基于知识流和迁移学习的负荷预测

doi:10.11959/j.issn.1000-0801.2022074

Abstract

Abstract:

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.

Key words: transfer learning, information fusion, knowledge flow, IoT perception, load prediction

CLC Number:

Wenjun ZHU, Sining WANG, Xiaoxin GAO, Qian ZHENG. Load forecasting based on knowledge flow and transfer learning[J]. Telecommunications Science, 2022, 38(5): 114-123.

Figures/Tables 10

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	学习方式	D _S＆D_T	T _S＆T_T
	传统机器学习	相同	相同
迁移学习	归纳式迁移学习	相同/相关	相关
	无监督迁移学习	相关	相关
	直推式迁移学习	相关	相同

分量	影响因子
IMF1/IMF2/IMF3	温度、多个规律性因子的共同制约等
IMF4/IMF5/IMF6/IMF7	规律性因子制约

评估参数	特征权重数值融合前	特征权重数值融合后
MAPE	8.04	10.60
MASE	0.99	1.41

Load forecasting based on knowledge flow and transfer learning

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