Power load combination forecasting system based on longitudinal data selection

Abstract

In the current situation of energy supply shortage and surging demand, effective and stable load forecasting is essential to ensure reliable power supply and the security of the power system. However, due to some factors such as periodicity and seasonality, the power load sequence shows complex nonlinear characteristics. Meanwhile, the current load forecasting lacks the ability to explore the data deeply, and it is difficult to accurately predict the short-term trend and fluctuation range. To remedy these limitations, this study proposes a hybrid point-interval prediction system (HPILS). The system integrates data preprocessing, optimal model selection, multi-objective optimization combination and interval prediction modules. To verify the performance of the proposed system, four load data sets in Australia are used as examples to conduct experiments. The experimental results demonstrate that HPILS can effectively provide the predicting power load trend changes and fluctuation ranges. Specifically, compared with the benchmark model, HPILS has a 13.47% ∼ 67.89% improvement in point prediction and a 1.67% ∼ 72.08% improvement in interval prediction. In addition, a series of discussion tests are performed to verify the superiority of the proposed system and further confirm the validity of our proposed system.