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A self-learning scheme for residential energy system control and management

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Abstract

In this paper, we apply intelligent optimization method to the challenge of intelligent price-responsive management of residential energy use, with an emphasis on home battery use connected to the power grid. For this purpose, a self-learning scheme that can learn from the user demand and the environment is developed for the residential energy system control and management. The idea is built upon a self-learning architecture with only a single critic neural network instead of the action-critic dual network architecture of typical adaptive dynamic programming. The single critic design eliminates the iterative training loops between the action and the critic networks and greatly simplifies the training process. The advantage of the proposed control scheme is its ability to effectively improve the performance as it learns and gains more experience in real-time operations under uncertain changes of the environment. Therefore, the scheme has the adaptability to obtain the optimal control strategy for different users based on the demand and system configuration. Simulation results demonstrate that the proposed scheme can financially benefit the residential customers with the minimum electricity cost.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, IL, 60607, USA

    Ting Huang & Derong Liu

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  1. Ting Huang
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  2. Derong Liu
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Corresponding author

Correspondence to Derong Liu.

Additional information

This work was supported by the National Science Foundation under Grant ECCS-1027602.

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Huang, T., Liu, D. A self-learning scheme for residential energy system control and management. Neural Comput & Applic 22, 259–269 (2013). https://doi.org/10.1007/s00521-011-0711-6

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  • DOI: https://doi.org/10.1007/s00521-011-0711-6

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