What if We Enrich day-ahead Solar Irradiance Time Series Forecasting with Spatio-Temporal Context?

Solar power harbors immense potential in mitigating climate change by substantially reducing CO$_{2}$ emissions. Nonetheless, the inherent variability of solar irradiance poses a significant challenge for seamlessly integrating solar power into the electrical grid. While the majority of prior research has centered on employing purely time series-based methodologies for solar forecasting, only a limited number of studies have taken into account factors such as cloud cover or the surrounding physical context. In this paper, we put forth a deep learning architecture designed to harness spatio-temporal context using satellite data, to attain highly accurate \textit{day-ahead} time-series forecasting for any given station, with a particular emphasis on forecasting Global Horizontal Irradiance (GHI). We also suggest a methodology to extract a distribution for each time step prediction, which can serve as a very valuable measure of uncertainty attached to the forecast. When evaluating models, we propose a testing scheme in which we separate particularly difficult examples from easy ones, in order to capture the model performances in crucial situations, which in the case of this study are the days suffering from varying cloudy conditions. Furthermore, we present a new multi-modal dataset gathering satellite imagery over a large zone and time series for solar irradiance and other related physical variables from multiple geographically diverse solar stations. Our approach exhibits robust performance in solar irradiance forecasting, including zero-shot generalization tests at unobserved solar stations, and holds great promise in promoting the effective integration of solar power into the grid

Exploring Interpretable LSTM Neural Networks over Multi-Variable Data

For recurrent neural networks trained on time series with target and exogenous variables, in addition to accurate prediction, it is also desired to provide interpretable insights into the data. In this paper, we explore the structure of LSTM recurrent neural networks to learn variable-wise hidden states, with the aim to capture different dynamics in multi-variable time series and distinguish the contribution of variables to the prediction. With these variable-wise hidden states, a mixture attention mechanism is proposed to model the generative process of the target. Then we develop associated training methods to jointly learn network parameters, variable and temporal importance w.r.t the prediction of the target variable. Extensive experiments on real datasets demonstrate enhanced prediction performance by capturing the dynamics of different variables. Meanwhile, we evaluate the interpretation results both qualitatively and quantitatively. It exhibits the prospect as an end-to-end framework for both forecasting and knowledge extraction over multi-variable data.Comment: Accepted to International Conference on Machine Learning (ICML), 201

Advanced multimodal fusion method for very short-term solar irradiance forecasting using sky images and meteorological data: A gate and transformer mechanism approach

Cloud dynamics are the main factor influencing the intermittent variability of short-term solar irradiance, therefore affect the solar farm output. Sky images have been widely used for short-term solar irradiance prediction with encouraging results due to the spatial information they contain. At present, there is little discussion on the most promising deep learning methods to integrate images with quantitative measures of solar irradiation. To address this gap, we optimise the current mainstream framework using gate architecture and propose a new transformer-based framework in an attempt to achieve better prediction results. It was found that compared to the classical CNN model based on late feature-level fusion, the transformer framework model based on early feature-level prediction improves the balanced accuracy of Ramp Event by 9.43% and 3.91% on the 2-min and 6-min scales, respectively. However, based on the results, it can be concluded that for the single picture-digital bimodal model, the spatial information validity of a single picture is difficult to achieve beyond 10 min. This work has the potential to contribute to the interpretability and iterability of deep learning models based on sky images

Deep learning-based hybrid short-term solar forecast using sky images and meteorological data

