A real-time demand-side management system considering user behavior using deep q-learning in home area network
Journal
Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
Journal Volume
2019-October
Pages
4050-4055
Date Issued
2019
Author(s)
Abstract
In smart grids, demand-side management (DSM) has become an important topic since it can reduce the total electricity cost by smart control and rescheduling of loads, meanwhile, reduce the peak-to-average ratio (PAR) under real-time pricing policy. On the other hand, with the growth of computation ability in recent years and the huge amount of data collected in home area network (HAN), machine learning skills such as reinforcement learning can be well applied into the DSM problem. However, it is hard to determine an optimal energy management strategy since the uncertainty of user behavior and the electricity consumption. In the proposed work, a real-time multi-agent deep reinforcement learning based approach has been proposed to solve the DSM problem in HAN and additionally considers the user behavior to avoid disturbing user comfort, meanwhile, adaptively learns the appliance usage preference and updates the system after each day. The simulation results reveal that the proposed DSM system has improved the energy efficiency in a smart home that not only reduces the electricity cost and peak value but also the PAR value. ? 2019 IEEE.
Subjects
Automation; Behavioral research; Cost reduction; Deep learning; Demand side management; Electric power transmission networks; Electric utilities; Energy efficiency; Energy management; Learning algorithms; Machine learning; Multi agent systems; Reinforcement learning; Smart power grids; Computation ability; Electricity costs; Electricity-consumption; Home area network; Home Area Network (HAN); Peak to average ratios; Smart grid; User behaviors; Home networks
SDGs
Other Subjects
Automation; Behavioral research; Cost reduction; Deep learning; Demand side management; Electric power transmission networks; Electric utilities; Energy efficiency; Energy management; Learning algorithms; Machine learning; Multi agent systems; Reinforcement learning; Smart power grids; Computation ability; Electricity costs; Electricity-consumption; Home area network; Home Area Network (HAN); Peak to average ratios; Smart grid; User behaviors; Home networks
Type
conference paper