Reinforcement Learning Based Energy Management Algorithm for Smart Energy Buildings

Kim, Sunyong; Lim, Hyuk

doi:10.3390/en11082010

Cited by 114 publications

(99 citation statements)

References 24 publications

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“…It was found that the proposed approach provided near optimum performance when compared to a theoretically optimal benchmark. In 2018, Kim and Lim utilized Q learning to learn when to charge and discharge a battery and when to buy and sell from the grid [100]. The authors report significant energy cost savings when compared to other methods.…”

Section: Smart Homes and The Electrical Gridmentioning

confidence: 99%

A review of reinforcement learning for autonomous building energy management

Mason

Grijalva

2019

Computers & Electrical Engineering

213

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The area of building energy management has received a significant amount of interest in recent years. This area is concerned with combining advancements in sensor technologies, communications and advanced control algorithms to optimize energy utilization. Reinforcement learning is one of the most prominent machine learning algorithms used for control problems and has had many successful applications in the area of building energy management. This research gives a comprehensive review of the literature relating to the application of reinforcement learning to developing autonomous building energy management systems. The main direction for future research and challenges in reinforcement learning are also outlined.

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Section: Smart Homes and The Electrical Gridmentioning

confidence: 99%

A review of reinforcement learning for autonomous building energy management

Mason

Grijalva

2019

Computers & Electrical Engineering

213

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“…Finally, it is claimed that the environmental inertia must be deeply analysed in order to understand the time response of the environment and correctly compute its effect in the learning analysis. Concerning the energy efficiency of next generation buildings, the papers of Yang et al [45] and Kim and Lim [46] give details on how the power consumption can be reduced utilising the Q-learning algorithm. Both papers provide details on how the state space is constructed and employ a stochastic framework for the problem formulation.…”

Section: Applications Of Reinforced Learning To Energy Systemsmentioning

confidence: 99%

Analysis and Adaptation of Q-Learning Algorithm to Expert Controls of a Solar Domestic Hot Water System

Bettoni

Soppelsa

Fedrizzi

et al. 2019

ASI

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This paper discusses the development of a coupled Q-learning/fuzzy control algorithm to be applied to the control of solar domestic hot water systems. The controller brings the benefit of showing performance in line with the best reference controllers without the need for devoting time to modelling and simulations to tune its parameters before deployment. The performance of the proposed control algorithm was analysed in detail concerning the input membership function defining the fuzzy controller. The algorithm was compared to four standard reference control cases using three performance figures: the seasonal performance factor of the solar collectors, the seasonal performance factor of the system and the number of on/off cycles of the primary circulator. The work shows that the reinforced learning controller can find the best performing fuzzy controller within a family of controllers. It also shows how to increase the speed of the learning process by loading the controller with partial pre-existing information. The new controller performed significantly better than the best reference case with regard to the collectors’ performance factor (between 15% and 115%), and at the same time, to the number of on/off cycles of the primary circulator (1.2 per day down from 30 per day). Regarding the domestic hot water performance factor, the new controller performed about 11% worse than the best reference controller but greatly improved its on/off cycle figure (425 from 11,046). The decrease in performance was due to the choice of reward function, which was not selected for that purpose and it was blind to some of the factors influencing the system performance factor.

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“…For instance, Brida et al [18] developed a battery energy management strategy for a MG by using the batch RL technology. Sunyong et al [19] proposed a RL-based EMS for a MG-like smart building to reduce the operating cost. Ganesh et al [20] proposed an evolutionary adaptive dynamic programming and RL framework for dynamic energy management of a smart MG. Elham et al [21] designed a multiagent-based RL system for optimal distributed energy management in a MG.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Energy Management of a Microgrid Using Deep Reinforcement Learning

Wang

et al. 2019

Energies

171

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Driven by the recent advances and applications of smart-grid technologies, our electric power grid is undergoing radical modernization. Microgrid (MG) plays an important role in the course of modernization by providing a flexible way to integrate distributed renewable energy resources (RES) into the power grid. However, distributed RES, such as solar and wind, can be highly intermittent and stochastic. These uncertain resources combined with load demand result in random variations in both the supply and the demand sides, which make it difficult to effectively operate a MG. Focusing on this problem, this paper proposed a novel energy management approach for real-time scheduling of an MG considering the uncertainty of the load demand, renewable energy, and electricity price. Unlike the conventional model-based approaches requiring a predictor to estimate the uncertainty, the proposed solution is learning-based and does not require an explicit model of the uncertainty. Specifically, the MG energy management is modeled as a Markov Decision Process (MDP) with an objective of minimizing the daily operating cost. A deep reinforcement learning (DRL) approach is developed to solve the MDP. In the DRL approach, a deep feedforward neural network is designed to approximate the optimal action-value function, and the deep Q-network (DQN) algorithm is used to train the neural network. The proposed approach takes the state of the MG as inputs, and outputs directly the real-time generation schedules. Finally, using real power-grid data from the California Independent System Operator (CAISO), case studies are carried out to demonstrate the effectiveness of the proposed approach.

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Reinforcement Learning Based Energy Management Algorithm for Smart Energy Buildings

Cited by 114 publications

References 24 publications

A review of reinforcement learning for autonomous building energy management

A review of reinforcement learning for autonomous building energy management

Analysis and Adaptation of Q-Learning Algorithm to Expert Controls of a Solar Domestic Hot Water System

Real-Time Energy Management of a Microgrid Using Deep Reinforcement Learning

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