International Journal on Advanced Science, Engineering and Information Technology, Vol. 12 (2022) No. 2, pages: 460-469, DOI:10.18517/ijaseit.12.2.15291

Determination of the Appropriate Number of Photovoltaic Panels for Microgeneration and Self-supply of Final Consumers by Energy Production Estimation via Fuzzy Logic

Cristhian Chávez, Juan David Ramírez, María Fernanda Trujillo L., Patricia Otero, Sebastián Taco-Vásquez, Víctor Tibanlombo

Abstract

A method is presented to determine the appropriate number of photovoltaic panels that should be installed in an end-user photovoltaic installation to guarantee the supply of energy to the load during the hours of solar radiation, according to factors such as the installation area and global solar radiation. Solar radiation is predicted by approximating the daily distribution of global irradiance through a Gaussian function, which is subsequently corrected using a heuristic approach. Meteorological parameters are used as input data such as the daily solar insolation and the maximum global irradiance for each day; this last parameter is obtained through an expert system based on fuzzy logic that was programmed and trained with the data of ambient temperature and relative humidity that were obtained in the processing stage. Output from this expert system is the predicted values of maximum radiation obtained for each day for a selectable time interval. With the predicted solar radiation, the generation of electrical energy from the photovoltaic panels is calculated. The load is randomly modeled from a pattern of the energy demand of the building to be powered by the photovoltaic system. The number of photovoltaic panels needed is found with the information acquired in the previous stages and the information of the energy demand of the load and the installation area. The results are the number of solar panels that would be needed at all hours of the day from which the radiation prediction was made.

Keywords:

Fuzzy logic; prediction of solar radiation; photovoltaic systems; capital recovery time; energy production estimation.

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