Short-Term Load Forecast of a Single-person Family in Taiwan:Application of Fuzzy Logic Controllers and Artificial Neural Networks
According to the features of short-term power load,the influence of temperature,humidity,weather types,durations of electricity usage,seasonal trends and day types (holiday or workday) are being considered in this study. Nowadays,it is essential to consume electricity more efficiently and to understand customer usage habits. This study is concerned with short-term load forecast (STLF) in a single-person family. This STLF provides a load prediction designed to help people save on their electricity bills. Since the precise load forecasting plays an important role in reducing unnecessary consumption and in arranging the dispatch of electric appliance. A fuzzy neural network approach that combines the neural model and a fuzzy logic controller for short-term load forecast is presented. A fuzzy logic controller enhances the performance of the system as well as making the forecast more accurate considering the realistic conditions. Lastly,the method is simulated using MATLAB and Super PCNeuron,taking the load data from a single-person family in Tainans Yongkang City and the weather data from the Central Weather Bureau as an example. The results showed that the proposed fuzzy neural system provides good predictive ability as claimed.
short-term load forecast (STLF) fuzzy logic controller artificial neutral networks
Jyh-Yih Hsu Chih-Ching Chen Jr-Chung Wu Jiunn-Lin Wu
Department of Management Information Systems National Chung Hsing University,No.250,Kuo-Kuang Road T Department of Computer Science and Engineering,National Chung Hsing University,No. 250,Kuo-Kuang Roa Institute of Networking and Multimedia,National Chung Hsing University,No. 250,Kuo-Kuang Road Taichu
国际会议
西安
英文
684-690
2011-12-23（万方平台首次上网日期，不代表论文的发表时间）