Technical Note: \cal Q-Learning
Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Electronic Commerce Research
A deployed multi-agent framework for distributed energy applications
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Clustering Distributed Energy Resources for Large-Scale Demand Management
SASO '07 Proceedings of the First International Conference on Self-Adaptive and Self-Organizing Systems
A Simulator for Self-Adaptive Energy Demand Management
SASO '08 Proceedings of the 2008 Second IEEE International Conference on Self-Adaptive and Self-Organizing Systems
Adaptive Control of Distributed Energy Management: A Comparative Study
SASO '08 Proceedings of the 2008 Second IEEE International Conference on Self-Adaptive and Self-Organizing Systems
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Agent-based distributed energy management
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
Evolutionary optimisation of distributed energy resources
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
A survey of multi-objective sequential decision-making
Journal of Artificial Intelligence Research
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In order to cope with the unpredictability of the energy market and provide rapid response when supply is strained by demand, an emerging technology, called energy demand management, enables appliances to manage and defer their electricity consumption when price soars. Initial experiments with our multi-agent, power load management simulator, showed a marked reduction in energy consumption when price-based constraints were imposed on the system. However, these results also revealed an unforeseen, negative effect: that reducing consumption for a bounded time interval decreases system stability. The reason is that price-driven control synchronizes the energy consumption of individual agents. Hence price, alone, is an insufficient measure to define global goals in a power load management system. In this article we explore the effectiveness of a multi-objective, system-level goal which combines both price and system stability. We apply the commonly known reinforcement learning framework, enabling the energy distribution system to be both cost saving and stable.