Load Forecasting Based On Soft Computing

Short-term load forecasting (STLF) is very important to the control, operation, and schedule of power system. Deregulation and competition of the power industry are now propelling the utilities to operate the system at an even higher efficiency. This trend further intensifies the concern as to the accuracy of STLF. The paper aims to improve the precision of STLF.In chapter 1, the significance and current situation of main researches in the paper are introduced. The original works completed by the author are also pointed out. In chapter 2, the basic principle of the neural network and immune algorithm is introduced.The main achievements of paper are1. An immune clustering RBF neural network(ICRBFNN) model is presented for STLF. In the design of the ICRBFNN, a novel clustering method based on the symbiotic evolutionary and the immune programming algorithm(SEIPCM) is proposed. The SEIPCM automatically adjust the number and positions of hidden layer RBF centers. The weights of output layer are decided by the recursive least squares algorithm. The proposed ICRBFNN model has been implemented based on the actual data collected from the East China Power Company and compared with the traditional RBF neural network(RBFNN) method. The test results reveal that the ICRBFNN method possesses far superior forecast precision than the RBFNN method.2. A cooperative coevolutionary immune network (CCIN) model is presented for STLF. In the design of neural network, a novel method based on the cooperative coevolutionary and the immune algorithm (CCIM) is proposed. The CCIM is used to evolve the structure and parameters of neural network. The proposed CCIN model has been implemented based on the actual data collected from the Eastern Slovakian Electricity Corporation and compared with the traditional RBF network (RBFN) method. The test results reveal that the CCIN method possesses far superior forecast precision than the RBFN method.3. An immune support vector machines(IWSVM) method is presented for STLF. The immune programming algorithm, inspired by the immune system of human and other mammals, is used to optimize the parameters of support vector machines. The algorithm has the advantage in preventing premature convergence and promoting population diversity. The forecasting results demonstrate that the proposed method has higher forecast precision for STLF.4. According to the speciality of electricity demand development in a city, the grey neural network model GNNM(1,1) is introduced into the field of city electricity demand forecasting. The GNNM(1,1) model is the combination of grey system and neural network, which can solve the complex uncertain problems. The GNNM(1,1) model builds a kind of BP neural network which can map the solution to the grey differential equation of GM(1,1) model, then the model is trained by using BP algorithm. City electricity demand is forecasted after the GNNM(1,1) model is convergent. The forecasting results demonstrate that the GNNM(1,1) model has higher adaptability and forecast precision for city electricity demand forecasting.The main works are summarized at the end of this paper. Further work to the research is pointed out.