Data-Driven Residence Energy Consumption Prediction Model Considering Water Use Data and Socio-Demographic Data
Publication: Construction Research Congress 2024
ABSTRACT
Data-driven energy prediction models can help urban planners and policymakers evaluate urban energy consumption patterns and then make informed decisions on how to improve urban energy efficiency. Typically, energy use data and building characteristics data were used to train these data-driven models. Few research utilized water use data and socio-demographic data, which have nexus with the energy consumption of buildings. This research utilized energy use data, building characteristics, socio-demographic, and water use data of multi-family buildings in New York City to train Least Absolute Shrinkage and Selection Operator (LASSO), Ridge Regression (RR), Support Vector Regression (SVR), and Random Forest Regression (RFR) machining learning models. The effects of socio-demographic and water use features on the performance of energy prediction were analyzed. Results showed that water use feature had significant positive impacts on the performance of LASSO, RR, and RFR models. Socio-demographic features had obvious positive impacts on the performance of SVR and RFR models. RFR trained with the BW dataset (including building characteristic features and water use feature) performed the best.
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Information & Authors
Information
Published In
Construction Research Congress 2024
Pages: 497 - 505
History
Published online: Mar 18, 2024
ASCE Technical Topics:
- Buildings
- Business management
- Energy consumption
- Energy engineering
- Energy sources (by type)
- Hydro power
- Infrastructure
- Municipal water
- Practice and Profession
- Renewable energy
- Social factors
- Structural engineering
- Structures (by type)
- Urban and regional development
- Urban areas
- Water (by type)
- Water and water resources
- Water management
- Water supply
- Water use
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