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.

能源是一种宝贵的资源，需要加以注意，以实现效率并减少其浪费。在全球范围内，多层建筑是最大的能源消耗者。建筑物内的大部分能量被消耗以维持所需的温度，以使居住者感到舒适。为此，需要满足建筑物的热负荷和冷负荷要求。应将这些要求降至最低，以减少能耗并优化能耗。建筑物的某些特性极大地影响了供暖负荷和制冷负荷的要求。本文提出了一种系统的方法来分析建筑物在能耗中起着至关重要作用的各种因素，其次是用于住宅建筑能耗预测的传统机器学习和现代集成学习的算法方法。结果表明，集成技术以明显的优势胜过机器学习技术。多元线性回归预测加热负荷和冷却负荷的准确度分别为88.59％和85.26％，支持向量回归的预测准确度为82.38％和89.32％，K近邻的预测准确度为91.91％和94.47％。集成技术获得的精度相对更好-随机森林为99.74％和94.79％，梯度增强机器为99.73％和96.22％，极端梯度增强为99.75％和95.94％。多元线性回归预测加热负荷和冷却负荷的准确度分别为88.59％和85.26％，支持向量回归的预测准确度为82.38％和89.32％，K近邻的预测准确度为91.91％和94.47％。集成技术获得的精度相对更好-随机森林为99.74％和94.79％，梯度增强机器为99.73％和96.22％，极端梯度增强为99.75％和95.94％。多元线性回归预测加热负荷和冷却负荷的准确度分别为88.59％和85.26％，支持向量回归的预测准确度为82.38％和89.32％，K近邻的预测准确度为91.91％和94.47％。集成技术获得的精度相对更好-随机森林为99.74％和94.79％，梯度增强机器为99.73％和96.22％，极端梯度增强为99.75％和95.94％。