Prediction model of improved artificial neural network and its application
Xiaohua Song · Journal of Central South University(Science and Technology) · 2008
By adopting the spike identification mechanism to improve the traditional BP algorithm on the ability of spike prediction,a BP algorithm with spike identification(SIBP) was proposed.The multi-orientation searching-mechanism was introduced into the particle swarm optimization(PSO) for enhancing the global optimizing-ability,and the improved PSO was combined with the SIBP to avoid the problem of local extreme.A new forecasting method was proposed with stronger learning ability and spike identification.The improved predicting model was applied in the forecasting of market cleaning price(MCP) which fluctuated acutely.Based on the actual date of American PJM power market from 2005-02-01 to 2005-05-16,the new artificial neural network was inspected by comparison with the other different methods.The results indicate that,compared with BP,using the MCP spike prediction model,the forecast accuracy improves by 10.16%,and the time cost of the new method is only increased by 6.2 s and the MCP spike prediction model is effective to solve the prediction problems of peak values.