The intermittent nature of solar irradiance, primarily due to cloud movements, leads to rapid short-term fluctuations in the power output of photovoltaic (PV) systems. These fluctuations pose a significant challenge for integrating this renewable energy source into the power grid. Accurate forecasting of solar irradiance is not only crucial but also multi-beneficial. It enables more precise grid management by allowing operators to anticipate power output fluctuations and adjust energy distribution and storage strategies accordingly. This proactive approach reduces the reliance on backup power sources, which are often less sustainable and more expensive. Furthermore, accurate forecasts enhance the overall efficiency and reliability of energy systems by minimizing the impact of power variability on the grid, thereby supporting a more stable and sustainable energy supply. Addressing this need, our study focuses on the development of a forecasting model through innovative feature engineering, systematic design of specific attributes, and optimization of sequence length. The model is tailored to perform efficiently across various weather conditions and offers predictions for a time horizon of 0 to 20 min ahead. Utilizing a Long Short-Term Memory (LSTM) model, we achieve a remarkable ramp Forecast Skill Score of 39% in sunny and 25% in partially cloudy conditions. This work not only contributes to the existing literature but also presents a pioneering methodology for solar energy integration, highlighting the importance and application of accurate short-term solar irradiance forecasting. 

All sky imaging-based short-term solar irradiance forecasting with Long Short-Term Memory networks

Sky-image-based solar forecasting using deep learning has been recognized as a promising approach in reducing the uncertainty in solar power generation. However, one of the biggest challenges is the lack of massive and diversified sky image samples. In this study, we present a comprehensive survey of open-source ground-based sky image datasets for very short-term solar forecasting (i.e., forecasting horizon less than 30 minutes), as well as related research areas which can potentially help improve solar forecasting methods, including cloud segmentation, cloud classification and cloud motion prediction. We first identify 72 open-source sky image datasets that satisfy the needs of machine/deep learning. Then a database of information about various aspects of the identified datasets is constructed. To evaluate each surveyed datasets, we further develop a multi-criteria ranking system based on 8 dimensions of the datasets which could have important impacts on usage of the data. Finally, we provide insights on the usage of these datasets for different applications. We hope this paper can provide an overview for researchers who are looking for datasets for very short-term solar forecasting and related areas.

Open-Source Ground-based Sky Image Datasets for Very Short-term Solar Forecasting, Cloud Analysis and Modeling: A Comprehensive Survey

Aiming at effectively improving photovoltaic (PV) park operation and the stability of the electricity grid, the current paper addresses the design and development of a novel system achieving the short-term irradiance forecasting for the PV park area, which is the key factor for controlling the variations in the PV power production. First, it introduces the Xception long short-term memory (XceptionLSTM) cell tailored for recurrent neural networks (RNN). Second, it presents the novel irradiance forecasting model that consists of a sequence-to-sequence image regression NNs in the form of a spatio-temporal encoder–decoder including Xception layers in the spatial encoder, the novel XceptionLSTM in the temporal encoder and decoder and a multilayer perceptron in the spatial decoder. The proposed model achieves a forecast skill of 16.57% for a horizon of 5 min when compared to the persistence model. Moreover, the proposed model is designed for execution on edge computing devices and the real-time application of the inference on the Raspberry Pi 4 Model B 8 GB and the Raspberry Pi Zero 2W validates the results.

Neural Network-Based Solar Irradiance Forecast for Edge Computing Devices

In recent years, deep learning-based solar forecasting using all-sky images has emerged as a promising approach for alleviating uncertainty in PV power generation. However, the stochastic nature of cloud movement remains a major challenge for accurate and reliable solar forecasting. With the recent advances in generative artificial intelligence, the synthesis of visually plausible yet diversified sky videos has potential for aiding in forecasts. In this study, we introduce \emph{SkyGPT}, a physics-informed stochastic video prediction model that is able to generate multiple possible future images of the sky with diverse cloud motion patterns, by using past sky image sequences as input. Extensive experiments and comparison with benchmark video prediction models demonstrate the effectiveness of the proposed model in capturing cloud dynamics and generating future sky images with high realism and diversity. Furthermore, we feed the generated future sky images from the video prediction models for 15-minute-ahead probabilistic solar forecasting for a 30-kW roof-top PV system, and compare it with an end-to-end deep learning baseline model SUNSET and a smart persistence model. Better PV output prediction reliability and sharpness is observed by using the predicted sky images generated with SkyGPT compared with other benchmark models, achieving a continuous ranked probability score (CRPS) of 2.81 (13\% better than SUNSET and 23\% better than smart persistence) and a Winkler score of 26.70 for the test set. Although an arbitrary number of futures can be generated from a historical sky image sequence, the results suggest that 10 future scenarios is a good choice that balances probabilistic solar forecasting performance and computational cost.

SkyGPT: Probabilistic Short-term Solar Forecasting Using Synthetic Sky Videos from Physics-constrained VideoGPT

Ground-based cloud image sequence prediction provides valuable insights into cloud motion and meteorological conditions, which are essential for photovoltaic power generation systems. Most existing models are, however, recurrent-based, which is problematic in providing satisfactory forecasting results with rapid speed because these recurrent-based models do not support parallel inference and usually suffer from slow inference speed. A novel recurrent-free deep learning-based framework, called multiscale spatiotemporal attention network (MSTANet) to address the issues is proposed in this study. The MSTANet leverages a multiscale spatiotemporal attention (MSTA) module to extract the multiscale, nonlinear spatiotemporal dependencies from cloud image sequences and uses a multiscale temporal attention (MTA) module to reinforce the temporal dependencies by capturing the high- and low-frequency spatiotemporal fluctuations of clouds. A gated aggregation unit (GAU) to mitigate the ghosting effects that are prevalent in spatiotemporal prediction tasks is introduced to filter the useful context information by integrating the historical information with the updated predictions. Additionally, a multiorder differential divergence regularization term is introduced into the loss function to improve the model’s performance by encouraging MSTANet to focus on the evolving trends of the neighborhood of clouds. Experimental results show that the proposed MSTANet outperforms the state-of-the-art (SOTA) prediction methods. It reduces 46% parameters and mean-squared-error (MSE) by 4.31% on the Moving Mnist dataset and reduces 22% parameters with a 1.82% performance improvement on the Folsom dataset compared to the baseline temporal attention unit (TAU). The codes are available at https://github.com/Csorasky/MSTANet.

A Multi-scale Spatiotemporal Attention Network for Ground-Based Remote Sensing Cloud Image Sequence Prediction

Solar forecasting from ground-based sky images has shown great promise in reducing the uncertainty in solar power generation. With more and more sky image datasets open sourced in recent years, the development of accurate and reliable deep learning-based solar forecasting methods has seen a huge growth in potential. In this study, we explore three diﬀerent training strategies for solar forecasting models by leveraging three heterogeneous datasets collected globally with diﬀerent climate patterns. Speciﬁcally, we compare the performance of local models trained individually based on single datasets and global models trained jointly based on the fusion of multiple datasets, and further examine the knowledge transfer from pre-trained solar forecasting models to a new dataset of interest. The results suggest that the local models work well when deployed locally, but signiﬁcant errors are observed when applied oﬀsite. The global model can adapt well to individual locations at the cost of a potential increase in training eﬀorts. Pre-training models on a large and diversiﬁed source dataset and transferring to a target dataset generally achieves superior performance over the other two strategies. With 80% less training data, it can achieve comparable

Sky-image-based solar forecasting using deep learning with multi-location data: training models locally, globally or via transfer learning?

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Sky-image-based solar forecasting using deep learning with multi-location data: training models locally, globally or via transfer learning?