Abstract
Predicting the power obtained at the output of the photovoltaic (PV) system is fundamental for the optimum use of the PV system. However, it varies at different times of the day depending on intermittent and nonlinear environmental conditions including solar irradiation, temperature and the wind speed, Short-term power prediction is vital in PV systems to reconcile generation and demand in terms of the cost and capacity of the reserve. In this study, a Gaussian kernel based Support Vector Regression (SVR) prediction model using multiple input variables is proposed for estimating the maximum power obtained from using perturb observation method in the different irradiation and the different temperatures for a short-term in the DC-DC boost converter at the PV system. The performance of the kernel-based prediction model depends on the availability of a suitable kernel function that matches the learning objective, since an unsuitable kernel function or hyper parameter tuning results in significantly poor performance. In this study for the first time in the literature both maximum power is obtained at maximum power point and short-term maximum power estimation is made. While evaluating the performance of the suggested model, the PV power data simulated at variable irradiations and variable temperatures for one day in the PV system simulated in MATLAB were used. The maximum power obtained from the simulated system at maximum irradiance was 852.6 W. The accuracy and the performance evaluation of suggested forecasting model were identified utilizing the computing error statistics such as root mean square error (RMSE) and mean square error (MSE) values. MSE and RMSE rates which obtained were 4.5566 * 10−04 and 0.0213 using ANN model. MSE and RMSE rates which obtained were 13.0000 * 10−04 and 0.0362 using SWD-FFNN model. Using SVR model, 1.1548 * 10−05 MSE and 0.0034 RMSE rates were obtained. In the short-term maximum power prediction, SVR gave higher prediction performance according to ANN and SWD-FFNN.
Highlights
Since fossil energy causes air pollution and is exhaustible, renewable energy is more widely used
It varies at different times of the day depending on intermittent and nonlinear environmental conditions including solar irradiation, temperature and the wind speed, Shortterm power prediction is vital in PV systems to reconcile generation and demand in terms of the cost and capacity of the reserve
There are only algorithms where the maximum power is obtained at the Maximum Power Point (MPP), and there are only algorithms with short-term power estimation
Summary
Since fossil energy causes air pollution and is exhaustible, renewable energy is more widely used. The researchers have proposed many machine learning methods based on support vector machine (SVM) [8,9,10,11], artificial neural network (ANN), [12,13,14,15] and hybrid algorithms [16,17,18] for modeling and predicting power output of PV systems. One other study proposed a hybrid model founded on the Swarm Decomposition Technique and Feed Forward Neural Network (SWD-FFNN) for the 15-min very short term solar photovoltaic energy generation prediction [16]. A new power prediction model using a Gaussian kernel based SVR is proposed for estimating the maximum power obtained from the DC-DC boost converter for a short-term. The conclusion of the study is presented in the Section 4
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