A hybrid prediction model for residential electricity consumption using holt-winters and extreme learning machine. (1st October 2020)

• Record Type: Journal Article
• Authors: Liu, Che; Sun, Bo; Zhang, Chenghui; Li, Fan
• Is Part Of: Applied energy. Volume 275(2020)
• Publication Date: 2020-10-01
• Subjects: Residential energy consumption -- Machine learning -- Holt-winters -- Extreme learning machine -- Electric load prediction -- Ultra-short-term prediction; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2020.115383
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: A novel hybrid model is proposed for residential electricity consumption forecasting. The model is applied to residential electricity consumption forecasting under 15-min resolution. The model can be developed using a small training set while ensuring high precision. The training time of the model is the shortest in comparison with all the relevant models. Abstract: Residential electricity consumption accounts for a large proportion of the primary energy consumption in China. Building energy management can effectively improve energy efficiency, where the accurate and reliable prediction results of residential electricity consumption are vital for the optimal management of building energy. In this paper, a hybrid model is developed for the ultra-short-term predictions of residential electricity consumption based on the Holt-Winters (HW) method and Extreme Learning Machine (ELM) network. The original data are decomposed into a stationary linear component and a fluctuant nonlinear residual using the Moving Average (MA) filter. The HW method is responsible for establishing the linear prediction model to forecasting the linear component. With the linear prediction results, nonlinear residual, and original data as inputs, the ELM could build a nonlinear prediction model for residential electricity consumption. The proposed HW-ELM model was used to predict 15-minute electricity consumption values in different training set sizes and seasons. When predicting residential electricity consumption, in comparison with HW, ELM, and long short-term memory network, the proposed model commonly demonstrated lower error. For a training set size of 50 days in spring, the root mean square error values were reduced by 87.98%, 64.89%, and 53.39%, respectively. The results of the experiments show that the proposed HW-ELM model offers more outstanding performance compared with the established models by the other methods.