Abstract
Solar energy is a safe, clean, environmentally-friendly and renewable energy source without any carbon emissions to the atmosphere. Therefore, there are many studies in the field of solar energy in order to obtain the maximum solar radiation during the day time, to estimate the amount of solar energy to be produced, and to increase the efficiency of solar energy systems. In this study, it was aimed to predict the daily photovoltaic power production using air temperature, relative humidity, total horizontal solar radiation and diffuse horizontal solar radiation parameters as multi-tupled inputs. For this purpose, grey wolf, ant lion and whale optimization algorithms were integrated to the multilayer perceptron. In addition, the effects of sigmoid, sinus and hyperbolic tangent activation functions on the prediction performance were analyzed in detail. As a result of overall accuracy indictors achieved, the grey wolf optimization algorithm-based multilayer perceptron model was found to be more successful and competitive for the daily photovoltaic power prediction. Furthermore, many meaningful patterns were revealed about the constructed models, input tuples and activation functions.
Highlights
According to the Renewables 2019 Global Status Report [1], the global renewable power capacity reached 2.378 GW by the end of 2018 and more than 33% of world’s total power generation was covered by renewable energy sources
Different from the studies in the literature, the main contribution of this study is to develop the novel hybrid approaches by integrating grey wolf, ant lion and whale optimization algorithms with multilayer perceptron models, and to implement them for the first time in the daily photovoltaic power prediction
Study, grey antand lion and optimization whale optimization algorithms-based multilayer models were developed for the daily photovoltaic power prediction
Summary
According to the Renewables 2019 Global Status Report [1], the global renewable power capacity reached 2.378 GW by the end of 2018 and more than 33% of world’s total power generation was covered by renewable energy sources. Different from the studies in the literature, the main contribution of this study is to develop the novel hybrid approaches by integrating grey wolf, ant lion and whale optimization algorithms with multilayer perceptron models, and to implement them for the first time in the daily photovoltaic power prediction. Another important contribution is to employ the air temperature, relative humidity, total horizontal solar radiation and diffuse horizontal solar radiation parameters in 4-, 3- and 2-tupled meteorological input structure.
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