ECLIPSE : Envisioning Cloud Induced Perturbations in Solar Energy

• URL: http://arxiv.org/abs/2104.12419v1
• Date: Mon, 26 Apr 2021 09:19:43 GMT
• Title: ECLIPSE : Envisioning Cloud Induced Perturbations in Solar Energy
• Authors: Quentin Paletta, Anthony Hu, Guillaume Arbod, Joan Lasenby
• Abstract summary: ECLIPSE is a neural network architecture that models cloud motion from sky images to predict both future segmented images and corresponding irradiance levels. We show that ECLIPSE anticipates critical events and considerably reduces temporal delay while generating visually realistic futures.
• Score: 2.867517731896504
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Efficient integration of solar energy into the electricity mix depends on a reliable anticipation of its intermittency. A promising approach to forecast the temporal variability of solar irradiance resulting from the cloud cover dynamics, is based on the analysis of sequences of ground-taken sky images. Despite encouraging results, a recurrent limitation of current Deep Learning approaches lies in the ubiquitous tendency of reacting to past observations rather than actively anticipating future events. This leads to a systematic temporal lag and little ability to predict sudden events. To address this challenge, we introduce ECLIPSE, a spatio-temporal neural network architecture that models cloud motion from sky images to predict both future segmented images and corresponding irradiance levels. We show that ECLIPSE anticipates critical events and considerably reduces temporal delay while generating visually realistic futures.

Related papers

• Precise Forecasting of Sky Images Using Spatial Warping [12.042758147684822]
  We introduce a deep learning method to predict a future sky image frame with higher resolution than previous methods. Our main contribution is to derive an optimal warping method to counter the adverse affects of clouds at the horizon.
  arXiv  Detail & Related papers  (2024-09-18T17:25:42Z)
• Computational Imaging for Long-Term Prediction of Solar Irradiance [14.339647548237838]
  Real-time forecasting of cloud movement is necessary to schedule and allocate energy across grid-connected photovoltaic systems. Previous works monitored cloud movement using wide-angle field of view imagery of the sky. We design and deploy a catadioptric system that delivers wide-angle spatial resolution of the sky over its field of view.
  arXiv  Detail & Related papers  (2024-09-18T14:29:43Z)
• Back to the Future: GNN-based NO$_2$ Forecasting via Future Covariates [49.93577170464313]
  We deal with air quality observations in a city-wide network of ground monitoring stations. We propose a conditioning block that embeds past and future covariates into the current observations. We find that conditioning on future weather information has a greater impact than considering past traffic conditions.
  arXiv  Detail & Related papers  (2024-04-08T09:13:16Z)
• A conditional latent autoregressive recurrent model for generation and forecasting of beam dynamics in particle accelerators [46.348283638884425]
  We propose a two-step unsupervised deep learning framework named as Latent Autoregressive Recurrent Model (CLARM) for learning dynamics of charged particles in accelerators. The CLARM can generate projections at various accelerator sampling modules by capturing and decoding the latent space representation. The results demonstrate that the generative and forecasting ability of the proposed approach is promising when tested against a variety of evaluation metrics.
  arXiv  Detail & Related papers  (2024-03-19T22:05:17Z)
• Performative Time-Series Forecasting [71.18553214204978]
  We formalize performative time-series forecasting (PeTS) from a machine-learning perspective. We propose a novel approach, Feature Performative-Shifting (FPS), which leverages the concept of delayed response to anticipate distribution shifts. We conduct comprehensive experiments using multiple time-series models on COVID-19 and traffic forecasting tasks.
  arXiv  Detail & Related papers  (2023-10-09T18:34:29Z)
• Improving day-ahead Solar Irradiance Time Series Forecasting by Leveraging Spatio-Temporal Context [46.72071291175356]
  Solar power harbors immense potential in mitigating climate change by substantially reducing CO$_2$ emissions. However, the inherent variability of solar irradiance poses a significant challenge for seamlessly integrating solar power into the electrical grid. In this paper, we put forth a deep learning architecture designed to harnesstemporal context using satellite data.
  arXiv  Detail & Related papers  (2023-06-01T19:54:39Z)
• Omnivision forecasting: combining satellite observations with sky images for improved intra-hour solar energy predictions [0.0]
  Integration of intermittent renewable energy sources into electric grids is challenging. Short-term changes in electricity production caused by occluding clouds can be predicted at different time scales. In this study, we integrate these two complementary points of view on the cloud cover in a single machine learning framework.
  arXiv  Detail & Related papers  (2022-06-07T11:52:09Z)
• Predicting Future Occupancy Grids in Dynamic Environment with Spatio-Temporal Learning [63.25627328308978]
  We propose a-temporal prediction network pipeline to generate future occupancy predictions. Compared to current SOTA, our approach predicts occupancy for a longer horizon of 3 seconds. We publicly release our grid occupancy dataset based on nulis to support further research.
  arXiv  Detail & Related papers  (2022-05-06T13:45:32Z)
• Forecasting large-scale circulation regimes using deformable convolutional neural networks and global spatiotemporal climate data [86.1450118623908]
  We investigate a supervised machine learning approach based on deformable convolutional neural networks (deCNNs) We forecast the North Atlantic-European weather regimes during extended boreal winter for 1 to 15 days into the future. Due to its wider field of view, we also observe deCNN achieving considerably better performance than regular convolutional neural networks at lead times beyond 5-6 days.
  arXiv  Detail & Related papers  (2022-02-10T11:37:00Z)
• Benchmarking of Deep Learning Irradiance Forecasting Models from Sky Images -- an in-depth Analysis [0.0]
  We train four commonly used Deep Learning architectures to forecast solar irradiance from sequences of hemispherical sky images. Results show that encodingtemporal aspects greatly improved the predictions with 10 min Forecast Skill reaching 20.4% on the test year. We conclude that, with a common setup, Deep Learning models tend to behave just as a'very smart persistence model', temporally aligned with the persistence model while mitigating its most penalising errors.
  arXiv  Detail & Related papers  (2021-02-01T09:31:14Z)
• Convolutional Neural Networks applied to sky images for short-term solar irradiance forecasting [0.0]
  This work presents preliminary results on the application of deep Convolutional Neural Networks for 2 to 20 min irradiance forecasting. We evaluate the models on a set of irradiance measurements and corresponding sky images collected in Palaiseau (France) over 8 months with a temporal resolution of 2 min.
  arXiv  Detail & Related papers  (2020-05-22T15:57:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.