The global growth of solar power generation is rapid, yet the complex nature of cloud movement introduces significant uncertainty to short-term solar irradiance, posing challenges for intelligent power systems. Accurate short-term solar irradiance and photovoltaic power generation predictions under cloudy skies are critical for sub-hourly electricity markets. Ground-based image (GSI) analysis using convolutional neural network (CNN) algorithms has emerged as a promising method due to advancements in machine vision models based on deep learning networks. In this work, a novel deep network, ”ViT-E,” based on an attention mechanism Transformer architecture for short-term solar irradiance forecasting has been proposed. This innovative model enables cross-modality data parsing by establishing mapping relationships within GSI and between GSI, meteorological data, historical irradiation, clear sky irradiation, and solar angles. The feasibility of the ViT-E network was assessed the Folsom dataset from California, USA. Quantitative analysis showed that the ViT-E network achieved RMSE values of 81.45 W/m2 , 98.68 W/m2 , and 104.91 W/m2 for 2, 6, and 10-minute forecasts, respectively, outperforming the persistence model by 4.87%, 16.06%, and 19.09% and displaying performance comparable to CNN-based models. Qualitative analysis revealed that the ViT-E network successfully predicted 20.21%, 33.26%, and 36.87% of solar slope events at 2, 6, and 10 minutes in advance, respectively, significantly surpassing the persistence model and currently prevalent CNN-based model by 9.43%, 3.91%, and -0.55% for 2, 6, and 10-minute forecasts, respectively. Transfer learning experiments were conducted to test the ViT-E model’s generalisation under different climatic conditions and its performance on smaller datasets. We discovered that the weights learned from the three-year Folsom dataset in the United States could be transferred to a half-year local dataset in Nottingham, UK. Training with a dataset one-fifth the size of the original dataset achieved baseline accuracy standards and reduced training time by 80.2%. Additionally, using a dataset equivalent to only 4.5% of the original size yielded a model with less than 2% accuracy below the baseline. These findings validated the generalisation and robustness of the model’s trained weights. Finally, the ViT-E model architecture and hyperparameters were optimised and searched. Our investigation revealed that directly applying migrated deep vision models leads to redundancy in solar forecasting. We identified the best hyperparameters for ViT-E through manual hyperparameter space exploration. As a result, the model’s computational efficiency improved by 60%, and prediction performance increased by 2.7%

Deep learning-based hybrid short-term solar forecast using sky images and meteorological data

The global growth of solar power generation is rapid, yet the complex nature of cloud movement introduces significant uncertainty to short-term solar irradiance, posing challenges for intelligent power systems. Accurate short-term solar irradiance and photovoltaic power generation predictions under cloudy skies are critical for sub-hourly electricity markets. Ground-based image (GSI) analysis using convolutional neural network (CNN) algorithms has emerged as a promising method due to advancements in machine vision models based on deep learning networks. In this work, a novel deep network, ”ViT-E,” based on an attention mechanism Transformer architecture for short-term solar irradiance forecasting has been proposed. This innovative model enables cross-modality data parsing by establishing mapping relationships within GSI and between GSI, meteorological data, historical irradiation, clear sky irradiation, and solar angles. The feasibility of the ViT-E network was assessed the Folsom dataset from California, USA. Quantitative analysis showed that the ViT-E network achieved RMSE values of 81.45 W/m2 , 98.68 W/m2 , and 104.91 W/m2 for 2, 6, and 10-minute forecasts, respectively, outperforming the persistence model by 4.87%, 16.06%, and 19.09% and displaying performance comparable to CNN-based models. Qualitative analysis revealed that the ViT-E network successfully predicted 20.21%, 33.26%, and 36.87% of solar slope events at 2, 6, and 10 minutes in advance, respectively, significantly surpassing the persistence model and currently prevalent CNN-based model by 9.43%, 3.91%, and -0.55% for 2, 6, and 10-minute forecasts, respectively. Transfer learning experiments were conducted to test the ViT-E model’s generalisation under different climatic conditions and its performance on smaller datasets. We discovered that the weights learned from the three-year Folsom dataset in the United States could be transferred to a half-year local dataset in Nottingham, UK. Training with a dataset one-fifth the size of the original dataset achieved baseline accuracy standards and reduced training time by 80.2%. Additionally, using a dataset equivalent to only 4.5% of the original size yielded a model with less than 2% accuracy below the baseline. These findings validated the generalisation and robustness of the model’s trained weights. Finally, the ViT-E model architecture and hyperparameters were optimised and searched. Our investigation revealed that directly applying migrated deep vision models leads to redundancy in solar forecasting. We identified the best hyperparameters for ViT-E through manual hyperparameter space exploration. As a result, the model’s computational efficiency improved by 60%, and prediction performance increased by 2.7%

Computational Intelligence for Modeling, Control, Optimization, Forecasting and Diagnostics in Photovoltaic Applications

This book is a Special Issue Reprint edited by Prof. Massimo Vitelli and Dr. Luigi Costanzo. It contains original research articles covering, but not limited to, the following topics: maximum power point tracking techniques; forecasting techniques; sizing and optimization of PV components and systems; PV modeling; reconfiguration algorithms; fault diagnosis; mismatching detection; decision processes for grid operators