Implementation of General Regression Neural Network into Long- and Middle- Term Electricity Demand Forecasts

• Main Authors: Jung-Yuan Tsai, 蔡蓉媛
• Other Authors: Yun-Shiow Chen
• Format: Others
• Language: zh-TW
• Published: 2001
• Online Access: http://ndltd.ncl.edu.tw/handle/96154424320423038354

碩士 === 元智大學 === 工業工程研究所 === 89 === In this research, we propose a novel approach to forecast long-term and middle-term demand. Traditionally, ARIMA was one of the most effective statistical methods used to forecast the power load in the past. Recently, Back Propagation Network（BPN） is a self-learning method and has performed well in various areas. We implement General Regression Neural Network into electricity demand model and our objective is to forecast the peak load and average load per period. The mean absolute deviation（MAD） is index for the performance evaluation. The factors affected demand considered in the GRNN model are year, month, temperature, personal mean income, index of industrial production, and backward 1-3 terms of load demand. Data was obtained from national official source. There are 165 sets of monthly data in total from 1985 to1999. The comparisons of forecast accuracy among GRNN, BPN and ARIMA are studied. Comparison results show that the mean and variance of MAD for GRNN approach both are smaller than the BPN’s. Meanwhile, BPN takes longer time in estimating such as, hidden layers, learning rate, moment rate, epochs, etc. for which learning speed can be influenced. Result reveals that GRNN can effectively learn the trend of demand and link between data from the historical data. Also performs better than BPN in forecasting both in long-term and middle-term load demand.