Enhanced Random Forest Model for Robust Short-Term Photovoltaic Power Forecasting Using Weather Measurements

Mohamed Massaoudi, Ines Chihi, Lilia Sidhom, Mohamed Trabelsi, Shady S. Refaat and Fakhreddine S. Oueslati
Additional contact information
Mohamed Massaoudi: Department of Electrical and Computer Engineering, Texas A&M University at Qatar, Doha 3263, Qatar
Ines Chihi: Département Ingénierie, Faculté des Sciences, des Technologies et de Médecine, Campus Kirchberg, Université du Luxembourg, 1359 Luxembourg, Luxembourg
Lilia Sidhom: Laboratory of Energy Applications and Renewable Energy Efficiency (LAPER), El Manar University, Tunis 1068, Tunisia
Mohamed Trabelsi: Department of Electronic and Communications Engineering, Kuwait College of Science and Technology, Doha District, Block 4, Doha P.O. Box 27235, Kuwait
Shady S. Refaat: Department of Electrical and Computer Engineering, Texas A&M University at Qatar, Doha 3263, Qatar
Fakhreddine S. Oueslati: Laboratoire Matériaux Molécules et Applications (LMMA) à l’IPEST, Carthage University, Tunis 1054, Tunisia

Energies, 2021, vol. 14, issue 13, 1-20

Abstract: Short-term Photovoltaic (PV) Power Forecasting (STPF) is considered a topic of utmost importance in smart grids. The deployment of STPF techniques provides fast dispatching in the case of sudden variations due to stochastic weather conditions. This paper presents an efficient data-driven method based on enhanced Random Forest (RF) model. The proposed method employs an ensemble of attribute selection techniques to manage bias/variance optimization for STPF application and enhance the forecasting quality results. The overall architecture strategy gathers the relevant information to constitute a voted feature-weighting vector of weather inputs. The main emphasis in this paper is laid on the knowledge expertise obtained from weather measurements. The feature selection techniques are based on local Interpretable Model-Agnostic Explanations, Extreme Boosting Model, and Elastic Net. A comparative performance investigation using an actual database, collected from the weather sensors, demonstrates the superiority of the proposed technique versus several data-driven machine learning models when applied to a typical distributed PV system.

Keywords: smart grid; Photovoltaic (PV) Power Forecasting; weather sensors; random decision forest; feature importance; energy management (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: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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