Özet:

Malaysia has been strongly dependent on non-renewable energy such as coal and natural gas to power up the nation. As the nation natural source started to deplete, solar energy is seen as the most suitable future energy source due to Malaysia position located at the equator of the Earth. However, it is hard for independent power producer to forecast the output power from the photovoltaic (PV) system due to the uncertainty of weather condition. This paper presents the forecasting generation of UiTM 50MW Gambang Large Scale Solar (LSS) farm located in Gambang, Pahang by using Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) technique. The PSO technique is used to optimize the weight of ANN to get the best Mean Square Error (MSE) and regression performance. This forecasting model uses total global horizontal irradiance, global irradiation on the module plan and PV module temperature as input variable while Alternating Current (AC) output power as output variable. The historical data used in the training and testing process are from the month of May 2019 until August 2019 and is divided at a ratio of 60/40. The data is forecasted at every 30 minutes’ basis and is compared with the actual AC output power. The result shows that the ANN-PSO method outperforms the traditional ANN having better MSE and regression performance.<

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