贫乏信息下基于深度迁移学习的智慧建筑负荷预测方法

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Abstract Accurate load forecasting can significantly optimize the operation strategy of equipment and release the energy-saving potential of buildings. With the advancement of computer science and smart meters, data-driven load forecasting models have become popular because of good forecasting accuracy. To improve the forecasting performance under poor information, this paper proposes a deep-transfer-learning predictive method based on the causal convolutional neural network. Taking three office buildings of the same type as an example, one of them is set as the target building and the other two are set as the source building. The proposed method and the built model are verified, and the forecasting results are compared with the long short-term memory network model. The forecasting results show that the model built in this paper reduces the average percentage error and the root mean square error coefficient of the predicted value and the actual value by 22.81% and 38.85%, respectively. Finally, this paper selects a building with better forecasting accuracy and compares and analyzes its annual cold, heat, and electrical load forecast results. The results show that the forecast accuracy of electricity and heat load is better than that of cooling load.

Key words： load forecasting transfer learning deep learning convolutional neural network (CNN)

Received: 29 September 2021

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	Articles by authors
	JIANG Jingjing
	DOU Zhenlan
	YANG Haitao
	ZHAO Min

Cite this article:

JIANG Jingjing,DOU Zhenlan,YANG Haitao等. Load forecasting method of smart building based on deep transfer learning under poor information[J]. Electrical Engineering, 2022, 23(5): 55-61.

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http://dqjs.cesmedia.cn/EN/Y2022/V23/I5/55

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