Load forecasting based on knowledge flow and transfer learning - Wenjun ZHU; Sining WANG; Xiaoxin GAO; Qian ZHENG (Dianxin kexue)
In all things connected, comprehensive perception, intelligent decision-making information era of big data, in the information acquisition of big data and a large amount of signal processing, there are still large amount of data redundancy, calculation and the shortcoming of high cost, not in time, and no marked.The transfer learning was applied, and the knowledge flow system based on the weight impact factor for information fusion was integrated to assist the analysis and simplify the calculation for the IoT sensing system.The knowledge flow mode of transfer learning and data fusion was adopted in the IoT sensing system, and the simulation calculation of short-term load prediction was made based on the partial data of regional power consumption.The influencing factors of users’ electricity consumption behavior were analyzed, and the optimal weight distribution of influencing factors was obtained through training, so as to predict the power consumption rate.The results show that in this way, it can clearly identify the characteristics of electricity consumption behavior, and predict the energy consumption according to the characteristics of electricity consumption.