E3S Web Conf.
Volume 213, 20202nd International Conference on Applied Chemistry and Industrial Catalysis (ACIC 2020)
|Number of page(s)||5|
|Section||Environmental Chemical Research and Energy-saving Technology Application|
|Published online||01 December 2020|
Research on Ultra-Short-Term Load Forecasting of Distribution Network Based on Fuzzy Clustering and RBF Neural Network
Economic and Technological Research Institute of State Grid Hebei Electric Power Co., Ltd, No. 27, Fuqiang Street, Yuhua District, Shijiazhuang, Heibei Provence, 050022, China
2 North China Electric Power University, Beijing, 102206, China
* Corresponding author: email@example.com
In order to solve the diversification of the load characteristics of the distribution network due to the difference in the electric structure and the electricity consumption habits of users, the calculation accuracy of the forecast model is difficult to meet the actual demand. In this paper, through in-depth study of the characteristics of ultra-short-term load, an ultra-short-term load forecasting model based on fuzzy clustering and RBF neural network (FCM-RBF) is constructed. The model not only considers the historical load characteristics of locally similar days, but also considers the current load characteristics of the forecast days. The load on a locally similar day can well reflect the overall trend of the predicted load; the current load on the forecast day can well reflect the changing law of real-time data during the forecast period and some random factors in the forecast period. Finally, a power grid load in a certain area of southwestern China is selected as an example to verify the effectiveness and accuracy of the proposed method.
© The Authors, published by EDP Sciences, 2020
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