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.
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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.
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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
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