Abstract
The increase of renewable energy sources of intermittent nature has brought several new challenges for power and energy systems. In order to deal with the variability from the generation side, there is the need to balance it by managing consumption appropriately. Forecasting energy consumption becomes, therefore, more relevant than ever. This paper presents and compares three different ensemble learning methods, namely random forests, gradient boosted regression trees and Adaboost. Hour-ahead electricity load forecasts are presented for the building N of GECAD at ISEP campus. The performance of the forecasting models is assessed, and results show that the Adaboost model is superior to the other considered models for the one-hour ahead forecasts. The results of this study compared to previous works indicates that ensemble learning methods are a viable choice for short-term load forecast.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Zhang, X., Wang, J., Zhang, K.: Short-term electric load forecasting based on singular spectrum analysis and support vector machine optimized by Cuckoo search algorithm. Electr. Power Syst. Res. 146, 270–285 (2017). https://doi.org/10.1016/j.epsr.2017.01.035
Raza, M.Q., Khosravi, A.: A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings. Renew. Sustain. Energy Rev. 50, 1352–1372 (2015). https://doi.org/10.1016/j.rser.2015.04.065
Saber, A.Y., Alam, A.K.M.R.: Short term load forecasting using multiple linear regression for big data. In: 2017 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1–6 (2017)
Pinto, T., Sousa, T.M., Vale, Z.: Dynamic artificial neural network for electricity market prices forecast. In: 2012 IEEE 16th International Conference on Intelligent Engineering Systems (INES), pp. 311–316 (2012)
Pinto, T., Sousa, T.M., Praça, I., et al.: Support Vector Machines for decision support in electricity markets’ strategic bidding. Neurocomputing 172, 438–445 (2016). https://doi.org/10.1016/j.neucom.2015.03.102
Ahmad, T., Chen, H.: Nonlinear autoregressive and random forest approaches to forecasting electricity load for utility energy management systems. Sustain Cities Soc. 45, 460–473 (2019). https://doi.org/10.1016/j.scs.2018.12.013
Touzani, S., Granderson, J., Fernandes, S.: Gradient boosting machine for modeling the energy consumption of commercial buildings. Energy Build 158, 1533–1543 (2018). https://doi.org/10.1016/j.enbuild.2017.11.039
Osório, G.J., Matias, J.C.O., Catalão, J.P.S.: Short-term wind power forecasting using adaptive neuro-fuzzy inference system combined with evolutionary particle swarm optimization, wavelet transform and mutual information. Renew. Energy 75, 301–307 (2015). https://doi.org/10.1016/j.renene.2014.09.058
Gou, J., Hou, F., Chen, W., et al.: Improving Wang–Mendel method performance in fuzzy rules generation using the fuzzy C-means clustering algorithm. Neurocomputing 151, 1293–1304 (2015). https://doi.org/10.1016/j.neucom.2014.10.077
Du, P., Wang, J., Yang, W., Niu, T.: Multi-step ahead forecasting in electrical power system using a hybrid forecasting system. Renew Energy 122, 533–550 (2018). https://doi.org/10.1016/j.renene.2018.01.113
Pedregosa, F., Varoquaux, G., Gramfort, A., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Friedman, J.H.: Stochastic gradient boosting. Comput. Stat. Data Anal. 38, 367–378 (2002). https://doi.org/10.1016/S0167-9473(01)00065-2
Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29, 1189–1232 (2001)
Drucker, H.: Improving regressors using boosting techniques. In: Proceedings of the Fourteenth International Conference on Machine Learning, pp. 107–115. Morgan Kaufmann Publishers Inc., San Francisco (1997)
Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55, 119–139 (1997). https://doi.org/10.1006/jcss.1997.1504
Jozi, A., Pinto, T., Praça, I., Vale, Z.: Day-ahead forecasting approach for energy consumption of an office building using support vector machines. In: 2018 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1620–1625 (2018)
Acknowledgements
This work has received funding from National Funds through FCT (Fundaçao da Ciencia e Tecnologia) under the project SPET – 29165, call SAICT 2017.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Silva, J., Praça, I., Pinto, T., Vale, Z. (2020). Energy Consumption Forecasting Using Ensemble Learning Algorithms. In: Herrera-Viedma, E., Vale, Z., Nielsen, P., Martin Del Rey, A., Casado Vara , R. (eds) Distributed Computing and Artificial Intelligence, 16th International Conference, Special Sessions. DCAI 2019. Advances in Intelligent Systems and Computing, vol 1004. Springer, Cham. https://doi.org/10.1007/978-3-030-23946-6_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-23946-6_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-23945-9
Online ISBN: 978-3-030-23946-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)