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Open AccessArticle

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

1
Department of Electrical and Computer Engineering, The University of Alabama, Tuscaloosa, AL 35487, USA
2
Department of Computer Science, The University of Alabama, Tuscaloosa, AL 35487, USA
*
Author to whom correspondence should be addressed.
Energies 2017, 10(11), 1722; https://doi.org/10.3390/en10111722
Received: 16 September 2017 / Revised: 6 October 2017 / Accepted: 24 October 2017 / Published: 27 October 2017
(This article belongs to the Section Electrical Power and Energy System)
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. View Full-Text
Keywords: electric water heater; energy conservation; thermodynamic modeling; demand-side management; smart homes electric water heater; energy conservation; thermodynamic modeling; demand-side management; smart homes
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Lin, B.; Li, S.; Xiao, Y. Optimal and Learning-Based Demand Response Mechanism for Electric Water Heater System. Energies 2017, 10, 1722.

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