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Electricity Generation Forecasting in Concentrating Solar-Thermal Power Plants with Ensemble Learning

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16th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2021) (SOCO 2021)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1401))

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Abstract

Electric power generation forecast systems in concentrating solar-thermal power plants are a key tool for their operation and maintenance optimization. The purpose of this work is to approach the problem of electric power prediction in Arenales concentrating solar-thermal plant (Sevilla, Spain). Throughout this work, the standard phases in the knowledge discovery in databases are followed, resulting in three different models for the hourly electric power forecasting, with a 24-h prediction horizon. Each model is based on a different algorithm: Extra Gradient Boosting, K-Nearest Neighbors, and a Multi-Layer Perceptron neural network. The fitness of the models is assessed by some of the most common error metrics in regression problems with a satisfactory result. Additionally, it is shown how the results obtained in the prediction of hourly energy give rise to also evaluate the daily and the aggregate energy prediction in a wide time interval. After an individual analysis of each model, a comparative study is included with the aim of determining the best performance model.

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Acknowledgements

The authors would like to thank the Spanish Ministry of Economy and Competitiveness for the support under project TIN2017-88209.

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Correspondence to F. Martínez-Álvarez .

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Melara, A., Torres, J.F., Troncoso, A., Martínez-Álvarez, F. (2022). Electricity Generation Forecasting in Concentrating Solar-Thermal Power Plants with Ensemble Learning. In: Sanjurjo González, H., Pastor López, I., García Bringas, P., Quintián, H., Corchado, E. (eds) 16th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2021). SOCO 2021. Advances in Intelligent Systems and Computing, vol 1401. Springer, Cham. https://doi.org/10.1007/978-3-030-87869-6_63

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