Work

Agent-based modeling framework for predicting regional electricity consumption considering occupant behavior shift and exogenous policy impact

Year: 2024

Type: article

Abstract: This study introduces a model to predict regional electricity consumption in urban areas with mixed land use patterns and high energy intensity. It employs an agent-based approach to tackle the temporal-spatial and heterogeneity distribution challenges brought about by rapid urbanization. The model evaluates potential shifts in electricity usage driven by occupants’ behavior, considering the urban environment, demographics, and energy policies. Using the GAMA platform, it forecasts electricity consumption for 907 agents in Taipei City, Taiwan. Integrating 5 clusters of 5 independent variables, the model utilizes Geographically Weighted Regression (GWR) and Decision Trees (DT) for analysis. Findings indicate a higher shift likelihood among residential occupants (Cluster 1 and Cluster 4) than commercial occupants (Cluster 2 and Cluster 3), especially in areas with diverse clusters. Consequently, the projected electricity consumption in 2030 is only 2 kWh/household lower than in 2020, underscoring the challenge of achieving carbon neutrality through changes in occupants’ electricity usage behavior in a mature city. Future research aims to enhance accuracy by delving into cluster shift mechanisms and socio-economic backgrounds. Despite the hurdles, the model contributes to understanding urban energy dynamics and informs policy formulation for energy-efficient practices.

Source: Energy and Buildings

DOI: 10.1016/j.enbuild.2024.114897

Authors: I-Chun Chen, Kuang-Ly Cheng

Language: English

Topic: Building Energy and Comfort Optimization

Subfield: Building and Construction

Field: Engineering

Domain: Physical Sciences

SDGs: Sustainable cities and communities, Climate action, Affordable and clean energy

Open Access status: hybrid

Estimated APC paid: $4,180

Funder: National Science and Technology Council