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

  • Research Article-Computer Engineering and Computer Science
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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.

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Authors and Affiliations

  1. Computer Science and Technology, Manav Rachna University (Formerly, Manav Rachna College of Engineering), Faridabad, India

    Monika Goyal & Mrinal Pandey

Authors
  1. Monika Goyal
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  2. Mrinal Pandey
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Contributions

Mrinal Pandey and Monika Goyal conducted the research and analyze the data. Monika Goyal performed the literature survey and experiments. Statistical Analysis is done by Mrinal Pandey. The research article is written by Mrinal Pandey and Monika Goyal.

Corresponding author

Correspondence to Mrinal Pandey.

Appendix

Appendix

1.1 Sample Calculations for Model Evaluation

The sample calculations using formulae in Eqs. 9–13 are described here. Table 6 contains the predicted values, observed values of response variables Y1 and Y2 from the dataset and predicted values after applying KNN algorithms. The calculations for model evaluation on the basis of values given in Table 6 have been performed manually on 20 and 100 sample size, respectively, which has been selected in respective order from 1–10 and 1–100.

Table 6 Sample dataset showing all predictor values and predicted values using KNN
Full size table

Referring to Eqs. 9–13, applying the formulae on observed values and values predicted using KNN, For Y1 calculated results for samples of initial 20 records,

$$ \begin{aligned} & {\text{RMSE}} = 12.01 \\ & {\text{MSE}} = 144.29 \\ & {\text{MAE}} = 10.2 \\ & R\;{\text{Squared}} = - 5.2 \\ & {\text{Accuracy}} = 43.82\% \\ \end{aligned} $$

Referring to Eqs. 9–13, applying the formulae on observed values and values predicted using KNN, For Y1 calculated results for samples of initial 100 records,

$$ \begin{aligned} & {\text{RMSE}} = 14.02 \\ & {\text{MSE}} = 196.6 \\ & {\text{MAE}} = 10.9 \\ & R\;{\text{Squared}} = - 1.55 \\ & {\text{Accuracy}} = 48.46\% \\ \end{aligned} $$

The sample calculations using formulae in Eqs. 9–13 are described here. Table 7 contains the predicted values, observed values of response variables Y1 and Y2 from the dataset, and predicted values after applying XGBoost algorithms. The calculations for model evaluation on the basis of values given in Table 7 have been performed manually on 20 and 100 sample size, respectively, which has been selected in respective order from 1–20 and 1–100.

Table 7 Sample dataset showing all predictor values and predicted values using XGBoost
Full size table

Applying the formulae on the values predicted using XGBoost, For Y1 the calculated results for samples of initial 20 records,

$$ \begin{aligned} & {\text{RMSE}} = 11.98 \\ & {\text{MSE}} = 143.59 \\ & {\text{MAE}} = 9.82 \\ & R\;{\text{Squared}} = - 5.17 \\ & {\text{Accuracy}} = 40.68 \\ \end{aligned} $$

Applying the formulae on the values predicted using XGBoost, For Y1 the calculated results for samples of initial 100 records,

$$ \begin{aligned} & {\text{RMSE}} = 14.25 \\ & {\text{MSE}} = 203.1 \\ & {\text{MAE}} = 11.13 \\ & R\;{\text{Squared}} = - 1.63 \\ & {\text{Accuracy}} = 49.57 \\ \end{aligned} $$

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Goyal, M., Pandey, M. A Systematic Analysis for Energy Performance Predictions in Residential Buildings Using Ensemble Learning. Arab J Sci Eng 46, 3155–3168 (2021). https://doi.org/10.1007/s13369-020-05069-2

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