Estimation of Heating Load Consumption in Residual Buildings using Optimized Regression Models Based on Support Vector Machine

Accurate energy consumption forecasting and assessing retrofit options are vital for energy conservation and emissions reduction. Predicting building energy usage is complex due to factors like building attributes, energy systems, weather conditions, and occupant behavior. Extensive research has led to diverse methods and tools for estimating building energy performance, including physics-based simulations. However, accurate simulations often require detailed data and vary based on modeling sophistication. The growing availability of public building energy data offers opportunities for applying machine learning to predict building energy performance. This study evaluates Support Vector Regression (SVR) models for estimating building heating load consumption. These models encompass a single model, one optimized with the Transit Search Optimization Algorithm (TSO) and another optimized with the Coot optimization algorithm (COA). The training dataset consists of 70% of the data, which incorporates eight input variables related to the geometric and glazing characteristics of the buildings. Following the validation of 15% of the dataset, the performance of the remaining 15% is evaluated using five different assessment metrics. Among the three candidate models, Support Vector Regression optimized with the Coot optimization algorithm (SVCO) demonstrates remarkable accuracy and stability, reducing prediction errors by an average of 20% to over 50% compared to the other two models and achieving a maximum R 2 value of 0.992 for heating load prediction.