Investigating Opinion Dynamics Models in Agent-Based Simulation of Energy Eco-Feedback Programs

TL;DR

This paper evaluates opinion dynamics models in agent-based simulations of energy eco-feedback programs, proposing a revised model that better aligns with real-world data to improve simulation accuracy.

Contribution

It introduces a Revised Opinion Dynamic (ROD) model that enhances the accuracy of eco-feedback simulations by considering influence conditions and opinion weights.

Findings

ROD model closely matches field experiment data

Influence conditions significantly affect simulation accuracy

Opinion weight factors impact energy-saving predictions

Abstract

According to research, reducing consumer energy demand through behavioural interventions is an important factor of efforts to reduce greenhouse gas emissions and climate change.On this basis, feedback interventions that make energy consumption and conservation efforts apparent are seen as a feasible method for increasing energy-saving habits. Simulation techniques provide a convenient and cost-effective tool for examining the parameters that may affect the amount of energy saved as a result of such interventions. However, constructing a reliable model that accurately represents real-world processes is a significant issue. Five Opinion Dynamic (OD) models that depict how opinion change occurs among individuals interactions are investigated in this paper, and a Revised OD (ROD) model is suggested to develop more efficient eco-feedback simulation models. The results show that the influence condition and the weight-factor of connected opinions have a substantial impact on the accuracy of simulation outputs when compared to field experiment reports. As a result, ROD has been proposed for eco-feedback program simulations, as it provides the nearest approximation to the field data.