Advances in Energy Research

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A gradient boosting regression based approach for energy consumption prediction in buildings

  • Bataineh, Ali S. Al (Department of Electrical Engineering and Computer Science, University of Toledo)
  • Received : 2019.01.20
  • Accepted : 2019.06.21
  • Published : 2019.09.25
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Abstract

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.

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References

  1. Bajcinovci, B. (2017), "Achieving thermal comfort and sustainable urban development in accordance with the principles of bioclimatic architecture: A case study of Ulcinj (Montenegro)", Quaestiones Geographicae, 40(3), 131-140. https://doi.org/10.1515/quageo-2017-0041
  2. Bataineh, A.A. and Kaur, D. (2018), "A comparative study of different curve fitting algorithms in artificial neural network using housing dataset", Proceedings of the NAECON2018-IEEE National Aerospace and Electronics Conference, Ohio, U.S.A., July.
  3. Candanedo, L.M., Feldheim, V. and Deramaix, D. (2017), "Data driven prediction models of energy use of appliances in alow-energy house", Energy Build., 140, 81-97. https://doi.org/10.1016/j.enbuild.2017.01.083.
  4. Dong, B., Cao, C. and Lee, S.E. (2006), "Applying support vector machines to predict building energy consumption in tropical region", Energy Build., 37(5), 545-553. https://doi.org/10.1016/j.enbuild.2004.09.009.
  5. Elith, J., Leathwick, J.R. and Hastie, T. (2008), "A working guide to boosted regression trees", J. Animal Ecol., 77(4), 802-813. https://doi.org/10.1111/j.1365-2656.2008.01390.x.
  6. Ferreira, P.M., Ruano, A.E., Pestana, R. and Koczy, L.T. (2009), "Evolving RBF predictive models to forecast the Portuguese electricity consumption", IFAC Proc. Vol., 42(19), 414-419. https://doi.org/10.3182/20090921-3-TR-3005.00073.
  7. Foucquier, A., Robert, S., Suard, F. and Stephan, L. (2013), "State of the art in building modelling and energy performances prediction: A review", Renew. Sust. Energy Rev., 23, 272-288. https://doi.org/10.1016/j.rser.2013.03.004.
  8. Hua, Y., Oliphant, M. and Hu, E.J. (2016), "Development of renewable energy in Australia and China: A comparison of policies and status", Renew. Energy, 85, 1044-1051. https://doi.org/10.1016/j.renene.2015.07.060.
  9. Jung, H. C., Kim, J.S. and Heo, H. (2015), "Prediction of building energy consumption using an improved real coded genetic algorithm based least squares support vector machine approach", Energy Build., 90, 76-84. https://doi.org/10.1016/j.enbuild.2014.12.029.
  10. Karatasou, S., Santamouris, M. and Geros, V. (2006), "Modeling and predicting building's energy use with artificial neural networks: Methods and results", Energy Build., 38(8), 949-958. https://doi.org/10.1016/j.enbuild.2005.11.005.
  11. Keneni, B.M., Kaur, D., Bataineh, A.A., Devabhaktuni, V.K., Javaid, A.Y., Zaientz, J.D. and Marinier, R.P. (2019), "Evolving rule-based explainable artificial intelligence for unmanned aerial vehicles", IEEE Access, 7, 17001-17016. https://doi.org/10.1109/ACCESS.2019.2893141.
  12. Khosravani, H.R., Castilla, M.D., Berenguel, M., Ruano, A.E. and Ferreira, P.M. (2016), "A comparison of energy consumption prediction models based on neural networks of a bioclimatic building", Energies, 9(1), 57. https://doi.org/10.3390/en9010057.
  13. Li, K., Su, H. and Chu, J. (2011), "Forecasting building energy consumption using neural networks and hybrid neuro-fuzzy system: A comparative study", Energy Build., 43(10), 2893-2899. https://doi.org/10.1016/j.enbuild.2011.07.010.
  14. Neto, A.H. and Fiorelli, F.A. (2008), "Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption", Energy Build., 40(12), 2169-2176. https://doi.org/10.1016/j.enbuild.2008.06.013.
  15. Panwar, N.L., Kaushik, S.C. and Kothari, S. (2011), "Role of renewable energy sources in environmental protection: A review", Renew. Sust. Energy Rev., 15(3), 1513-1524. https://doi.org/10.1016/j.rser.2010.11.037.
  16. Perez-Lombard, L., Ortiz, J. and Pout, C. (2008), "A review on buildings energy consumption information", Energy Build., 40(3), 394-398. https://doi.org/10.1016/j.enbuild.2007.03.007.
  17. Scarlat, N., Dallemand, J.F., Monforti-Ferrario, F., Banja, M. and Motola, V. (2015), "Renewable energy policy framework and bioenergy contribution in the European Union-An overview from National Renewable Energy Action Plans and Progress Reports", Renew. Sust. Energy Rev., 51, 969-985. https://doi.org/10.1016/j.rser.2015.06.062.
  18. Tzikopoulos, A., Karatza, M.C. and Paravantis, J. (2005), "Modeling energy efficiency of bioclimatic buildings", Energy Build., 37(5), 529-554. https://doi.org/10.1016/j.enbuild.2004.09.002.
  19. Zhou, Z.H. (2012), Ensemble Methods.
  20. Zhu, C., Chen, W., Zhu, Z.A., Wang, G., Wang, D. and Chen, Z. (2009), "A general magnitude-preserving boosting algorithm for search ranking", Proceedings of the 18th ACM Conference on Information and Knowledge Management-CIKM 09, Hong Kong, China, November.