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...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.展开更多
基金funded by the Portuguese Portuguese Fundação para a Ciência e a Tecnologia(FCT)I.P./MCTES through IDMEC under LAETA,Project:UIDB/50022/2020(https://doi.org/10.54499/UIDB/50022/2020),UIDP/50022/2020(https://doi.org/10.54499/UIDP/50022/2020)and through national funds(PIDDAC),through IDL–UIDB/50019/2020 https://doi.org/10.54499/UIDB/50019/2020),UIDP/50019/2020(https://doi.org/10.54499/UIDP/50019/2020)and LA/P/0068/2020(https://doi.org/10.54499/LA/P/0068/2020)and the FCT Studentship 2021.06299.BD(G Pontes Luz)。
文摘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.
