Photovoltaic Power Forecasting Using Multiscale-Model-Based Machine Learning Techniques

Manel Marweni, Mansour Hajji, Majdi Mansouri () and Mohamed Fouazi Mimouni
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Manel Marweni: Laboratory of Automatic Electrical Systems and Environment, National Engineering School of Monastir, University of Monastir, Monastir 5000, Tunisia
Mansour Hajji: Research Unit Advanced Materials and Nanotechnologies, Higher Institute of Applied Sciences and Technology of Kasserine, Kairouan University, Kasserine 1200, Tunisia
Majdi Mansouri: Electrical and Computer Engineering Program, Texas A&M University at Qatar, Doha 23874, Qatar
Mohamed Fouazi Mimouni: Laboratory of Automatic Electrical Systems and Environment, National Engineering School of Monastir, University of Monastir, Monastir 5000, Tunisia

Energies, 2023, vol. 16, issue 12, 1-16

Abstract: The majority of energy sources being used today are traditional types. These sources are limited in nature and quantity. Additionally, they are continuously diminishing as global energy consumption increases as a result of population growth and industrial expansion. Their compensation is made from clean energy and renewable energy. Renewable energy is strongly dependent on climatic conditions; therefore, an aspect of energy management is needed, which is essential in distribution systems, because it enables us to calculate the precise energy used by the load as well as by its many components. It also helps us understand how much energy is required and its origin. The energy management aspect contains two main phases: forecasting and optimization. In this study, we are focused on the forecasting level using intelligent machine learning (ML) techniques. To ensure better energy management, it is very important to predict the production of renewable energy over a wide time period. In our work, several cases are proposed in order to predict the temperature, the irradiance, and the power produced by a PV system. The proposed approach is validated by an experimental procedure and a real database for a PV system. The big data from the sensors are noisy, which pose a major problem for forecasting. To reduce the impact of noise, we applied the multiscale strategy. To evaluate this strategy, we used different performance criteria, such as mean error (ME), mean absolute error (MAE), root mean square error (RMSE), nRMSE and the coefficient of determination ( R 2 ). The obtained experimental results show good performance with lower error. Indeed, they achieved an error for nRMSE criteria between 0.01 and 0.37.

Keywords: photovoltaic (PV); energy management (EM); forecasting; stand-alone PV system (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 (1)

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