Research On Short-Term Electricity Load Forecasting Based On Optimized Decision Tree

Short-term load forecasting is an important and challengeable work. In order to accurately forecast the loads of power system, this article presents a new short-term load forecasting method based on optimized decision tree, which efficiently takes the non-load factors'influences into account. Portrait and transverse comparability are employed to distinguish and correct bad load data. Rough set is used to optimize the testing attributes of decision tree by reducing the non-load factors. Then to eliminate limitation of the ID3 algorithm, an optimized algorithm MBSID3 is presented to select the testing attributes. And the pruning method is used to reduce the complexity. After three optimization, the established short-term load forecasting model can categorize much better and has lesser scale. Good test results using actual data demonstrate that this method could improve the accuracy of short-term load forecasting effectively, and it has practicability and superiority.