https://scholars.lib.ntu.edu.tw/handle/123456789/607420
標題: | A real-time demand-side management system considering user preference with adaptive deep Q learning in home area network | 作者: | Tai C.-S Hong J.-H Hong D.-Y LI-CHEN FU |
關鍵字: | Deep Q Network;Demand-side management;Home area network;Reinforcement learning;Smart grid;User behavior;Automation;Behavioral research;Cost reduction;Deep learning;Demand side management;Digital storage;Electric power transmission networks;Electric utilities;Energy management;Energy utilization;Home networks;Multi agent systems;Smart power grids;Deep Q network;Electricity costs;Energy-consumption;Global energy;Peak to average ratios;Q-learning;Real- time;Research topics;User behaviors;User's preferences | 公開日期: | 2022 | 卷: | 29 | 來源出版物: | Sustainable Energy, Grids and Networks | 摘要: | With the increase in global energy consumption, the demand-side management (DSM) system has grown into an important research topic because of its ability to reduce the total electricity cost and peak-to-average ratio (PAR) by rescheduling loads. Besides, the large amount of sensor data in the home area network (HAN) helps to record the energy demand and achieve the DSM capacity, and the machine learning skills such as reinforcement learning can be applied to solve the DSM problem. However, determining a suitable energy management strategy is complicated because the user behaviors are uncertain. In this study, a real-time multi-agent DSM system based on HAN was proposed to find a suitable control policy for reducing the energy cost in a smart home. This system integrated the Deep Q-Network (DQN) agents that adaptively learn the preference of appliance usage to control different types of appliances and energy storage system. The simulation results show that the proposed DSM system reduced peak value, PAR value, and electricity cost by 28.9%, 20.9%, and 28.6% respectively. This system can also be applied to REDD dataset and achieved 74.9% cost reduction. ? 2021 |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120986195&doi=10.1016%2fj.segan.2021.100572&partnerID=40&md5=49c087e4f5520422372af027e42f7bfc https://scholars.lib.ntu.edu.tw/handle/123456789/607420 |
ISSN: | 23524677 | DOI: | 10.1016/j.segan.2021.100572 |
顯示於: | 資訊工程學系 |
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