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Energy management of buildings with energy storage and solar photovoltaic:A diversity in experience approach for deep reinforcement learning agents

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Energy and AI 2024年第1期15卷 1-14页

作者： Akhtar Hussain Petr Musilek Department of Electrical and Computer Engineering University of AlbertaEdmontonT6G 2G2ABCanada Department of Applied Cybernetics University of Hradec Králové50003Hradec KrálovéCzech Republic

Deep reinforcement learning(DRL)is a suitable approach to handle uncertainty in managing the energy consumption of buildings with energy storage ***,DRL agents are trained by randomly selecting samples from a data set,which can result in overexposure to some data categories and under/no exposure to other data ***,the trained model may be biased towards some data groups and underperform(provide suboptimal results)for data groups to which it was less *** address this issue,diversity in experience-based DRL agent training framework is proposed in this *** approach ensures the exposure of agents to all types of *** proposed framework is implemented in two *** the first step,raw data are grouped into different clusters using the K-means clustering *** clustered data is then arranged by stacking the data of one cluster on top of *** the second step,a selection algorithm is proposed to select data from each cluster to train the DRL *** frequency of selection from each cluster is in proportion to the number of data points in that cluster and therefore named the proportional selection *** analyze the performance of the proposed approach and compare the results with the conventional random selection method,two indices are proposed in this study:the flatness index and the divergence *** model is trained using different data sets(1-year,3-year,and 5-year)and also with the inclusion of solar *** simulation results confirmed the superior performance of the proposed approach to flatten the building’s load curve by optimally operating the energy storage system.

关键词： Battery energy storage building demand management Deep reinforcement learning Diversity in experience Energy management

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