Short-term load forecasting method based on fuzzy optimization combined model of load feature recognition

With the continuous development of smart grid construction and the gradual improvement of power market operation mechanisms, the importance of power load forecasting is continually increasing. In this study, a short-term load prediction method based on the fuzzy optimization combined model of load feature recognition was designed to address the problems of weak generalization ability and poor prediction accuracy of the conventional feedforward neural network prediction model. First, the Douglas-Peucker algorithm and fuzzy optimization theory of load feature recognition were analyzed, and the combined prediction model was constructed. Second, data analysis and pre-processing were performed based on the actual historical load data of a certain area and the corresponding meteorological and calendar rule information data. Finally, a practical example was used to test and analyze the short-term load forecasting effect of the fuzzy optimization combined model. The calculation results proved that the presented fuzzy optimization combined model of load feature recognition outperformed the conventional model in terms of computational efficiency and specific performance; therefore, the proposed model supports further development of actual power load prediction.