Hybrid Models for Forecasting Interior Lighting Energy Consumption Using Support Vector Regression, CatBoost, and Chaos Game Optimization
Abstract
In the scientific community, forecasting building energy use has emerged as a key strategy for improving energy efficiency in recent years. A significant portion of electricity is used for building lighting. However, because there are so many variables that affect lighting energy usage, it is still difficult to predict with any degree of accuracy. Given the need and significance of forecasting building energy consumption, this study attempted to forecast building interior lighting energy using machine learning (ML) techniques. The primary ML techniques employed in this work are the Chaos Game Optimization (CGO) algorithm in conjunction with support vector regression (SVR) and categorical boosting (CatBoost). These algorithms were integrated into hybrid frameworks (SVR-CGO and CatBoost-CGO) in order to optimize hyperparameters and enhance predictive performance. The aim of this integration is to create hybrid models that optimize the hyperparameters of the main algorithms. The case study's findings demonstrated that the suggested approach had a suitable and acceptable level of accuracy and that the hybrid models it suggested could accurately estimate a building's interior lighting energy values. Specifically, the models achieved R² values above 0.99 across most energy categories, with the CatBoost-CGO hybrid yielding the lowest error values (RMSE = 39.23, MAE = 14.87, MAPE = 0.0144 for label A) and the SVR-CGO hybrid performing better in categories B, C, and E (R² up to 0.9999 and RMSE as low as 1.93). The results showed that the Catboost-CGO hybrid model is more accurate because, according to the test dataset, it has relatively higher evaluation index values in the energy labels
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DOI: https://doi.org/10.31449/inf.v46i29.9544
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