Advancing Renewable Energy Forecasting: A Comprehensive Review of Renewable Energy Forecasting Methods

Rita Teixeira, Adelaide Cerveira, Eduardo J. Solteiro Pires and José Baptista ()
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Rita Teixeira: Department of Engineering, University of Trás-os-Montes and Alto Douro, 5000-801 Vila Real, Portugal
Adelaide Cerveira: Department of Mathematics, University of Trás-os-Montes and Alto Douro, 5000-801 Vila Real, Portugal
Eduardo J. Solteiro Pires: Department of Engineering, University of Trás-os-Montes and Alto Douro, 5000-801 Vila Real, Portugal
José Baptista: Department of Engineering, University of Trás-os-Montes and Alto Douro, 5000-801 Vila Real, Portugal

Energies, 2024, vol. 17, issue 14, 1-30

Abstract: Socioeconomic growth and population increase are driving a constant global demand for energy. Renewable energy is emerging as a leading solution to minimise the use of fossil fuels. However, renewable resources are characterised by significant intermittency and unpredictability, which impact their energy production and integration into the power grid. Forecasting models are increasingly being developed to address these challenges and have become crucial as renewable energy sources are integrated in energy systems. In this paper, a comparative analysis of forecasting methods for renewable energy production is developed, focusing on photovoltaic and wind power. A review of state-of-the-art techniques is conducted to synthesise and categorise different forecasting models, taking into account climatic variables, optimisation algorithms, pre-processing techniques, and various forecasting horizons. By integrating diverse techniques such as optimisation algorithms and pre-processing methods and carefully selecting the forecast horizon, it is possible to highlight the accuracy and stability of forecasts. Overall, the ongoing development and refinement of forecasting methods are crucial to achieve a sustainable and reliable energy future.

Keywords: forecasting; meteorological variables; renewable energy; machine learning; algorithms; pre-processing (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: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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