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Advances in Energy Research
  Volume 6, Number 2, September 2019, pages 91-101
open access

A gradient boosting regression based approach for energy consumption prediction in buildings
Ali S. Al Bataineh

    This paper proposes an efficient data-driven approach to build models for predicting energy consumption in buildings. Data used in this research is collected by installing humidity and temperature sensors at different locations in a building. In addition to this, weather data from nearby weather station is also included in the dataset to study the impact of weather conditions on energy consumption. One of the main emphasize of this research is to make feature selection independent of domain knowledge. Therefore, to extract useful features from data, two different approaches are tested: one is feature selection through principal component analysis and second is relative importance-based feature selection in original domain. The regression model used in this research is gradient boosting regression and its optimal parameters are chosen through a two staged coarse-fine search approach. In order to evaluate the performance of model, different performance evaluation metrics like r2-score and root mean squared error are used. Results have shown that best performance is achieved, when relative importance-based feature selection is used with gradient boosting regressor. Results of proposed technique has also outperformed the results of support vector machines and neural network-based approaches tested on the same dataset.
Key Words
    energy consumption; ensemble methods; gradient boosting regression
Ali S. Al Bataineh: Department of Electrical Engineering and Computer Science, University of Toledo, 2801 Bancroft Street, Toledo, OH 43606, U.S.A.

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