Daily Load Forecasting Based on a Combination of Classification and Regression Tree and Deep Belief Network

Abstract

The next-day load forecasting is complex due to the load pattern variations driven by external factors, such as weather and time. This study proposes a hybrid model that incorporates the Classification and Regression Tree (CART) with pruning conditions and a Deep Belief Network (DBN) to improve forecasting accuracy. The CART can recognize the load patterns by classifying similar groups with low variance, thus reducing the complexity of the forecasting model. The actual 48-period load data from the Electricity Generating Authority of Thailand (EGAT) is used. The proposed model is compared with six widely used standalone forecasting benchmark models and provides better at the minimum 0.46% mean absolute percentage error. Moreover, the forecasting performance of DBN and the other four benchmark models are improved by using our hybrid approach.

Keywords

• Classification and regression tree (CART)
• daily load forecasting
• deep belief network (DBN)
• forecasting accuracy
• pruned-CART