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Electric Water Boiler Energy Prediction: State-of-the-Art Review of Influencing Factors, Techniques, and Future Directions

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  • Ibrahim Ali Kachalla

    (CETHIL UMR 5008, INSA Lyon, Université de Lyon, F-69621 Villeurbanne, France)

  • Christian Ghiaus

    (CETHIL UMR 5008, INSA Lyon, Université de Lyon, F-69621 Villeurbanne, France)

Abstract

Accurate and efficient prediction of electric water boiler (EWB) energy consumption is significant for energy management, effective demand response, cost minimisation, and robust control strategies. Adequate tracking and prediction of user behaviour can enhance renewable energy mini-grid (REMD) management. Fulfilling these demands for predicting the energy consumption of electric water boilers (EWB) would facilitate the establishment of a new framework that can enhance precise predictions of energy consumption trends for energy efficiency and demand management, which necessitates this state-of-the-art review. This article first reviews the factors influencing the prediction of energy consumption of electric water boilers (EWB); subsequently, it conducts a critical review of the current approaches and methods for predicting electric water boiler (EWB) energy consumption for residential building applications; after that, the performance evaluation methods are discussed. Finally, research gaps are ascertained, and recommendations for future work are summarised.

Suggested Citation

  • Ibrahim Ali Kachalla & Christian Ghiaus, 2024. "Electric Water Boiler Energy Prediction: State-of-the-Art Review of Influencing Factors, Techniques, and Future Directions," Energies, MDPI, vol. 17(2), pages 1-32, January.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:2:p:443-:d:1320347
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    1. Wang, Wei & Hong, Tianzhen & Li, Nan & Wang, Ryan Qi & Chen, Jiayu, 2019. "Linking energy-cyber-physical systems with occupancy prediction and interpretation through WiFi probe-based ensemble classification," Applied Energy, Elsevier, vol. 236(C), pages 55-69.
    2. Kazmi, H. & D’Oca, S. & Delmastro, C. & Lodeweyckx, S. & Corgnati, S.P., 2016. "Generalizable occupant-driven optimization model for domestic hot water production in NZEB," Applied Energy, Elsevier, vol. 175(C), pages 1-15.
    3. Shilpa Bindu & José Pablo Chaves Ávila & Luis Olmos, 2023. "Factors Affecting Market Participant Decision Making in the Spanish Intraday Electricity Market: Auctions vs. Continuous Trading," Energies, MDPI, vol. 16(13), pages 1-23, July.
    4. Simon Hagemann & Christoph Weber, 2015. "Trading Volumes in Intraday Markets - Theoretical Reference Model and Empirical Observations in Selected European Markets," EWL Working Papers 1503, University of Duisburg-Essen, Chair for Management Science and Energy Economics, revised Apr 2015.
    5. Heidari, Amirreza & Maréchal, François & Khovalyg, Dolaana, 2022. "An occupant-centric control framework for balancing comfort, energy use and hygiene in hot water systems: A model-free reinforcement learning approach," Applied Energy, Elsevier, vol. 312(C).
    6. Bourke, Grant & Bansal, Pradeep & Raine, Robert, 2014. "Performance of gas tankless (instantaneous) water heaters under various international standards," Applied Energy, Elsevier, vol. 131(C), pages 468-478.
    7. Ueno, Tsuyoshi & Inada, Ryo & Saeki, Osamu & Tsuji, Kiichiro, 2006. "Effectiveness of an energy-consumption information system for residential buildings," Applied Energy, Elsevier, vol. 83(8), pages 868-883, August.
    8. McKenna, Eoghan & Thomson, Murray, 2016. "High-resolution stochastic integrated thermal–electrical domestic demand model," Applied Energy, Elsevier, vol. 165(C), pages 445-461.
    9. Fan, Cheng & Xiao, Fu & Wang, Shengwei, 2014. "Development of prediction models for next-day building energy consumption and peak power demand using data mining techniques," Applied Energy, Elsevier, vol. 127(C), pages 1-10.
    10. Amasyali, Kadir & El-Gohary, Nora M., 2018. "A review of data-driven building energy consumption prediction studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1192-1205.
    11. Zhou, Yue & Wang, Chengshan & Wu, Jianzhong & Wang, Jidong & Cheng, Meng & Li, Gen, 2017. "Optimal scheduling of aggregated thermostatically controlled loads with renewable generation in the intraday electricity market," Applied Energy, Elsevier, vol. 188(C), pages 456-465.
    12. Carlos Adrian Correa-Florez & Andrea Michiorri & Georges Kariniotakis, 2019. "Comparative Analysis of Adjustable Robust Optimization Alternatives for the Participation of Aggregated Residential Prosumers in Electricity Markets," Energies, MDPI, vol. 12(6), pages 1-27, March.
    13. Kim, Dongwoo & Yim, Taesu & Lee, Jae Yong, 2021. "Analytical study on changes in domestic hot water use caused by COVID-19 pandemic," Energy, Elsevier, vol. 231(C).
    14. Zhang, Liang & Wen, Jin & Li, Yanfei & Chen, Jianli & Ye, Yunyang & Fu, Yangyang & Livingood, William, 2021. "A review of machine learning in building load prediction," Applied Energy, Elsevier, vol. 285(C).
    15. Pied, Marie & Anjos, Miguel F. & Malhamé, Roland P., 2020. "A flexibility product for electric water heater aggregators on electricity markets," Applied Energy, Elsevier, vol. 280(C).
    16. Zhengwei Qu & Chenglin Xu & Kai Ma & Zongxu Jiao, 2019. "Fuzzy Neural Network Control of Thermostatically Controlled Loads for Demand-Side Frequency Regulation," Energies, MDPI, vol. 12(13), pages 1-15, June.
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