Macroeconomics

Quantum mechanics

Engineering

Stability (learning theory)

Economics

Power (physics)

Computer science

Physics

Electric power system

Power grid

Term (time)

Photovoltaic system

Machine learning

Production (economics)

Electrical engineering

Applied Sciences

Photovoltaic (PV) power is attracting more and more concerns. Power output prediction, as a necessary technical requirement of PV plants, closely relates to the rationality of power grid dispatch. If the accuracy of power prediction in PV plants can be further enhanced by forecasting, stability of power grid will be improved. Therefore, a 1-h-ahead power output forecasting based on long-short-term memory (LSTM) networks is proposed. The forecasting output of the model is based on the time series of 1-h-ahead numerical weather prediction to reveal the spatio-temporal characteristic. The comprehensive meteorological conditions, including different types of season and weather conditions, were considered in the model, and parameters of LSTM models were investigated simultaneously. Analysis of prediction result reveals that the proposed model leads to a superior prediction performance compared with traditional PV output power predictions. The accuracy of output power prediction is enhanced by 3.46–13.46%.

Short-Term Forecasting of Power Production in a Large-Scale Photovoltaic Plant Based on LSTM