Long term electric load forecasting based on TS-type recurrent fuzzy neural network model

It is of great demand in the power industry to use hourly information to establish the appropriate predictive model and improve the forecasting accuracy in the short/long term load forecasting processes. In this paper, we propose a new load forecasting approach to provide a more accurate and defensible forecast on the long term load forecasting process. The proposed method consists of two stages: weather station selection stage and Takagi-Sugeno (TS) fuzzy model based prediction stage. In the first stage, a new weather station selection method is developed, in which the most relevant 3 weather stations are combined to generate 5 temperature series for each zone and the temperature series with the least mean absolute percentage error (MAPE) is chosen as the weather station information used to forecast. At the second stage, an improved self-organizing radial basis function recurrent neural network with TS fuzzy model (ISO-TS-RBF-RFNN) is exploited in view of the conditions such as the appearing uncertainties of holiday feature component in different years, the violation of Gaussian distribution on feature component and the temperature measurement divergence. In the proposed ISO-TS-RBF-RFNN model, a computing method on current firing strength of the fuzzy modes is developed by using a novel type of activation mechanism and robust-type fuzzy rules to improve the robustness of the predictive model. Finally, we use the same partition in the dataset from GEFCom2012 to train and demonstrate the application of the proposed model. Compared with the other five models i.e., MLR, SVR, FIR, GA-LSTM and traditional SO-TS-RBF-RFNN, the proposed model has higher forecasting accuracy than others under four evaluation criteria.