Decomposition-Accumulation Principle-Based Monthly Electricity Consumption Forecasting Approach Using EMD-XGBoost Hybrid Model

Accurate monthly electricity consumption forecasting (ECF) is very important for the profitability of electricity retailers in the competitive electricity market. High resolution historical data is rarely used in monthly ECF since it often requires to be combined with multi-step iterative forecasting methods, which will lead to the accumulation of errors and the decrease in forecasting accuracy. This paper proposes a decomposition-accumulation principle-based monthly ECF approach using EMD-XGBoost hybrid model to address the above issues. First, the historical electricity consumption data is divided into seven parts with week label (the day of the week). Then, each part is forecasted separately using the EMD-XGBoost hybrid model. Finally, the monthly ECF result is the accumulation of the seven partial forecasting results. The proposed forecasting approach can reduce the steps of iteration and consider the calendar effects meanwhile the EMD-XGBoost hybrid model can mine data information of various parts in depth without additional influence factors. The case study using real data from PJM demonstrates the effectiveness and superiority of the proposed approach.