A novel hybrid model based on combined preprocessing method and advanced optimization algorithm for power load forecasting

Abstract

Short-term power load forecasting occupies an important position in improving the operating efficiency and economic effects of power system. Aiming at improving forecast performance, a substantial number of load forecasting models are proposed. However, most of the previous studies ignored the limitations of individual prediction models and the necessity of data preprocessing, resulting in low forecast accuracy. In this study, a novel hybrid model which combines data preprocessing technology, individual forecasting algorithm and weight determination theory is successfully presented for obtaining higher accuracy and better forecasting ability. Among this model, the data preprocessing stage first uses a novel combinationdata preprocessingmethod, which overcomes the shortcomings of single preprocessing methods. In addition, a combined forecasting mechanism composed of RBF, GRNN and ELM is proposed using the weight determination theory, which exceeds the limits of individual prediction models and improves prediction accuracy. For the sake of assessing the availability of the proposed hybrid model, three datasets of half-hour power load of Queensland, South Australia and Victoria in Australia are selected in this study. The final experimental results show that the proposed model not only can approximate the actual power load very well, but also can be used as a helpful tool for power grid planning and dispatching.