Assessing Broadband and Spectral Irradiance Variability for Solar Nowcasting Using Statistical Analysis and Machine Learning

Abstract

Solar photovoltaic (PV) resources are a key enabling technology in the global energy transition towards a more sustainable future. However, PV generation is highly variable due to the dynamic shading caused by clouds. To mitigate the effects of PV variability on electrical grid stability, grid operators rely on solar forecasts to proactively dispatch grid assets and balance supply and demand. To gain insights into the nature of solar variability, which is key for effective solar forecasting, this thesis presents a statistical assessment of high resolution spectral and broadband solar irradiance in Ottawa, Canada. The statistical assessment investigates the first- and second-order spectral and temporal dependencies of irradiance time series within the context of stationarity. The temporal structures indicate that solar irradiance processes are at best weakly stationary, and the implications for forecasting are discussed. The results of the statistical assessment are leveraged to develop several deterministic machine learning solar forecasting models (LSTM, XGBoost, and 1D-CNN). These models are implemented and compared in terms of computational complexity and prediction accuracy. It was found that under all sky conditions, the inclusion of spectral irradiance data improved forecasting performance compared to only using broadband irradiance. A ramp regime classification algorithm is then described, which enables the training and testing specialized ramp regime forecasting sub-models. These specialized sub-models were found to yield even greater forecasting accuracy within their respective ramp regimes, compared with the all-sky models. Further optimization and ensembling of the presented solar forecasting models is recommended for future work.