Short-term Solar PV Forecasting Using Computer Vision: the Search for Optimal CNN Architectures for Incorporating Sky Images and PV Generation History

Cloud movement makes short-term forecasting of solar photovoltaic (PV) panel output challenging. A better PV forecast can realize value for both grid operators and commercial or industrial customers with solar assets. In this study, we build convolutional neural network (CNN) based models to forecast power output from PV panels 15 min into the future. Model inputs are the PV power output history and ground-based sky images for the past 15 min. The key challenge is ensuring that due importance is given to each type of input. We systematically explore 28 methods of "fusing" these heterogeneous inputs in our CNN. These methods of fusion (MoF) belong to 4 families. We also systematically explore the many hyperparameters related to model training and tuning. Limited resources preclude an exhaustive search. We apply a three-stage "funnel" approach instead, wherein we narrow our search to the most promising one of these 28 MoF. We find that a two-step autoregression-CNN MoF has the best performance followed closely by a "mix-in" MoF that performs feature expansion and reduction to give appropriate importance to the two types of inputs. The two-step autoregression-CNN model has a forecast skill (FS) of 17.1% relative to smart persistence on the test set comprising 20 complete days (9 sunny, FS = 22%; 11 cloudy, FS = 16.9%). This optimization results in the improvement of FS from 14.1% for a previously published nonoptimized "baseline" model, a CNN wherein the PV history was simply concatenated to the end of the image-sourced vector obtained after convolution, pooling, and flattening operations.