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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.

The randomness and volatility of wind power generation are the main reasons restricting the capacity of power grid to absorb wind power. Thermal power units are the main force of power grid frequency modulation. The speed of load adjustment rate determines their response ability to power grid load. Based on the analysis of the characteristics of wind power generation, the nonlinear multiscale decomposition of the automatic power generation control (AGC) load command is carried out. Combined with the different load regulation rates of different types of units, the rational allocation of unit combinations can effectively ensure the power grid load regulation capacity and compensate the random disturbance of wind power.

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