Hierarchical Electricity Demand Forecasting by Exploring the Electricity Consumption Patterns

Accurate electricity demand forecasting is necessary to develop an efficient and sustainable power system. Total demand of the whole region can be disaggregated at different levels, thus producing a hierarchical structure. In the hierarchical demand forecasting, the prediction accuracy and aggregate consistency between levels are two important issues, however in the previous works the prediction accuracy is often affected by conducting the aggregate consistency. In this work, we propose a novel pattern-based hierarchical time series forecasting (PHF) method which consists of two aggregation stages. In the first aggregation stage, by exploring the electricity consuming patterns with clustering method, the bottom level electricity demand forecasting is improved, and in the second stage the region level aggregation is conducted to achieve the whole level forecasting. The experiments are conducted on the Energy Demand Research Project (EDRP) datasets, and the experimental results show that compared with the previous state-of-the-art methods, our method improves the prediction accuracy in all hierarchical levels with keeping aggregation consistency.