Abstract
Decarbonisation of the economy is the key to reducing greenhouse-effect gas emissions and climate change. One of the ways decarbonisation of economy is electrification of economic sectors. In this case, the implementation of micro-grids in different economic sectors such as households, industry, and commerce is a great mechanism that allows the integration of renewable energies into the electrical power system and to contribute with accelerated energy transition for decarbonisation. However, micro-grids include self-generation through renewable energy and distributed generation, as well as energy efficiency in the consumer. Micro-grids have energetic, economic, and environmental benefits for the user and the power system, but for the security of the energy supply it is necessary to balance the offer and demand of electricity at all times, which in this case must be estimated for the market of the next day. The problem here is how to estimate generation and consume for the next day when the determinant of offer and demand are variable. This paper proposes algorithms of forecasting based on machine learning with high accuracy in a decision support system of management of energy for a micro-grid.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Binder, C.R., Knoeri, C., Hecher, M.: Modeling transition paths towards decentralized regional energy autonomy: the role of legislation, technology adoption, and resource availability. Raumforschung Raumordnung – Spatial Res. Planning 74(3), 273–284 (2016). https://doi.org/10.1007/s13147-016-0396-5
Buechler, E., et al.: Global changes in electricity consumption during COVID-19. iScience 25(1), 103568 (2022). https://doi.org/10.1016/j.isci.2021.103568
Cai, L., Gu, J., Jin, Z.: Two-layer transfer-learning-based architecture for short-term load forecasting. IEEE Trans. Industr. Inf. 16(3), 1722–1732 (2020). https://doi.org/10.1109/TII.2019.2924326
Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery And Data Mining, pp. 785–794 (2016)
Dogaru, L.: The main goals of the fourth industrial revolution. Renew. Energy Perspect. Procedia Manufact. 46, 397–401 (2020). https://doi.org/10.1016/j.promfg.2020.03.058
EIA University: Sources of energy (2021). https://www.eia.gov/energyexplained/what-is-energy/sources-of-energy.php
Ali, F., et al.: Advancing from community to peer-to-peer energy trading in the Medellín-Colombia local energy market trial. IEEE Smart Cities, p. 200 (2022)
Hippert, H., Pedreira, C., Souza, R.: Neural networks for short-term load forecasting: a review and evaluation. IEEE Trans. Power Syst. 16(1), 44–55 (2001). https://doi.org/10.1109/59.910780
Hong, T.: Energy forecasting: past, present, and future. foresight. Int. J. Appl. Forecasting 32 43–48 (2014). https://ideas.repec.org/a/for/ijafaa/y2014i32p43-48.html
Hong, T., Pinson, P., Wang, Y., Weron, R., Yang, D., Zareipour, H.: Energy forecasting: a review and outlook. IEEE Open Access J. Power Energy 7, 376–388 (2020). https://doi.org/10.1109/OAJPE.2020.3029979
International energy agency: climate change and energy transition law - policies - iea. https://www.iea.org/policies/13323-climate-change-and-energy-transition-law. Accessed 30 Jan 2023
Llano, M.M.: La micro-red inteligente: una ciudad eficiente, en miniatura. Revista universitaria científica, pp. 24–29 (2015). https://www.upb.edu.co/es/documentos/doc-ciudadeficienteminiatura-inv-1464100344537.pdf
Ma, J., et al.: Demand and supply-side determinants of electric power consumption and representative roadmaps to 100% renewable systems. J. Clean. Prod. 299(2006), 126832 (2021). https://doi.org/10.1016/j.jclepro.2021.126832
Mitchell, T.M.: Machine Learning. Mcgraw-Hill science. Engineering/Math 1, 27 (1997)
Nowotarski, J., Weron, R.: Recent advances in electricity price forecasting: a review of probabilistic forecasting. Renew. Sustain. Energy Rev. 81, 1548–1568 (2018). https://doi.org/10.1016/j.rser.2017.05.234. https://www.sciencedirect.com/science/article/pii/S1364032117308808
Shi, H., Xu, M., Li, R.: Deep learning for household load forecasting-a novel pooling deep RNN. IEEE Trans. Smart Grid 9(5), 5271–5280 (2017)
UPME: Redes Inteligentes (2019). https://www1.upme.gov.co/DemandayEficiencia/Paginas/Redes-Inteligentes.aspx
Vapnik, V.: The Nature Of Statistical Learning Theory. Springer science & Business Media (2013). https://doi.org/10.1007/978-1-4757-3264-1
Willis, H., Northcote-Green, J.: Spatial electric load forecasting: a tutorial review. Proc. IEEE 71(2), 232–253 (1983). https://doi.org/10.1109/PROC.1983.12562
Zareipour, H.: Short-term electricity market prices: a review of characteristics and forecasting methods. Handbook of networks in power systems I, pp. 89–121 (2012)
Acknowledgements
This research is part of the project “Strategy of Transformation of the Colombian Energy Sector in the Horizon 2030” funded by the call 788 of Minciencias: Scientific Ecosystem. Contract number FP44842-210-2018.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Puerta, A., Hoyos, S.H., Bonet, I., Caraffini, F. (2023). An AI-Based Support System for Microgrids Energy Management. In: Correia, J., Smith, S., Qaddoura, R. (eds) Applications of Evolutionary Computation. EvoApplications 2023. Lecture Notes in Computer Science, vol 13989. Springer, Cham. https://doi.org/10.1007/978-3-031-30229-9_33
Download citation
DOI: https://doi.org/10.1007/978-3-031-30229-9_33
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-30228-2
Online ISBN: 978-3-031-30229-9
eBook Packages: Computer ScienceComputer Science (R0)