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

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/m2, 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