Energy Reports (Nov 2022)
Ten-minute prediction of solar irradiance based on cloud detection and a long short-term memory (LSTM) model
Abstract
With the inherent volatility of solar radiation, accurate short-term prediction of solar irradiance is essential to cost-efficiently stabilise and operate power grids. Cloud is the major influencing factor for variations in solar irradiance at a minute scale. This study proposes a short-term solar irradiance prediction model based on a deep learning network. Firstly, a new hybrid cloud detection method is proposed to calculate cloud coverage under different sky condition. The relationship between cloud coverage at the current moment and the global horizontal irradiance (GHI) 10 min later is directly analysed to avoid the error of assuming cloud motion as rigid motion. Meteorological parameters with strong correlation with the GHI in ten minutes are admitted into the model input, mainly including relative humidity and Aerosol Optical Depth (AOD) of 500 nm in this study. The GHI of two-time intervals ahead are also selected after autocorrelation analysis to represent other comprehensive impacts. Together with cloud, the meteoritical parameters and the historical GHI are taken as the input of a deep learning network simultaneously, which is based on long short-term memory network (LSTM) and optimised by Bayesian Optimisation (BO). The solar irradiance after ten minutes is the output of the model. Experiments were conducted to evaluate the performance of the proposed forecast model comparing with some benchmark models. Results showed that the proposed model outperformed other models with a normalised root mean square error (nRMSE) of 15.25% under all sky conditions. An improvement of 8.23% was reached compared with persistent model.
