Solar Prediction Strategy for Managing Virtual Power Stations
نویسندگان
چکیده
The potential for solar power is available in Indonesia because it located on the equator, with good sunshine all year round. Indonesian government currently actively developing a plant while still looking at consequences of development, especially surrounding environment. It necessary to pay attention so that does not disturb environment, which can also cause climate change. city Medan one largest cities Indonesia, has direct exposure sunlight quite promising predicting plants future. Solar energy generation last decade continued improve and develop predictions short period. Integration sources without accurate hinder network operations use renewable sources. To solve this problem, virtual modeling as solution manages minimize prediction error. This research studies methods efficiently generate significant daily Photovoltaic (PV) locations studied using data from Meteorology, Climatology, Geophysics Agency (MCGA). approach two models based RMSE (root mean square error) MAE (Mean Absolute Error), be management minimal its taking into account uncertainty provide additional information plants. Verified strategy performance against PV module output set geographic meteorological station have been used simulate Virtual Power Plants (VPP). forecasting refers LSTM (Long Short-Term Memory) gives an error close other learning methods, RMS characteristic 4.19 W/m2 under lead time different launch times. application VPP model reduce global by about 12.37% RMSE, shows great potential.
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ژورنال
عنوان ژورنال: International Journal of Energy Economics and Policy
سال: 2023
ISSN: ['2146-4553']
DOI: https://doi.org/10.32479/ijeep.14124