Machine learning for energy performance prediction at the design stage of buildings

阶段(地层学) 能量(信号处理) 计算机科学 能源性能 建筑工程 人工智能 机器学习 环境科学 工程类 地质学 数学 统计 古生物学
作者
Razak Olu-Ajayi,Hafiz Alaka,Ismail Sulaimon,Funlade Sunmola,Saheed Ajayi
出处
期刊:Energy for Sustainable Development [Elsevier BV]
卷期号:66: 12-25 被引量:23
标识
DOI:10.1016/j.esd.2021.11.002
摘要

The substantial amount of energy consumption in buildings and the associated adverse effects prompts the importance of understanding building energy efficiency. Developing an energy prediction model with high accuracy is considered one of the most effective approach to understanding building energy efficiency. Therefore, various studies have developed diverse models for predicting building energy consumption focused on the current building stock. However, to ensure future buildings are constructed to be more energy efficient, it is essential to consider energy efficiency at the design stage. Machine Learning (ML) algorithms are considered the most contemporary and best method for prediction, and these algorithms (such as Support Vector Machine (SVM) and Decision Tree (DT), among others) have gained much attention in the field of energy prediction. However, no study has explored the application of hyper parameter tuning and feature selection methods in developing a design stage Machine Learning (ML) energy predictive model. In this research, nine machine learning classification-based algorithms were compared for energy performance assessment at the design stage of residential buildings. Additionally, feature selection and hyper parameter tunning were implemented. The result shows that it is possible to develop a high performing ML model for building energy use prediction at the design stage. Furthermore, Gradient Boosting (GB) outperformed the other models with an accuracy of 0.67 for predicting building energy performance. • We explored the development of an efficient energy performance assessment model for building designers. • We developed nine models for assessing energy performance at the building design stage. • We investigated the effect of feature selection on model performance • We conducted parameter optimization to achieve the best performance
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