A Systematic Analysis for Energy Performance Predictions in Residential Buildings Using Ensemble Learning

Abstract

Energy being a precious resource needs to be mindfully utilized, so that efficiency is achieved and its wastage is curbed. Globally, multi-storeyed buildings are the biggest energy consumers. A large portion of energy within a building is consumed to maintain the desired temperature for the comfort of occupants. For this purpose, heating load and cooling load requirements of the building need to be met. These requirements should be minimized to reduce energy consumption and optimize energy usage. Some characteristics of buildings greatly affect the heating load and cooling load requirements. This paper presented a systematic approach for analysing various factors of a building playing a vital role in energy consumption, followed by the algorithmic approaches of traditional machine learning and modern ensemble learning for energy consumption prediction in residential buildings. The results revealed that ensemble techniques outperform machine learning techniques with an appreciable margin. The accuracy of predicting heating load and cooling load, respectively, with multiple linear regression was 88.59% and 85.26%, with support vector regression was 82.38% and 89.32%, with K-nearest neighbours was 91.91% and 94.47%. The accuracy achieved with ensemble techniques was comparatively better—99.74% and 94.79% with random forests, 99.73% and 96.22% with gradient boosting machines, 99.75% and 95.94% with extreme gradient boosting.