Optimization of a photovoltaic-battery system using deep reinforcement learning and load forecasting

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
António Corte Real , G. Pontes Luz , J.M.C. Sousa , M.C. Brito , S.M. Vieira
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引用次数: 0

Abstract

Home Energy Management Systems (HEMS) are increasingly relevant for demand-side management at the residential level by collecting data (energy, weather, electricity prices) and controlling home appliances or storage systems. This control can be performed with classical models that find optimal solutions, with high real-time computational cost, or data-driven approaches, like Reinforcement Learning, that find good and flexible solutions, but depend on the availability of load and generation data and demand high computational resources for training. In this work, a novel HEMS is proposed for the optimization of an electric battery operation in a real, online and data-driven environment that integrates state-of-the-art load forecasting combining CNN and LSTM neural networks to increase the robustness of decisions. Several Reinforcement Learning agents are trained with different algorithms (Double DQN, Dueling DQN, Rainbow and Proximal Policy Optimization) in order to minimize the cost of electricity purchase and to maximize photovoltaic self-consumption for a PV-Battery residential system. Results show that the best Reinforcement Learning agent achieves a 35% reduction in total cost when compared with an optimization-based agent.

Abstract Image

利用深度强化学习和负荷预测优化光伏电池系统
家庭能源管理系统(HEMS)通过收集数据(能源、天气、电价)和控制家用电器或储能系统,在住宅层面的需求侧管理中发挥着越来越重要的作用。这种控制可以采用传统模型,找到最优解,但实时计算成本较高;也可以采用数据驱动方法,如强化学习,找到良好而灵活的解决方案,但这取决于负载和发电数据的可用性,并且需要大量计算资源进行训练。在这项工作中,我们提出了一种新型 HEMS,用于在真实、在线和数据驱动的环境中优化蓄电池的运行,该系统集成了最先进的负荷预测技术,并结合了 CNN 和 LSTM 神经网络,以提高决策的鲁棒性。使用不同的算法(双 DQN、决斗 DQN、彩虹和近端策略优化)对多个强化学习代理进行了训练,以最小化购电成本,最大化光伏电池住宅系统的光伏自消耗。结果表明,与基于优化的代理相比,最佳强化学习代理的总成本降低了 35%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Energy and AI
Energy and AI Engineering-Engineering (miscellaneous)
CiteScore
16.50
自引率
0.00%
发文量
64
审稿时长
56 days
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