Short-Term Power Load Forecasting Model Based on Fuzzy Neural Network using Improved Decision Tree

Abstract

Aiming at the low accuracy of short-term power load forecasting, Short-Term power Load Forecasting Model Based on Fuzzy Neural Network using Improved Decision Tree is proposed. High accuracy power load forecasting for a specific area, specific date and specific time period in the smart grid environment can be implemented for power dispatching. The short-term power load forecasting model preprocesses the historical data of each region and quantifies each feature, then classifies the training data using DBSCAN clustering algorithm and decision tree algorithm MID3, and finally sends the classified data to the fuzzy neural network for training and prediction. Based on the original fuzzy neural network, the short-term power load forecasting model introduces the decision tree to classify the historical data. The simulation results show that the short-term power load forecasting model improves the prediction accuracy, reduces the relative error, and the model is more effective.