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Article

Adaptive Optimization Design of Building Energy System for Smart Elderly Care Community Based on Deep Deterministic Policy Gradient

School of Economics and Management, Huizhou University, Huizhou 516007, China
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Processes 2023, 11(7), 2155; https://doi.org/10.3390/pr11072155
Submission received: 25 May 2023 / Revised: 6 July 2023 / Accepted: 14 July 2023 / Published: 19 July 2023

Abstract

In smart elderly care communities, optimizing the design of building energy systems is crucial for improving the quality of life and health of the elderly. This study pioneers an innovative adaptive optimization design methodology for building energy systems by harnessing the cutting-edge capabilities of deep reinforcement learning. This avant-garde method initially involves modeling a myriad of energy equipment embedded within the energy ecosystem of smart elderly care community buildings, thereby extracting their energy computation formulae. In a groundbreaking progression, this study ingeniously employs the actor–critic (AC) algorithm to refine the deep deterministic policy gradient (DDPG) algorithm. The enhanced DDPG algorithm is then adeptly wielded to perform adaptive optimization of the operational states within the energy system of a smart retirement community building, signifying a trailblazing approach in this realm. Simulation experiments indicate that the proposed method has better stability and convergence compared to traditional deep Q-learning algorithms. When the environmental interaction coefficient and learning ratio is 4, the improved DDPG algorithm under the AC framework can converge after 60 iterations. The stable reward value in the convergence state is −996. When the scheduling cycle of the energy system is between 0:00 and 8:00, the photovoltaic output of the system optimized by the DDPG algorithm is 0. The wind power output fluctuates within 50 kW. This study realizes efficient operation, energy saving, and emission reduction in building energy systems in smart elderly care communities and provides new ideas and methods for research in this field. It also provides an important reference for the design and operation of building energy systems in smart elderly care communities.
Keywords: deep learning; smart community; architecture; energy system; adaptive; energy conservation deep learning; smart community; architecture; energy system; adaptive; energy conservation

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MDPI and ACS Style

Liu, C.; Xue, Z. Adaptive Optimization Design of Building Energy System for Smart Elderly Care Community Based on Deep Deterministic Policy Gradient. Processes 2023, 11, 2155. https://doi.org/10.3390/pr11072155

AMA Style

Liu C, Xue Z. Adaptive Optimization Design of Building Energy System for Smart Elderly Care Community Based on Deep Deterministic Policy Gradient. Processes. 2023; 11(7):2155. https://doi.org/10.3390/pr11072155

Chicago/Turabian Style

Liu, Chunmei, and Zhe Xue. 2023. "Adaptive Optimization Design of Building Energy System for Smart Elderly Care Community Based on Deep Deterministic Policy Gradient" Processes 11, no. 7: 2155. https://doi.org/10.3390/pr11072155

APA Style

Liu, C., & Xue, Z. (2023). Adaptive Optimization Design of Building Energy System for Smart Elderly Care Community Based on Deep Deterministic Policy Gradient. Processes, 11(7), 2155. https://doi.org/10.3390/pr11072155

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