IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v259y2020ics030626191931832x.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S030626191931832X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2019.114145?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Oluwole Owoye, 1995. "The causal relationship between taxes and expenditures in the G7 countries: cointegration and error-correction models," Applied Economics Letters, Taylor & Francis Journals, vol. 2(1), pages 19-22.
    2. David Findley, 1991. "Counterexamples to parsimony and BIC," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 43(3), pages 505-514, September.
    3. Laura Auria & Rouslan A. Moro, 2008. "Support Vector Machines (SVM) as a Technique for Solvency Analysis," Discussion Papers of DIW Berlin 811, DIW Berlin, German Institute for Economic Research.
    4. Spagnuolo, Antonio & Petraglia, Antonio & Vetromile, Carmela & Formosi, Roberto & Lubritto, Carmine, 2015. "Monitoring and optimization of energy consumption of base transceiver stations," Energy, Elsevier, vol. 81(C), pages 286-293.
    5. 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.
    6. Thiaux, Yaël & Dang, Thu Thuy & Schmerber, Louis & Multon, Bernard & Ben Ahmed, Hamid & Bacha, Seddik & Tran, Quoc Tuan, 2019. "Demand-side management strategy in stand-alone hybrid photovoltaic systems with real-time simulation of stochastic electricity consumption behavior," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Rongheng Lin & Shuo Chen & Zheyu He & Budan Wu & Hua Zou & Xin Zhao & Qiushuang Li, 2024. "Electricity Behavior Modeling and Anomaly Detection Services Based on a Deep Variational Autoencoder Network," Energies, MDPI, vol. 17(16), pages 1-20, August.
    2. 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).
    3. Wang, Xinlin & Flores, Robert & Brouwer, Jack & Papaefthymiou, Marios, 2022. "Real-time detection of electrical load anomalies through hyperdimensional computing," Energy, Elsevier, vol. 261(PA).
    4. 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).
    5. Wang, Xinlin & Yao, Zhihao & Papaefthymiou, Marios, 2023. "A real-time electrical load forecasting and unsupervised anomaly detection framework," Applied Energy, Elsevier, vol. 330(PA).
    6. 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).
    7. 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).
    8. Xinghua Wang & Zilv Li & Chenyang Fu & Xixian Liu & Weikang Yang & Xiangyuan Huang & Longfa Yang & Jianhui Wu & Zhuoli Zhao, 2024. "Short-Term Photovoltaic Power Probabilistic Forecasting Based on Temporal Decomposition and Vine Copula," Sustainability, MDPI, vol. 16(19), pages 1-25, September.
    9. 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.
    10. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zeljković, Čedomir & Mršić, Predrag & Erceg, Bojan & Lekić, Đorđe & Kitić, Nemanja & Matić, Petar, 2022. "Optimal sizing of photovoltaic-wind-diesel-battery power supply for mobile telephony base stations," Energy, Elsevier, vol. 242(C).
    2. A. Phiri, 2019. "Asymmetries in the revenue–expenditure nexus: new evidence from South Africa," Empirical Economics, Springer, vol. 56(5), pages 1515-1547, May.
    3. Afonso, António & Coelho, José Carlos, 2024. "Drivers of fiscal sustainability: A time-varying analysis for Portugal," International Economics, Elsevier, vol. 178(C).
    4. 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.
    5. Chen, Zhelun & O’Neill, Zheng & Wen, Jin & Pradhan, Ojas & Yang, Tao & Lu, Xing & Lin, Guanjing & Miyata, Shohei & Lee, Seungjae & Shen, Chou & Chiosa, Roberto & Piscitelli, Marco Savino & Capozzoli, , 2023. "A review of data-driven fault detection and diagnostics for building HVAC systems," Applied Energy, Elsevier, vol. 339(C).
    6. Fan, Cheng & Sun, Yongjun & Zhao, Yang & Song, Mengjie & Wang, Jiayuan, 2019. "Deep learning-based feature engineering methods for improved building energy prediction," Applied Energy, Elsevier, vol. 240(C), pages 35-45.
