Short-term solar irradiance forecasting under data transmission constraints

We report a data-parsimonious machine learning model for short-term forecasting of solar irradiance. The model follows the convolutional neural network – long-short term memory architecture. Its inputs include sky camera images that are reduced to scalar features to meet data transmission constraints. The model focuses on predicting the deviation of irradiance from the persistence of cloudiness (POC) model. Inspired by control theory, a noise signal input is used to capture the presence of unknown and/or unmeasured input variables and is shown to improve model predictions, often considerably. Five years of data from the NREL Solar Radiation Research Laboratory were used to create three rolling train-validate sets and determine the best representations for time, the optimal span of input measurements, and the most impactful model input data (features). For the chosen validation data, the model achieves a mean absolute error of 74.29 W/m2over a time horizon of up to two hours, compared to a baseline 134.35 W/m2 using the POC model. •A data-efficient model predicts solar irradiance for remote locations.•Accuracy improves as we predict deviation from persistence of cloudiness.•A novel noise model accounts for unmeasured and dropped variables.•A feature importance and input span sensitivity study.