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Abstract
Improving the energy efficiency of the building sector has become an increasing concern in the world, given the alarming reports of greenhouse gas emissions. The management of building energy systems is considered an essential means for achieving this goal. Predicting indoor temperature constitutes a critical task for the management strategies of these systems. Several approaches have been developed for predicting indoor temperature. Determining the most effective has thus become a necessity. This paper contributes to this objective by comparing the ability of seven machine learning algorithms (ML) and the thermal gray box model to predict the indoor temperature of a closed room. The comparison was conducted on a set of data recorded in a room of the Laboratory of Civil Engineering and geo-Environment (LGCgE) at Lille University. The results showed that the best prediction was obtained with the artificial neural network (ANN) and extra trees regressor (ET) methods, which outperformed the thermal gray box model.
Highlights
Civil Engineering and geo-Environment (LGCgE) at Lille University
The results showed that the Gaussian process regression (GPR) outperformed the artificial neural network (ANN), support vector machine (SVM), decision tree (DT), and random forest (RF) models
This part focuses on the results of the prediction of the temperature at the center of the room only, since similar results were obtained for the prediction of the temperature of the internal faces of the walls
Summary
Civil Engineering and geo-Environment (LGCgE) at Lille University. The results showed that the best prediction was obtained with the artificial neural network (ANN) and extra trees regressor (ET). The indoor temperature is a major key to such a strategy, being one of the most critical parameters affecting energy consumption and personal comfort. Temperature forecasting has been considered an interesting subject, widely studied in the literature [1,2,3,4] It has been integrated into predictive control models, developed to optimize energy devices [5,6]. The black box model forgoes the need for detailed input data of the simulated building and focuses on learning from the available historical data [11]. This approach has been used in a wide variety of building energy performance applications.
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