INOVASI DALAM ENERGI TERBARUKAN: JARINGAN SYARAF TIRUAN UNTUK MERAMALKAN DAYA FOTOVOLTAIK

Variations in meteorological conditions cause intermittency, voltage spikes, and feedback power flow, impacting the uncertainty of photovoltaic output, which affects the reliability, stability, and scheduling of photovoltaic operations. Optimal prediction of photovoltaic output power is necessary in the planning and operation of power systems. Photovoltaic technology is utilized to generate electrical energy from direct sunlight. Climatic factors such as cloud cover, humidity, and wind speed also contribute to the electrical energy produced by solar modules. This research discusses a model for predicting photovoltaic output power for the next day using the backpropagation neural network method and multi-factor analysis. In this study, there are 2 different input neurons with 10 predetermined network architectures, a learning rate of 0.1, and a minimum target error of 0.001. The best performance prediction results with the smallest Mean Squared Error (MSE) value were obtained using a backpropagation neural network structure from the 5-20-1 model, which is almost close to the actual value. However, further research is needed to improve the prediction results.