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Article

Short-Term Power Forecasting Framework for Microgrids Using Combined Baseline and Regression Models

1
Department of Electronic Technology, Escuela Politécnica Superior, Universidad de Sevilla, 41011 Seville, Spain
2
Electrical, Electronics and Telecommunication Engineering and Naval Architecture Department (DITEN), Savona Campus, University of Genoa, 17100 Savona, Italy
*
Author to whom correspondence should be addressed.
Academic Editors: Pierluigi Siano and Hassan Haes Alhelou
Appl. Sci. 2021, 11(14), 6420; https://doi.org/10.3390/app11146420
Received: 20 June 2021 / Revised: 9 July 2021 / Accepted: 9 July 2021 / Published: 12 July 2021
Short-term electric power forecasting is a tool of great interest for power systems, where the presence of renewable and distributed generation sources is constantly growing. Specifically, this type of forecasting is essential for energy management systems in buildings, industries and microgrids for optimizing the operation of their distributed energy resources under different criteria based on their expected daily energy balance (the consumption–generation relationship). Under this situation, this paper proposes a complete framework for the short-term multistep forecasting of electric power consumption and generation in smart grids and microgrids. One advantage of the proposed framework is its capability of evaluating numerous combinations of inputs, making it possible to identify the best technique and the best set of inputs in each case. Therefore, even in cases with insufficient input information, the framework can always provide good forecasting results. Particularly, in this paper, the developed framework is used to compare a whole set of rule-based and machine learning techniques (artificial neural networks and random forests) to perform day-ahead forecasting. Moreover, the paper presents and a new approach consisting of the use of baseline models as inputs for machine learning models, and compares it with others. Our results show that this approach can significantly improve upon the compared techniques, achieving an accuracy improvement of up to 62% over that of a persistence model, which is the best of the compared algorithms across all application cases. These results are obtained from the application of the proposed methodology to forecasting five different load and generation power variables for the Savona Campus at the University of Genova in Italy. View Full-Text
Keywords: short-term forecasting; multistep forecasting; artificial neural networks; renewable energy sources; smart grids; microgrids short-term forecasting; multistep forecasting; artificial neural networks; renewable energy sources; smart grids; microgrids
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MDPI and ACS Style

Parejo, A.; Bracco, S.; Personal, E.; Larios, D.F.; Delfino, F.; León, C. Short-Term Power Forecasting Framework for Microgrids Using Combined Baseline and Regression Models. Appl. Sci. 2021, 11, 6420. https://doi.org/10.3390/app11146420

AMA Style

Parejo A, Bracco S, Personal E, Larios DF, Delfino F, León C. Short-Term Power Forecasting Framework for Microgrids Using Combined Baseline and Regression Models. Applied Sciences. 2021; 11(14):6420. https://doi.org/10.3390/app11146420

Chicago/Turabian Style

Parejo, Antonio, Stefano Bracco, Enrique Personal, Diego F. Larios, Federico Delfino, and Carlos León. 2021. "Short-Term Power Forecasting Framework for Microgrids Using Combined Baseline and Regression Models" Applied Sciences 11, no. 14: 6420. https://doi.org/10.3390/app11146420

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