Application of the BORUTA Algorithm to Input Data Selection for a Model Based on Rough Set Theory (RST) to Prediction Energy Consumption for Building Heating

Tomasz Szul, Sylwester Tabor and Krzysztof Pancerz
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Tomasz Szul: Faculty of Production and Power Engineering, University of Agriculture, 30-149 Krakow, Poland
Sylwester Tabor: Faculty of Production and Power Engineering, University of Agriculture, 30-149 Krakow, Poland
Krzysztof Pancerz: Department of Technology and Computer Science, Szymon Szymonowic State School of Higher Education in Zamość, 22-400 Zamość, Poland

Energies, 2021, vol. 14, issue 10, 1-13

Abstract: Energy prediction used for building heating has attracted particular attention because it is often required in the development of various strategies to improve the energy efficiency of buildings, especially those undergoing thermal improvements. The complexity, dynamics, uncertainty, and nonlinearity of existing building energy systems create a great need for modeling techniques. One of them is machine learning models, which are based on input data consisting of features that describe the objects under study. The data describing actual buildings used to build the model may be characterized by missing values, duplicate or inconsistent features, noise, and outliers. Therefore, an extremely important aspect of the prediction model development effort is the proper selection of features to simplify the prediction of energy consumption for heating. In this connection, the goal was to evaluate the usefulness of a model describing the final energy demand rate for building heating using groups of features describing actual residential buildings undergoing thermal retrofit. The model was created by combining two algorithms: the BORUTA feature selection algorithm, which prepares conditional variables corresponding to features for a prediction model based on rough set theory (RST). The research was conducted on a group of 109 multi-family buildings from the end of the last century (made in large-panel technology), thermomodernized at the beginning of the 21st century. Evaluation metrics such as MAPE, MBE, CV RMSE, and R 2 , which are adopted as statistical calibration standards by ASHRAE, were used to assess the quality of the developed prediction model. The analysis of the obtained results indicated that the model based on RST, based on the features selected by the BORUTA algorithm, gives a satisfactory prediction quality with a limited number of input variables, and thus allows to predict energy consumption (after thermal improvement) for this type of buildings with high accuracy.

Keywords: data selection; BORUTA algorithm; building load prediction; rough set theory; building energy modeling; thermal improvement of buildings (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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