Short-term electrical load forecasting using the Support Vector Regression (SVR) model to calculate the demand response baseline for office buildings

• Published in: Applied energy 2017-06, Vol.195, p.659-670
• Main Authors: Chen, Yongbao, Xu, Peng, Chu, Yiyi, Li, Weilin, Wu, Yuntao, Ni, Lizhou, Bao, Yi, Wang, Kun
• Format: Article
• Language: English
• Subjects: Demand respond; Load forecasting; Short-term baseline; SVR model
• ISSN: 0306-2619; 1872-9118
• DOI: 10.1016/j.apenergy.2017.03.034

•A new SVR model to forecast the demand response baseline for office buildings.•Take temperature two hours before DR event can improve the forecasting accuracy.•The forecasting accuracy is better than other seven existing methods in DR programs.•The model is very generic and can be applied to a wide variety of office buildings. Demand Response (DR) aims at improving the operation efficiency of power plants and grids, and it constitutes an effective means of reducing grid risk during peak periods to ensure the safety of power supplies. One key challenge related to DR is the calculation of load baselines. A fair and accurate baseline serves as useful information for resource planners and system operators who wish to implement DR programs. In the meantime, baseline calculation cannot be too complex, and in most cases, only weather data input is permitted. Inspired by the strong non-linear capabilities of Support Vector Regression (SVR), this paper proposes a new SVR forecasting model with the ambient temperature of two hours before DR event as input variables. We use electricity loads for four typical office buildings as sample data to test the method. After analyzing the model prediction results, we find that the SVR model offers a higher degree of prediction accuracy and stability in short-term load forecasting compared to the other seven traditional forecasting models.