@misc{10481/71601,
year = {2021},
url = {http://hdl.handle.net/10481/71601},
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.},
organization = {Deanship of Scientific Research (DSR), King Abdulaziz
University, Jeddah, under grant No. (D1441‐135‐626)},
publisher = {MDPI},
keywords = {Solar PV module},
keywords = {Performance prediction},
keywords = {Simulation},
keywords = {Self‐consumption model},
keywords = {RSM},
keywords = {Adaptive neuro-fuzzy inference system (ANFIS)},
keywords = {Hospitales},
title = {Designing a Solar Photovoltaic System for Generating   Renewable Energy of a Hospital: Performance Analysis and Adjustment Based on RSM and ANFIS Approaches},
doi = {10.3390/math9222929},
author = {Alamoudi, Rami and Taylan, Osman and Azmi Aktacir, Mehmet and Herrera Viedma, Enrique},
}