Assessment of Artificial Neural Networks Learning Algorithms and Training Datasets for Solar Photovoltaic Power Production Prediction

Frontiers in Energy Research

Sameer Al-Dahidi¹  Osama Ayadi²  Mohamed Louzazni³  Jehad Adeeb⁴ 

[1] Department of Mechanical and Maintenance Engineering, School of Applied Technical Sciences, German Jordanian University, Amman, Jordan;Mechanical Engineering Department, Faculty of Engineering, The University of Jordan, Amman, Jordan;National School of Applied Sciences, Abdelmalek Essaadi University, Tétouan, Morocco;Renewable Energy Center, Applied Science Private University, Amman, Jordan;

关键词: solar photovoltaic;    power prediction;    Artificial Neural Networks;    learning algorithms;    training datasets;    persistence;   

DOI  :  10.3389/fenrg.2019.00130

来源: DOAJ

【 摘 要 】

The capability of accurately predicting the Solar Photovoltaic (PV) power productions is crucial to effectively control and manage the electrical grid. In this regard, the objective of this work is to propose an efficient Artificial Neural Network (ANN) model in which 10 different learning algorithms (i.e., different in the way in which the adjustment on the ANN internal parameters is formulated to effectively map the inputs to the outputs) and 23 different training datasets (i.e., different combinations of the real-time weather variables and the PV power production data) are investigated for accurate 1 day-ahead power production predictions with short computational time. In particular, the correlations between different combinations of the historical wind speed, ambient temperature, global solar radiation, PV power productions, and the time stamp of the year are examined for developing an efficient solar PV power production prediction model. The investigation is carried out on a 231 kWac grid-connected solar PV system located in Jordan. An ANN that receives in input the whole historical weather variables and PV power productions, and the time stamp of the year accompanied with Levenberg-Marquardt (LM) learning algorithm is found to provide the most accurate predictions with less computational efforts. Specifically, an enhancement reaches up to 15, 1, and 5% for the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R2) performance metrics, respectively, compared to the Persistence prediction model of literature.

【 授权许可】

Unknown