Distributed solar generation forecasting using attention-based deep neural networks for cloud movement prediction

TL;DR

This paper introduces an attention-based deep neural network approach for predicting cloud movement from images to improve short-term solar power forecasts, especially for high-altitude clouds.

Contribution

It applies an attention-based convolutional LSTM network for cloud movement prediction, demonstrating improved solar forecast accuracy over non-attention methods.

Findings

Attention-based methods improve high-altitude cloud forecast skill by over 5.86%.

Cloud movement prediction enhances distributed solar generation forecasting.

The approach offers insights into different performances for high and low altitude clouds.

Abstract

Accurate forecasts of distributed solar generation are necessary to reduce negative impacts resulting from the increased uptake of distributed solar photovoltaic (PV) systems. However, the high variability of solar generation over short time intervals (seconds to minutes) caused by cloud movement makes this forecasting task difficult. To address this, using cloud images, which capture the second-to-second changes in cloud cover affecting solar generation, has shown promise. Recently, deep neural networks with "attention" that focus on important regions of an image have been applied with success in many computer vision applications. However, their use for forecasting cloud movement has not yet been extensively explored. In this work, we propose an attention-based convolutional long short-term memory network to forecast cloud movement and apply an existing self-attention-based method previously proposed for video prediction to forecast cloud movement. We investigate and discuss the impact of cloud forecasts from attention-based methods towards forecasting distributed solar generation, compared to cloud forecasts from non-attention-based methods. We further provide insights into the different solar forecast performances that can be achieved for high and low altitude clouds. We find that for clouds at high altitudes, the cloud predictions obtained using attention-based methods result in solar forecast skill score improvements of 5.86% or more compared to non-attention-based methods.