EVALUATION OF HYBRID SOLAR PHOTOVOLTAIC-GAS TURBINE SYSTEM USING ARTIFICIAL NEURAL NETWORK - MOHAMED SHAABAN KHAMIS, MOHAMED ATEF (2020)
Solar energy one of the most available power source in Malaysia. The economic impact was the main target of this investigation with the fulfilment of the technical requirements of the Universiti Teknologi PETRONAS (UTP) Microgrid (MG). Since hybrid Solar Photovoltaic (SPV) and Gas Turbine Generator (GTG) (H-PVGTGs) will be considered in this research, the former models’ accuracy (in hybrid SPV optimization techniques) is very important. The least error for the existing mathematical models for SPV is 5.5% and this figure hypothetically can be further improved by using Artificial Intelligent (AI) method. There is always potential Annualized Total Life Cycle Cost (ATLCC) to be improved for the feasibility options.