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Real-time prediction and anomaly detection of electrical load in a residential community

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  • Wang, Xinlin
  • Ahn, Sung-Hoon

Abstract

Regression model-based electrical load anomaly detection shows great potential to improve the quality of demand side management (DSM) because the load prediction and detection requirements can be satisfied by a single framework simultaneously. However, compared with other detection methods, both prediction and detection accuracy need improvement. To overcome this limitation, this work proposes a residential electrical load anomaly detection framework (RELAD) that includes a hybrid one-step-ahead load predictor (OSA-LP) and a rule-engine-based load anomaly detector (RE-AD). Considering that the diversity and randomness of residential electricity usage may render prediction difficult, the OSA-LP cascades an autoregressive integrated moving average (ARIMA) model and artificial neural networks (ANN) to achieve high precision in linear and nonlinear regression. Meanwhile, through employing the Bayesian information criterion (BIC), the OSA-LP efficiently reduces the influence of the over- or underfitting problem in real-time prediction and improves the prediction accuracy. To remedy the deficiency of overreliance on prediction outcomes in regression-model-based anomaly detection methods, the RE-AD integrates a support vector machine (SVM), the k-nearest neighbors (kNN) method and the cross-entropy loss function to develop an independent detection process to analyze the correctness of data. This method was applied to detect the load of the off-grid solar power plant in Ngurudoto, a rural area in Tanzania with 44 households and nearly 150 residents. The results of the practical application demonstrate that the proposed predictor and anomaly detector exhibit better predictive and detective accuracy than that achieved in previous work, which demonstrates the practicality of the proposed method.

Suggested Citation

  • Wang, Xinlin & Ahn, Sung-Hoon, 2020. "Real-time prediction and anomaly detection of electrical load in a residential community," Applied Energy, Elsevier, vol. 259(C).
    • Handle: RePEc:eee:appene:v:259:y:2020:i:c:s030626191931832x
    DOI: 10.1016/j.apenergy.2019.114145
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    Cited by:

    1. Yang, Wangwang & Shi, Jing & Li, Shujian & Song, Zhaofang & Zhang, Zitong & Chen, Zexu, 2022. "A combined deep learning load forecasting model of single household resident user considering multi-time scale electricity consumption behavior," Applied Energy, Elsevier, vol. 307(C).
    2. Wang, Xinlin & Flores, Robert & Brouwer, Jack & Papaefthymiou, Marios, 2022. "Real-time detection of electrical load anomalies through hyperdimensional computing," Energy, Elsevier, vol. 261(PA).
    3. Kong, Jun & Jiang, Wen & Tian, Qing & Jiang, Min & Liu, Tianshan, 2023. "Anomaly detection based on joint spatio-temporal learning for building electricity consumption," Applied Energy, Elsevier, vol. 334(C).
    4. Wang, Xinlin & Yao, Zhihao & Papaefthymiou, Marios, 2023. "A real-time electrical load forecasting and unsupervised anomaly detection framework," Applied Energy, Elsevier, vol. 330(PA).
    5. Wang, Xinlin & Wang, Hao & Ahn, Sung-Hoon, 2021. "Demand-side management for off-grid solar-powered microgrids: A case study of rural electrification in Tanzania," Energy, Elsevier, vol. 224(C).
    6. Himeur, Yassine & Ghanem, Khalida & Alsalemi, Abdullah & Bensaali, Faycal & Amira, Abbes, 2021. "Artificial intelligence based anomaly detection of energy consumption in buildings: A review, current trends and new perspectives," Applied Energy, Elsevier, vol. 287(C).
    7. 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.
    8. Yong Zhu & Mingyi Liu & Lin Wang & Jianxing Wang, 2022. "Potential Failure Prediction of Lithium-ion Battery Energy Storage System by Isolation Density Method," Sustainability, MDPI, vol. 14(12), pages 1-14, June.

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