Title: The Impact of Energy Feedback on Occupant Energy Conservation Behavior: an Agent-Based Approach

Authors: Mohammad Zareie , Amirhossein Hosseini Sarcheshmeh , Mojtaba Maghrebi

Abstract:

Purpose: The purpose of this study is to enhance the effectiveness of energy-saving awareness events by identifying optimal participant selection methods. Given the global and national importance of reducing energy consumption, the research investigates how residents’ behavioral characteristics—such as energy consumption level, behavioral adaptability, and social influence—affect the overall impact of awareness events. The study develops an agent-based simulation model to analyze the long-term effects of different participant selection strategies on community-wide energy consumption patterns.
Design/methodology/approach: This research employs an agent-based modeling (ABM) approach to simulate dynamic interactions among residents in a social network. The model integrates mathematical frameworks for opinion diffusion and behavioral change to represent how individuals’ energy consumption patterns evolve following an awareness event and through peer interactions. A synthetic community of agents is created, each defined by three attributes: the Energy Consumption Index (ECI), Susceptibility Index (SI), and Social Connections (SC). Several event participation selection methods are evaluated, including (S1) random selection, (S2) selection based on behavioral adaptability, (S3) selection based on social influence, (S4) selection based on energy consumption level, and (S5) a combined method integrating all three criteria. The model is implemented in Python and simulated across communities of varying sizes (100, 1,000, and 10,000 agents) to evaluate scalability and generalizability.
Findings: Simulation results reveal that the method of participant selection significantly influences the medium-term effectiveness of energy-saving events. Specifically, selecting participants based on a combination of energy consumption level, social influence, and behavioral adaptability (S5) yields the highest energy-saving outcomes. The study also finds that the positive impact of optimized participant selection increases with community size, emphasizing the role of network effects in promoting behavioral diffusion. In addition, sensitivity analysis further shows that event success rate and participant percentage have strong effects on overall performance, while the average number of social connections has a marginal influence. The model outputs align with empirical findings from previous studies, where behavioral interventions typically achieve 5–12% energy savings, confirming the model’s validity as a predictive and decision-support tool.
Research limitations/implications: Although grounded in well-established theoretical models, this study’s primary limitation is the absence of empirical validation using real-world behavioral data. Capturing accurate social interaction patterns and quantifying behavioral attributes in practice remains challenging. Future research should conduct field experiments to empirically verify the model’s outcomes, refine parameter estimation for behavioral traits, and explore cross-cultural differences in behavioral diffusion. Integrating real consumption datasets could also enhance model accuracy and policy relevance.
Practical implications: The findings offer actionable insights for policymakers, utility companies, and event organizers aiming to design cost-effective energy awareness programs. By strategically selecting participants with high social influence and behavioral adaptability, the overall impact of awareness campaigns can be maximized even under budget constraints. This approach enables more efficient resource allocation, reduces campaign costs, and increases community-wide behavioral adoption rates. Additionally, the framework can guide the development of targeted reward and penalty systems that promote collective energy efficiency.
Social implications: This research contributes to sustainable energy management and environmental responsibility by encouraging community-based behavioral change. The results highlight the importance of leveraging social influence to spread energy-saving habits, potentially leading to long-term cultural shifts toward energy efficiency. Implementing such optimized event strategies could improve public awareness, foster social cooperation, and enhance quality of life through reduced energy costs and environmental impact.
Originality/value: This study presents a novel, integrated agent-based framework that simultaneously considers individual energy behavior, adaptability, and social influence to optimize participant selection for awareness events. Unlike previous studies that focus solely on event design or social network structure, this work uniquely bridges behavioral theory and computational modeling to propose a systematic, data-driven method for improving energy-saving program efficiency. The model can serve as a decision-support tool for designing effective social interventions in both residential and urban contexts.

DOI: https://doi.org/10.22108/pom.2025.140474.1599