Abstract
Forecasting the day-ahead electricity load is beneficial for both suppliers and consumers. The reduction of electricity waste and the rational dispatch of electric generator units can be significantly improved with accurate load forecasts. This article is focused on studying and developing computational intelligence techniques for electricity load forecasting. Several models are developed to forecast the electricity load of the next hour using real data from an industrial pole in Spain. Feature selection and feature extraction are performed to reduce overfitting and therefore achieve better models, reducing the training time of the developed methods. The best of the implemented models is optimized using grid search strategies on hyperparameter space. Then, twenty four different instances of the optimal model are trained to forecast the next twenty four hours. Considering the computational complexity of the applied techniques, they are developed and evaluated on the computational platform of the National Supercomputing Center (Cluster-UY), Uruguay. Standard performance metrics are applied to evaluate the proposed models. The main results indicate that the best model based on ExtraTreesRegressor obtained has a mean absolute percentage error of 2.55% on day ahead hourly forecast which is a promising result.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Amjady, N., Keynia, F.: Short-term load forecasting of power systems by combination of wavelet transform and neuro-evolutionary algorithm. Energy 34(1), 46–57 (2009)
Bashir, Z., El-Hawary, M.: Applying wavelets to short-term load forecasting using pso-based neural networks. IEEE Trans. Power Syst. 24(1), 20–27 (2009)
Bellman, R.: Dynamic Programming. Princeton University Press, Princeton (1957)
Buitinck, L., et al.: API design for machine learning software: experiences from the scikit-learn project. In: ECML PKDD Workshop: Languages for Data Mining and Machine Learning, pp. 108–122 (2013)
Burger, E., Moura, S.: Gated ensemble learning method for demand-side electricity load forecasting. Energy Build. 109, 23–34 (2015)
Chen, Y., Luh, P., Rourke, S.: Short-term load forecasting: similar day-based wavelet neural networks. In: 7th World Congress on Intelligent Control and Automation, pp. 3353–3358 (2008)
De Felice, M., Alessandri, A., Ruti, P.M.: Electricity demand forecasting over italy: potential benefits using numerical weather prediction models. Electr. Power Syst. Res. 104, 71–79 (2013)
Diniz, A., Costa, F., Maceira, M., dos Santos, T., Dos Santos, L., Cabral, R.: Short/mid-term hydrothermal dispatch and spot pricing for large-scale systems-the case of Brazil. In: Power Systems Computation Conference, pp. 1–7 (2018)
Do, L., Lin, K.H., Molnár, P.: Electricity consumption modelling: a case of germany. Econ. Model. 55, 92–101 (2016)
Dudek, G.: Pattern-based local linear regression models for short-term load forecasting. Electr. Power Syst. Res. 130, 139–147 (2016)
Fan, S., Chen, L., Lee, W.J.: Machine learning based switching model for electricity load forecasting. Energy Convers. Manag. 49(6), 1331–1344 (2008)
Hagan, M., Behr, S.: The time series approach to short term load forecasting. IEEE Trans. Power Syst. 2(3), 785–791 (1987)
Karrthikeyaa, S., Vijayaraghavan, V.: A two-fold machine learning approach for efficient day-ahead load prediction at hourly granularity for NYC. In: Advances in Information and Communication, vol. 2, p. 84 (2019)
Kim, C., Yu, I., Song, Y.: Kohonen neural network and wavelet transform based approach to short-term load forecasting. Electr. Power Syst. Res. 63(3), 169–176 (2002)
Lahouar, A., Slama, J.: Day-ahead load forecast using random forest and expert input selection. Energy Convers. Manag. 103, 1040–1051 (2015)
Lazos, D., Sproul, A., Kay, M.: Optimisation of energy management in commercial buildings with weather forecasting inputs: a review. Renew. Sustain. Energy Rev. 39, 587–603 (2014)
Luján, E., Otero, A., Valenzuela, S., Mocskos, E., Steffenel, L., Nesmachnow, S.: An integrated platform for smart energy management: the CC-SEM project. Revista Facultad de Ingeniería (2019)
McKinney, W.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, pp. 51–56 (2010)
Nesmachnow, S., Iturriaga, S.: Cluster-UY: scientific HPC in Uruguay. In: International Supercomputing in Mexico (2019)
Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Qamber, I.: Peak load estimation studies in several countries. Electr. Power Syst. Res. 1(2) (2017)
Resende, L., Soares, M., Ferreira, P.: Electric power load in Brazil: view on the long-term forecasting models. Production 28, 83–99 (2018)
Silva, L.: A feature engineering approach to wind power forecasting: GEFCom 2012. Int. J. Forecast. 30(2), 395–401 (2014)
Soliman, A., Al-Kandari, A.: Electrical Load Forecasting: Modeling and Model Construction. Elsevier, Amsterdam (2010)
Son, H., Kim, C.: Short-term forecasting of electricity demand for the residential sector using weather and social variables. Resour. Conserv. Recycl. 123, 200–207 (2017)
Taylor, J., McSharry, P.: Short-term load forecasting methods: an evaluation based on European data. IEEE Trans. Power Syst. 22(4), 2213–2219 (2007)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Porteiro, R., Nesmachnow, S., Hernández-Callejo, L. (2020). Short Term Load Forecasting of Industrial Electricity Using Machine Learning. In: Nesmachnow, S., Hernández Callejo, L. (eds) Smart Cities. ICSC-CITIES 2019. Communications in Computer and Information Science, vol 1152. Springer, Cham. https://doi.org/10.1007/978-3-030-38889-8_12
Download citation
DOI: https://doi.org/10.1007/978-3-030-38889-8_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-38888-1
Online ISBN: 978-3-030-38889-8
eBook Packages: Computer ScienceComputer Science (R0)