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Anomaly Detection with Machine Learning Algorithms and Big Data in Electricity Consumption

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  • Simona-Vasilica Oprea

    (Department of Economic Informatics and Cybernetics, Bucharest University of Economic Studies, Romana Square 6, 010374 Bucharest, Romania)

  • Adela Bâra

    (Department of Economic Informatics and Cybernetics, Bucharest University of Economic Studies, Romana Square 6, 010374 Bucharest, Romania)

  • Florina Camelia Puican

    (Department of Economic Informatics and Cybernetics, Bucharest University of Economic Studies, Romana Square 6, 010374 Bucharest, Romania)

  • Ioan Cosmin Radu

    (Departament of Engineering in Foreign Languages, University Politehnica of Bucharest, Splaiul Independenței, No. 313, 060042 Bucharest, Romania)

Abstract

When analyzing smart metering data, both reading errors and frauds can be identified. The purpose of this analysis is to alert the utility companies to suspicious consumption behavior that could be further investigated with on-site inspections or other methods. The use of Machine Learning (ML) algorithms to analyze consumption readings can lead to the identification of malfunctions, cyberattacks interrupting measurements, or physical tampering with smart meters. Fraud detection is one of the classical anomaly detection examples, as it is not easy to label consumption or transactional data. Furthermore, frauds differ in nature, and learning is not always possible. In this paper, we analyze large datasets of readings provided by smart meters installed in a trial study in Ireland by applying a hybrid approach. More precisely, we propose an unsupervised ML technique to detect anomalous values in the time series, establish a threshold for the percentage of anomalous readings from the total readings, and then label that time series as suspicious or not. Initially, we propose two types of algorithms for anomaly detection for unlabeled data: Spectral Residual-Convolutional Neural Network (SR-CNN) and an anomaly trained model based on martingales for determining variations in time-series data streams. Then, the Two-Class Boosted Decision Tree and Fisher Linear Discriminant analysis are applied on the previously processed dataset. By training the model, we obtain the required capabilities of detecting suspicious consumers proved by an accuracy of 90%, precision score of 0.875, and F1 score of 0.894.

Suggested Citation

  • Simona-Vasilica Oprea & Adela Bâra & Florina Camelia Puican & Ioan Cosmin Radu, 2021. "Anomaly Detection with Machine Learning Algorithms and Big Data in Electricity Consumption," Sustainability, MDPI, vol. 13(19), pages 1-20, October.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:19:p:10963-:d:648867
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    References listed on IDEAS

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    1. Capozzoli, Alfonso & Piscitelli, Marco Savino & Brandi, Silvio & Grassi, Daniele & Chicco, Gianfranco, 2018. "Automated load pattern learning and anomaly detection for enhancing energy management in smart buildings," Energy, Elsevier, vol. 157(C), pages 336-352.
    2. Fan, Cheng & Xiao, Fu & Zhao, Yang & Wang, Jiayuan, 2018. "Analytical investigation of autoencoder-based methods for unsupervised anomaly detection in building energy data," Applied Energy, Elsevier, vol. 211(C), pages 1123-1135.
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    Cited by:

    1. Sepideh Radhoush & Maryam Bahramipanah & Hashem Nehrir & Zagros Shahooei, 2022. "A Review on State Estimation Techniques in Active Distribution Networks: Existing Practices and Their Challenges," Sustainability, MDPI, vol. 14(5), pages 1-16, February.
    2. Mobarak Abumohsen & Amani Yousef Owda & Majdi Owda, 2023. "Electrical Load Forecasting Using LSTM, GRU, and RNN Algorithms," Energies, MDPI, vol. 16(5), pages 1-31, February.
    3. Alexandra-Nicoleta Ciucu-Durnoi & Margareta Stela Florescu & Camelia Delcea, 2023. "Envisioning Romania’s Path to Sustainable Development: A Prognostic Approach," Sustainability, MDPI, vol. 15(17), pages 1-18, August.
    4. Yiran Wang & Shuowei Jin & Ming Cheng, 2023. "A Convolution–Non-Convolution Parallel Deep Network for Electricity Theft Detection," Sustainability, MDPI, vol. 15(13), pages 1-22, June.
    5. Yuping Zou & Rui Wu & Xuesong Tian & Hua Li, 2023. "Realizing the Improvement of the Reliability and Efficiency of Intelligent Electricity Inspection: IAOA-BP Algorithm for Anomaly Detection," Energies, MDPI, vol. 16(7), pages 1-15, March.
    6. Marta Moure-Garrido & Celeste Campo & Carlos Garcia-Rubio, 2022. "Entropy-Based Anomaly Detection in Household Electricity Consumption," Energies, MDPI, vol. 15(5), pages 1-21, March.

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