Designing a Solar Photovoltaic System for Generating Renewable Energy of a Hospital: Performance Analysis and Adjustment Based on RSM and ANFIS Approaches

Rami Alamoudi, Osman Taylan, Mehmet Azmi Aktacir and Enrique Herrera-Viedma
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Rami Alamoudi: Department of Industrial Engineering, Faculty of Engineering, King Abdulaziz University, P.O. Box 80204, Jeddah 21589, Saudi Arabia
Osman Taylan: Department of Industrial Engineering, Faculty of Engineering, King Abdulaziz University, P.O. Box 80204, Jeddah 21589, Saudi Arabia
Mehmet Azmi Aktacir: GAP Renewable Energy and Energy Efficiency Center, Department of Mechanical Engineering, Osmanbey Campus, Harran University, 63100 Sanliurfa, Turkey
Enrique Herrera-Viedma: Andalusian Research Institute in Data Science and Computational Intelligence, University of Granada, 18071 Granada, Spain

Mathematics, 2021, vol. 9, issue 22, 1-24

Abstract: One of the most favorable renewable energy sources, solar photovoltaic (PV) can meet the electricity demand considerably. Sunlight is converted into electricity by the solar PV systems using cells containing semiconductor materials. A PV system is designed to meet the energy needs of King Abdulaziz University Hospital. A new method has been introduced to find optimal working capacity, and determine the self-consumption and sufficiency rates of the PV system. Response surface methodology (RSM) is used for determining the optimal working conditions of PV panels. Similarly, an adaptive neural network based fuzzy inference system (ANFIS) was employed to analyze the performance of solar PV panels. The outcomes of methods were compared to the actual outcomes available for testing the performance of models. Hence, for a 40 MW target PV system capacity, the RSM determined that approximately 33.96 MW electricity can be produced, when the radiation rate is 896.3 W/m 2 , the module surface temperature is 41.4 °C, the outdoor temperature is 36.2 °C, the wind direction and speed are 305.6 and 6.7 m/s, respectively. The ANFIS model (with nine rules) gave the highest performance with lowest residual for the same design parameters. Hence, it was determined that the hourly electrical energy requirement of the hospital can be met by the PV system during the year.

Keywords: solar PV module; performance prediction; simulation; self-consumption model; RSM; ANFIS; hospital (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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