KPF-AE-LSTM: A Deep Probabilistic Model for Net-Load Forecasting in High Solar Scenarios. (arXiv:2203.04401v1 [eess.SP])

With the expected rise in behind-the-meter solar penetration within the
distribution networks, there is a need to develop time-series forecasting
methods that can reliably predict the net-load, accurately quantifying its
uncertainty and variability. This paper presents a deep learning method to
generate probabilistic forecasts of day-ahead net-load at 15-min resolution, at
various solar penetration levels. Our proposed deep-learning based architecture
utilizes the dimensional reduction, from a higher-dimensional input to a
lower-dimensional latent space, via a convolutional Autoencoder (AE). The
extracted …

ae arxiv forecasting lstm solar