Deep and Machine Learning Models to Forecast Photovoltaic Power Generation

Sergio Cantillo-Luna, Ricardo Moreno-Chuquen (), David Celeita and George Anders
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Sergio Cantillo-Luna: Faculty of Engineering, Universidad Autónoma de Occidente, Cali 760030, Colombia
Ricardo Moreno-Chuquen: Faculty of Engineering and Design, Universidad Icesi, Cali 760031, Colombia
David Celeita: School of Engineering, Science and Technology, Universidad del Rosario, Bogotá 111221, Colombia
George Anders: Faculty of Engineering, Technical University of Lodz, 90-924 Lodz, Poland

Energies, 2023, vol. 16, issue 10, 1-24

Abstract: The integration and management of distributed energy resources (DERs), including residential photovoltaic (PV) production, coupled with the widespread use of enabling technologies such as artificial intelligence, have led to the emergence of new tools, market models, and business opportunities. The accurate forecasting of these resources has become crucial to decision making, despite data availability and reliability issues in some parts of the world. To address these challenges, this paper proposes a deep and machine learning-based methodology for PV power forecasting, which includes XGBoost, random forest, support vector regressor, multi-layer perceptron, and LSTM-based tuned models, and introduces the ConvLSTM1D approach for this task. These models were evaluated on the univariate time-series prediction of low-volume residential PV production data across various forecast horizons. The proposed benchmarking and analysis approach considers technical and economic impacts, which can provide valuable insights for decision-making tools with these resources. The results indicate that the random forest and ConvLSTM1D model approaches yielded the most accurate forecasting performance, as demonstrated by the lowest RMSE, MAPE, and MAE across the different scenarios proposed.

Keywords: deep learning; machine learning; PV power forecasting; time-series analysis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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