An Agent-Based Computational Model on Household Electricity Consumption in Indian Cities

Traditional methods of planning and managing cities often adopt a simplified linear approach to understanding their composition and functioning. These approaches have led to the creation of many undesirable and non-functional cities. However, over the last few decades, there has been a radical shift in the approach to investigating cities through the adoption of complexity science and ist various concepts. Unfortunately, this paradigm shift has been limited to only a handful of developed Western nations. This study proposes an extendable agent-based computational model for assessing household electricity consumption patterns in cities of developing nations. The model is demonstrated using an urban precinct in India as a representative case. It simulates the monthly electricity consumption of an urban precinct in Nagpur by combining the significant factors impacting household electricity consumption in developing nations, with the household's electricity consumption process in relation to outdoor weather conditions, and the heterogeneity in occupants’ behavior. The predictive validation of the model using the “BehaviorSpace” tool yielded promising results, with the model consistently and accurately predicting the growth of variables and household electricity consumption patterns. The model also successfully generated multiple future scenarios, showcasing ist capacity to forecast the future implications of policy interventions. The simulation results indicated that household electricity consumption in Indian cities is driven by population growth and income levels. Additionally, it suggests that widely practiced rooftop solar panel installation policies may not be a viable approach to achieving sustainability in this sector. This study finds that the “emergent urbanism” approach has the potential to revitalize the urban energy planning process through an effective framework for comprehensively understanding the phenomena and addressing ist various complexities.