A novel combined model based on hybrid optimization algorithm for electrical load forecasting - Wang, Rui; Wang, Jiyang; Xu, Yunzhen (Applied soft computing, 2019)
Accurate electrical load forecasting always plays a vital role in power system administration and energy dispatch, which are the foundation of the smooth operation of the national economy and people’s daily life. Thinking from this vision, many scholars have made great efforts to seek suitable optimization algorithms to improve the performance of existing forecasting algorithm. However, most of the studies ignore the inherent disadvantages of single optimization algorithm, which leads to sub-optimal forecasting performance. Therefore, a novel electric load forecasting system was successfully proposed in this paper by the combination of data preprocessing, hybrid optimization algorithms, and several single classical forecasting methods, which successfully overcomes the defects of single traditional forecasting models and achieves higher forecasting accuracy than that of single model optimization. Besides, the 30 min interval data of Queensland, Australia from March to April is used as illustrative examples to evaluate the performance of the developed model. The results of tests demonstrate that the proposed hybrid model can better approximate the actual value, and it can also be employed as a useful tool for smart grids dispatching planning. •Develop a novel power load forecasting system for grid safety dispatch.•Extract important information effectively from the original load time series.•Creatively propose two hybrid optimization methods to optimize forecasting models.•Set up a comprehensive evaluation system to verify the effectiveness of the models.