Özet:

Sustainable energy obtained from solar energy has become one of the important sources in meeting the energy demand in residential, commercial and industrial applications. However, the main difficulty in generating electricity from solar energy is the instantaneous changes in power caused by weather conditions in photovoltaic (PV) power plants. For large-scale solar PV power plants, power imbalances electrically negatively affect system efficiency and stability. Therefore, accurate forecasting of the output power of PV power plants in the short term is of great importance for efficient daily/hourly management of electricity grid generation, distribution and storage and for decision making in the energy market. This paper aims to develop a population-based algorithm estimating the power generation of a PV power plant based on the cultural transition target. Also, short-term forecasting is carried out based on the Particle Swarm Optimization (PSO) method, with the feature of allowing faster convergence by considering all the variables in each iteration. In order to obtain the results of short-term PV panel output power estimation with the least error rate, a hybrid method was created by using the multilayer artificial neural network model with PSO and Cultural Algorithm (CA). Since the CA provides faster convergence than evolutionary algorithms with the ability to store and use the information collected during iteration, effective results are obtained from the short-term forecasting of the PV energy output power.

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