Novel Data-Driven Machine Learning Models for Heating Load Prediction: Single and Optimized Naive Bayes

Numerous approaches can be employed to create models for assessing the heat gains of a building arising from both external and internal sources. This modeling process evaluates effective operational strategies, conducts retrofit audits, and projects energy consumption. These techniques range from simple regression analyses to more intricate models grounded in physical principles. A prevalent assumption underlying all these modeling techniques is the requirement for input variables to be derived from authentic data, as the absence of realistic input data can lead to substantial underestimations or overestimations in energy consumption assessments. In this paper, eight input parameters, including relative compactness, orientation, wall area, roof area, glazing area, overall height, surface area, and glazing area distribution, are employed for training proposed Naive Bayes (NB)-based machine learning models. Utilizing a novel approach, this research explores the application of Beluga Whale Optimization and the Coot Optimization algorithm for optimizing the Naive Bayes model in heating load prediction. By harnessing the collective intelligence of Beluga Whales and drawing from the cooperative behavior of coots, the research aims to improve the model's predictive capabilities, which is of paramount importance in building energy management. Based on the comparative analysis between developed models (NB, NBCO, and NBBW), it is attainable that NBCO and NBBW, as two optimized models, have 2.4% and 1.3% higher R-2 values, respectively. Also, the RMSE of the NBCO was, on average, 19-33% lower than that of the two other models, confirming the high accuracy of NBCO. This innovative integration of bio-inspired optimization techniques with machine learning demonstrates a promising avenue for optimizing predictive models, offering potential energy efficiency and sustainability advancements in the built environment.