Changing Energy Consumption Patterns Based on Multi-Agent Human Behavior Modeling for Analyzing the Effects of Feedback Techniques

With the deployment of smart grid technology and Advanced Metering Infrastructure, demand side management via feedback is a subject of interest to many researchers. Home area devices, such as in-home displays and smart appliances, are being developed and implemented to achieve real-time feedback. Analyses on the effects of real-time feedback show improvements in energy conservation, but the improvements are not as expected. This is due to the fact that altering human behavior is not an easy task, and not all react in the same way to a similar feedback. Modeling consumer behavior helps to understand their reactions toward different types of feedback, and hence allows for the categorization of the feedback and its relevance to a specific consumer category. This research focuses on identifying different consumer behavioral categories and feedback sets for each behavioral category. The goal is to analyze the behavior changes by using a Multi-Agent System (MAS), which models human behaviors and their usage patterns, in order to eventually improve energy consumption and reduce the overall demand and peak demand. Our MAS model helps to quantify the reduction of energy consumption as a result of real-time feedback.