A real-time demand-side management system considering user preference with adaptive deep Q learning in home area network
Journal
Sustainable Energy, Grids and Networks
Journal Volume
29
Date Issued
2022
Author(s)
Abstract
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
Subjects
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
SDGs
Type
journal article