Abstract
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.
Highlights
At present, there are many methods of load forecasting
The results show that in load forecasting, the radial basis function neural network (RBF) network has higher accuracy than the BP network
2.Load forecasting model based on fuzzy clustering and RBF neural network
Summary
There are many methods of load forecasting. Compared with traditional methods, there are regression analysis methods, time series method, line extrapolation method, gray forecasting, etc. [3] uses a radial basis function neural network (RBF) for short-term load forecasting research. This paper uses the method of combining fuzzy clustering and RBF neural network to perform ultra-short-term load forecasting on the distribution network and verifies that the method can effectively improve the load forecasting accuracy through an example.
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