    7. Xavier Serrano-Guerrero & Guillermo Escrivá-Escrivá & Santiago Luna-Romero & Jean-Michel Clairand, 2020. "A Time-Series Treatment Method to Obtain Electrical Consumption Patterns for Anomalies Detection Improvement in Electrical Consumption Profiles," Energies, MDPI, vol. 13(5), pages 1-23, February.
    8. Netzah Calamaro & Yuval Beck & Ran Ben Melech & Doron Shmilovitz, 2021. "An Energy-Fraud Detection-System Capable of Distinguishing Frauds from Other Energy Flow Anomalies in an Urban Environment," Sustainability, MDPI, vol. 13(19), pages 1-38, September.
    9. Kevin L. Ross & James E. Payne, 1998. "A Reexamination of Budgetary Disequilibria," Public Finance Review, , vol. 26(1), pages 67-79, January.
    10. Aurelia Rybak & Aleksandra Rybak & Spas D. Kolev, 2023. "Modeling the Photovoltaic Power Generation in Poland in the Light of PEP2040: An Application of Multiple Regression," Energies, MDPI, vol. 16(22), pages 1-17, November.
    11. Kyuhan Lee & Jinsoo Park & Iljoo Kim & Youngseok Choi, 2018. "Predicting movie success with machine learning techniques: ways to improve accuracy," Information Systems Frontiers, Springer, vol. 20(3), pages 577-588, June.
    12. Nadeem Iqbal & Wasim Shahid Malik, 2010. "Budget Balance: Through Revenue or Spending Adjustment: Evidence from Pakistan," The Pakistan Development Review, Pakistan Institute of Development Economics, vol. 49(4), pages 611-630.
    13. Zhao, Guanjia & Cui, Zhipeng & Xu, Jing & Liu, Wenhao & Ma, Suxia, 2022. "Hybrid modeling-based digital twin for performance optimization with flexible operation in the direct air-cooling power unit," Energy, Elsevier, vol. 254(PC).
    14. Wang, Xinlin & Yao, Zhihao & Papaefthymiou, Marios, 2023. "A real-time electrical load forecasting and unsupervised anomaly detection framework," Applied Energy, Elsevier, vol. 330(PA).
    15. Gao, Bixuan & Kong, Xiangyu & Li, Shangze & Chen, Yi & Zhang, Xiyuan & Liu, Ziyu & Lv, Weijia, 2024. "Enhancing anomaly detection accuracy and interpretability in low-quality and class imbalanced data: A comprehensive approach," Applied Energy, Elsevier, vol. 353(PB).
    16. Francisco de Castro & José Manuel González-Páramo & Pablo Hernández de Cos, 2004. "Fiscal consolidation in Spain: dynamic interdependence of public spending and revenues," Investigaciones Economicas, Fundación SEPI, vol. 28(1), pages 193-207, January.
    17. Wang, Xuewei & Wang, Jing & Wang, Lin & Yuan, Ruiming, 2019. "Non-overlapping moving compressive measurement algorithm for electrical energy estimation of distorted m-sequence dynamic test signal," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    18. Liang, Xinbin & Zhu, Xu & Chen, Kang & Chen, Siliang & Jin, Xinqiao & Du, Zhimin, 2023. "Endowing data-driven models with rejection ability: Out-of-distribution detection and confidence estimation for black-box models of building energy systems," Energy, Elsevier, vol. 263(PC).
    19. Yashobanta Parida, 2012. "Causal Link between Central Government Revenue and Expenditure: Evidence for India," Economics Bulletin, AccessEcon, vol. 32(4), pages 2808-2816.
    20. Inoue, Atsushi & Kilian, Lutz, 2006. "On the selection of forecasting models," Journal of Econometrics, Elsevier, vol. 130(2), pages 273-306, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:259:y:2020:i:c:s030626191931832x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.