Study on the daily characteristic load forecasting based on the optimizied algorithm of decision tree

To eliminate limitation of the ID3 algorithm, an optimized algorithm for two-level information gain of attribute-value pairs is presented to establish the forecasting model of daily--characteristic-load decision tree. The algorithm has improved primitive ID3 algorithm in many aspects. It can prevent expansion biasing the attribute which has multi values. By this improved algorithm, the relationship of attributes can be considered well. Through setting threshold value sensitization of noise can be reduced. Daily characteristic load forecasting can be implemented by this model which associates day-forecasted information such as weather, week and so on. The analytic method of histogram is adopted to discretize the data of the load rate-of-change and the data of weather combined hierarchical clustering and discretization based on entropy; after the data is pre-processed, the forecasting model of load decision tree is established by the optimized algorithm for two-level information gain of attribute-value pairs and the characteristic load can be forecasted by giving the information of date-forecasted weather and week. The forecasting results meet even exceed the requirements of utility and demonstrate high-accuracy of the proposed model. If use the 24 or 96 load and its corresponding influent factors to train, 24 or 96 forecasting models will be formed. Then 24 or 96 load can be forecasted by these models.