Meta learning regression framework for energy consumption prediction in retrofitted buildings: A case study of South Korea

Abstract

Building energy consumption (BEC) prediction is crucial for optimizing energy efficiency in building retrofit projects, particularly during the early design phase. However, existing machine learning (ML) models often face challenges with overfitting and complexity, limiting their accuracy and generalizability. This study addresses these issues by proposing a novel meta-learning regression framework for energy prediction in green retrofitted buildings. The framework employs optimized base regressors using various ML models and integrates them through stacking regression to enhance predictive accuracy and generalization. The model's performance is evaluated against advanced methods like Deep Neural Networks and Ensemble Learning Regression using data from over 811 green retrofitting projects in South Korea. The proposed method demonstrates superior efficiency, achieving the lowest mean absolute error (19.899 for Primary Energy Consumption and 11.301 for Energy Required Amount), lowest root mean squared error (29.494 for Primary Energy Consumption and 19.977 for Energy Required Amount), and highest R-squared score (0.786 for Primary Energy Consumption and 0.542 for Energy Required Amount). This research contributes a novel approach to ensemble modeling for BEC prediction, showcasing its superior accuracy, generalizability, and practical applicability in the context of green building retrofits. © 2024 Elsevier Ltd