Short-term photovoltaic power production forecasting based on novel hybrid data-driven models

• Published in: Journal of big data Vol. 10; no. 1; pp. 26 - 25
• Main Authors: Alrashidi, Musaed, Rahman, Saifur
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.12.2023; Springer Nature B.V; SpringerOpen
• Subjects: Artificial neural networks; Big Data; Communications Engineering; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Database Management; Energy management systems; Feature selection; Forecasting; Heuristic methods; Hierarchies; Hyperparameters and architectures tuning; Information Storage and Retrieval; Machine learning; Mathematical Applications in Computer Science; Mathematical models; Metaheuristic Optimization Algorithms; Networks; Neural networks; Optimization algorithms; Particle swarm optimization; Photovoltaic cells; PV power forecast; Search algorithms; Support vector machines; Weather; Metaheuristic Optimization Algorithms; Hyperparameters and architectures tuning; Feature selection; PV power forecast; Machine learning
• ISSN: 2196-1115; 2196-1115
• DOI: 10.1186/s40537-023-00706-7

The uncertainty associated with photovoltaic (PV) systems is one of the core obstacles that hinder their seamless integration into power systems. The fluctuation, which is influenced by the weather conditions, poses significant challenges to local energy management systems. Hence, the accuracy of PV power forecasting is very important, particularly in regions with high PV penetrations. This study addresses this issue by presenting a framework of novel forecasting methodologies based on hybrid data-driven models. The proposed forecasting models hybridize Support Vector Regression (SVR) and Artificial Neural Network (ANN) with different Metaheuristic Optimization Algorithms, namely Social Spider Optimization, Particle Swarm Optimization, Cuckoo Search Optimization, and Neural Network Algorithm. These optimization algorithms are utilized to improve the predictive efficacy of SVR and ANN, where the optimal selection of their hyperparameters and architectures plays a significant role in yielding precise forecasting outcomes. In addition, the proposed methodology aims to reduce the burden of random or manual estimation of such paraments and improve the robustness of the models that are subject to under and overfitting without proper tuning. The results of this study exhibit the superiority of the proposed models. The proposed SVR models show improvements compared to the default SVR models, with Root Mean Square Error between 12.001 and 50.079%. Therefore, the outcomes of this research work can uphold and support the ongoing efforts in developing accurate data-driven models for PV forecasting.