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Optimal and Learning-Based Demand Response Mechanism for Electric Water Heater System

Author

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  • Bo Lin

    (Department of Electrical and Computer Engineering, The University of Alabama, Tuscaloosa, AL 35487, USA)

  • Shuhui Li

    (Department of Electrical and Computer Engineering, The University of Alabama, Tuscaloosa, AL 35487, USA)

  • Yang Xiao

    (Department of Computer Science, The University of Alabama, Tuscaloosa, AL 35487, USA)

Abstract

This paper investigates how to develop a learning-based demand response approach for electric water heater in a smart home that can minimize the energy cost of the water heater while meeting the comfort requirements of energy consumers. First, a learning-based, data-driven model of an electric water heater is developed by using a nonlinear autoregressive network with external input (NARX) using neural network. The model is updated daily so that it can more accurately capture the actual thermal dynamic characteristics of the water heater especially in real-life conditions. Then, an optimization problem, based on the NARX water heater model, is formulated to optimize energy management of the water heater in a day-ahead, dynamic electricity price framework. A genetic algorithm is proposed in order to solve the optimization problem more efficiently. MATLAB (R2016a) is used to evaluate the proposed learning-based demand response approach through a computational experiment strategy. The proposed approach is compared with conventional method for operation of an electric water heater. Cost saving and benefits of the proposed water heater energy management strategy are explored.

Suggested Citation

  • Bo Lin & Shuhui Li & Yang Xiao, 2017. "Optimal and Learning-Based Demand Response Mechanism for Electric Water Heater System," Energies, MDPI, vol. 10(11), pages 1-17, October.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:11:p:1722-:d:116726
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    References listed on IDEAS

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    1. Christian Barteczko-Hibbert & Mark Gillott & Graham Kendall, 2009. "An artificial neural network for predicting domestic hot water characteristics," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 4(2), pages 112-119, April.
    2. Tao Hong & Jason Wilson & Jingrui Xie, 2013. "Long term probabilistic load forecasting and normalization with hourly information," HSC Research Reports HSC/13/13, Hugo Steinhaus Center, Wroclaw University of Technology.
    3. Linas Gelažanskas & Kelum A. A. Gamage, 2015. "Forecasting Hot Water Consumption in Residential Houses," Energies, MDPI, vol. 8(11), pages 1-16, November.
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    Cited by:

    1. Baxter Williams & Daniel Bishop & Patricio Gallardo & J. Geoffrey Chase, 2023. "Demand Side Management in Industrial, Commercial, and Residential Sectors: A Review of Constraints and Considerations," Energies, MDPI, vol. 16(13), pages 1-28, July.
    2. Antonopoulos, Ioannis & Robu, Valentin & Couraud, Benoit & Kirli, Desen & Norbu, Sonam & Kiprakis, Aristides & Flynn, David & Elizondo-Gonzalez, Sergio & Wattam, Steve, 2020. "Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
    3. Tiago Cardoso Pereira & Rui Amaral Lopes & João Martins, 2019. "Exploring the Energy Flexibility of Electric Water Heaters," Energies, MDPI, vol. 13(1), pages 1-11, December.
    4. Ángel Á. Pardiñas & Pablo Durán Gómez & Fernando Echevarría Camarero & Pablo Carrasco Ortega, 2023. "Demand–Response Control of Electric Storage Water Heaters Based on Dynamic Electricity Pricing and Comfort Optimization," Energies, MDPI, vol. 16(10), pages 1-25, May.
    5. Wang, Chuyao & Ji, Jie & Yang, Hongxing, 2024. "Day-ahead schedule optimization of household appliances for demand flexibility: Case study on PV/T powered buildings," Energy, Elsevier, vol. 289(C).
    6. Israr Ullah & DoHyeun Kim, 2017. "An Improved Optimization Function for Maximizing User Comfort with Minimum Energy Consumption in Smart Homes," Energies, MDPI, vol. 10(11), pages 1-21, November.

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