Power Load Signal Forecasting by Non-Fixed Fuzzy-Neural Networks

• Main Authors: Lin, Jiun-Hong, 林俊宏
• Other Authors: Hwang Rey-Chue
• Format: Others
• Language: zh-TW
• Published: 1996
• Online Access: http://ndltd.ncl.edu.tw/handle/11704058403411560669

碩士 === 高雄工學院 === 電機電力研究所 === 85 === Due to the very significance of power system load forecasting to electric util ity company, a wide variety of procedures for load forecasting have been propo sed in the last two decades. In recent years, the neural network (NN) technolo gy has been applied widely in this area based on its excellent learning abilit y. In most of these studies, NN structure utilized is fixed that means the NN keeps same size during the training and testing phases. It automatically devel ops an internal non-linear, complex relationship between power load and its in fluencing factors such as weather information through a training process on th e historical data. Then, the trained NN can be used to carry out the forecast no matter the training is appropriate or not. However, the correlations betwee n load and its influencing factors are various, depended very much on geograph ics, seasons, and the behavior of consumption of customers. the improper infor mation will make fixed NN to an ill-learning and cause a poor forecasting.In t his research, the NN structure utilized is non-fixed that means the NN's size keeps changing based on different situations during its learning and testing p rocesses. The correlations of load and its influencing factors on historical d ata are analyzed precisely. The problem of improper influencing is handled bef ore NN's training. Therefore, it makes non-fixed NN develop a more accurate mo del and then carry out a better forecasting. Furthermore, the phenomenon of ov ertraining is always happened in NN's learning with a non-stationary environme nt that makes an ill result of load forecasting . In this research, the modifi cation of learning rate based on fuzzy theory is also investigated. A proper p rocedure how to solve this training problem is proposed. From the point of com mercialization of NN, we hope to make NN technology utilizing in this field ha s a real potential and more promising.