Determining of Solar Power by Using Machine Learning Methods in a Specified Region

In this study, it is aimed to estimate the solar power according to the hourly meteorological data of the specified location measured between 2002 and 2006 by using different Machine Learning (ML) algorithms. Data Mining Processes (DMP) were used to select the most appropriate input variables from these measured data. Data groups created using DMP were evaluated according to three different ML algorithms such as Artificial Neural Network (ANN), Support Vector Regression (SVR) and K-Nearest Neighbors (KNN). It can be concluded that DMP-ML based prediction models are more successful than models developed using all available data. The most successful model developed among these models estimated the hourly solar power potential with an accuracy of 97%. Also, different error measurement statistics were used to evaluate ML algorithms. According to Symmetric Mean Absolute Percentage Error, 6.12%, 7.22% and 12.72% values were found in the most successful prediction models developed using ANN, KNN and SVR, respectively. In addition, from the meteorological data used in this study the most effective data on solar power as a result of DMP were shown to be Temperature and Hourly Sunshine Duration

Technoeconomic Evaluation for an Installed Small-Scale Photovoltaic Power Plant

Solar energy production and economic evaluation are analyzed, in this study, by using daily solar radiation and average temperature data which are measured for 3 years in the Osmaniye province in Turkey. Besides, this study utilizes the photovoltaic- (PV-) based grid connected to a power plant which has an installed capacity of 1 MW investment in electricity production. Economic values show that the net present value (NPV), the first economic method in the research, is about 111941 USD, which is greater than zero. Therefore, the payback year of this investment is approximately 8.3. The second one of these methods, the payback period of the simple payback period (PBP), is 6.27 years. The last method, which is the mean value of the internal rate of return (IRR), is 10.36%. The results of this study show that Osmaniye is a considerable region for the PV investment in electricity production. As a result, investment of a PV system in Osmaniye can be applicable

Effective Estimation of Hourly Global Solar Radiation Using Machine Learning Algorithms

The precise estimation of solar radiation is of great importance in solar energy applications with respect to installation and capacity. In estimate modelling on selected target locations, various computer-based and experimental methods and techniques are employed. In the present study, the Multilayer Feed-Forward Neural Network (MFFNN), K-Nearest Neighbors (K-NN), a Library for Support Vector Machines (LibSVM), and M5 rules algorithms, which are among the Machine Learning (ML) algorithms, were used to estimate the hourly average solar radiation of two geographic locations on the same latitude. The input variables that had the most impact on solar radiation were identified and grouped as a result of 29 different applications that were developed by using 6 different feature selection methods with Waikato Environment for Knowledge Analysis (WEKA) software. Estimation models were developed by using the selected data groups and all input variables for each target location. The results show that the estimations developed with the feature selection method were more successful for target locations, and the radiation potentials were similar. The performance of the estimation models was evaluated by comparing each model with different statistical indicators and with previous studies. According to the RMSE, MAE, R2, and SMAPE statistical scales, the results of the most successful estimation models that were developed with MFFNN were 0.0508-0.0536, 0.0341-0.0352, 0.9488-0.9656, and 7.77%-7.79%, respectively