BP neural network with rough set for short term load forecasting
Expert Systems with Applications: An International Journal
Power load forecasting using support vector machine and ant colony optimization
Expert Systems with Applications: An International Journal
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In order to short-term load forecasting (STLF), two different seasonal artificial neural networks (ANNs) are designed and compared with conventional regression. Furthermore designed ANNs are compared with each other in terms of model complexity, robustness and forecasting accurate to make more accurate short-term load forecasting in electricity market of Iran. The first model is a daily forecasting model which is used for forecasting the hourly load of the next day, and the second model is comprised of 24 sub-networks which are used for forecasting the hourly load of the next day. In fact, the second model is partitioning the first model. Time, temperature, and historical loads are taken as inputs. Results show a good conformity between actual data and ANNs outcome. In comparison between ANNs results and regression's, even thought in some cases regression shows less MAPE, the total MAPE of ANN is proved to be less than regression's. Moreover, it is founded that the first designed model consisting of single ANN is more appropriate than the second model consisting of 24 ANNs.