorFlow)	Google (TensorFlow/Keras)	Zawarty w TF 2.12+	Predykcja i prognozowanie obciążenia
Moduł PLC (Power Line Communication)	STMicroelectronics	ST7580	Komunikacja w inteligentnych sieciach energetycznych
Plotly Dash	Plotly Inc.	Wersja 2.x	Interaktywna wizualizacja
Python 3.10	Python Software Foundation	Wersja 3.10.x	Przetwarzanie i analityka danych
PyTorch	Meta AI	Wersja 2.0+	Implementacja głębokiego uczenia
Algorytm Random Forest	Scikit-learn	Zawarty w v1.3+	Klasyfikacja usterek
Raspberry Pi 4 Model B (4GB RAM)	Raspberry Pi Foundation	RPI4-MODBP-4GB	Przetwarzanie krawędziowe
React.js / Angular	Meta / Google	React 18.x / Angular 16+	Tworzenie interfejsu użytkownika
Moduł transceivera RS-485	Maxim Integrated (Analog Devices)	MAX485	Łączność z urządzeniami przemysłowymi
Scikit-learn	Scikit-learn Developers	Wersja 1.3+	Modele uczenia maszynowego
Maszyna wektorów nośnych (SVM)	Scikit-learn	Zawarty w v1.3+	Klasyfikacja i walidacja
TensorFlow	Google LLC	Wersja 2.12+	Trenowanie modelu
Twilio / Firebase Cloud Messaging	Twilio Inc. / Google	Nie dotyczy	Alerty i powiadomienia
Moduł Wi-Fi	Espressif Systems	ESP8266 / ESP32	Transmisja danych
XGBoost	DMLC (Distributed ML Community)	Wersja 1.7+	Inteligentne wsparcie decyzji