Applicability of Multivariate Linear Regression in Building Energy Demand Estimation

Abstract

The vision of the research project is to find an energy optimal building configuration, suitable for specified requirements and restrictions. The first step on this way is to create a measure to compare building configurations, faster than explicit energetic simulations. The current study examines the applicability of multivariate linear regression to support the solution of building optimization problems. During the study, multivariate linear regression models were created to estimate the expected annual heating energy demand of building configurations and examined their accuracy Between examinations, the models were modified so that the complexity was increased only to such an extent that the approximation was still sufficiently accurate. The result was a multivariate linear model that estimated the expected output for unknown descriptive variables with a 0% relative error and a 1.6% standard deviation. The R2 point of the estimates was 0.9884. Based on these, the model was considered applicable in the search space defined by the training patterns.

Determinants of Air Quality in Building Environments: A Multi-Regression Analysis and Implications for Open Teaching Practices

“…A formidable challenge persists: how to sustainably accommodate this demographic explosion without further straining limited resources or exacerbating existing environmental issues? Over the last two decades, there has been an increased emphasis on a multidisciplinary approach to building and energy management, establishing it as an indispensable facet of the contemporary urban milieu [1][2][3][4][5][6][7][8][9][10].…”