Improving day-ahead Solar Irradiance Time Series Forecasting by Leveraging Spatio-Temporal Context

• URL: http://arxiv.org/abs/2306.01112v2
• Date: Mon, 23 Oct 2023 17:14:16 GMT
• Title: Improving day-ahead Solar Irradiance Time Series Forecasting by Leveraging Spatio-Temporal Context
• Authors: Oussama Boussif, Ghait Boukachab, Dan Assouline, Stefano Massaroli, Tianle Yuan, Loubna Benabbou, Yoshua Bengio
• Abstract summary: 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.
• Score: 46.72071291175356
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• MambaDS: Near-Surface Meteorological Field Downscaling with Topography Constrained Selective State Space Modeling [68.69647625472464]
  Downscaling, a crucial task in meteorological forecasting, enables the reconstruction of high-resolution meteorological states for target regions. Previous downscaling methods lacked tailored designs for meteorology and encountered structural limitations. We propose a novel model called MambaDS, which enhances the utilization of multivariable correlations and topography information.
  arXiv  Detail & Related papers  (2024-08-20T13:45:49Z)
• FusionSF: Fuse Heterogeneous Modalities in a Vector Quantized Framework for Robust Solar Power Forecasting [24.57911612111109]
  We propose a multi-modality fusion framework to integrate historical power data, numerical weather prediction, and satellite images. Our framework demonstrates strong zero-shot forecasting capability, which is especially useful for those newly installed plants. Our model not only operates with robustness but also boosts accuracy in both zero-shot forecasting and scenarios rich with training data, surpassing leading models.
  arXiv  Detail & Related papers  (2024-02-08T17:03:10Z)
• Observation-Guided Meteorological Field Downscaling at Station Scale: A Benchmark and a New Method [66.80344502790231]
  We extend meteorological downscaling to arbitrary scattered station scales and establish a new benchmark and dataset. Inspired by data assimilation techniques, we integrate observational data into the downscaling process, providing multi-scale observational priors. Our proposed method outperforms other specially designed baseline models on multiple surface variables.
  arXiv  Detail & Related papers  (2024-01-22T14:02:56Z)
• Benchmarks and Custom Package for Energy Forecasting [55.460452605056894]
  Energy forecasting aims to minimize the cost of subsequent tasks such as power grid dispatch. In this paper, we collected large-scale load datasets and released a new renewable energy dataset. We conducted extensive experiments with 21 forecasting methods in these energy datasets at different levels under 11 evaluation metrics.
  arXiv  Detail & Related papers  (2023-07-14T06:50:02Z)
• A Comparative Study on Generative Models for High Resolution Solar Observation Imaging [59.372588316558826]
  This work investigates capabilities of current state-of-the-art generative models to accurately capture the data distribution behind observed solar activity states. Using distributed training on supercomputers, we are able to train generative models for up to 1024x1024 resolution that produce high quality samples indistinguishable to human experts.
  arXiv  Detail & Related papers  (2023-04-14T14:40:32Z)
• Data-driven soiling detection in PV modules [58.6906336996604]
  We study the problem of estimating the soiling ratio in photo-voltaic (PV) modules. A key advantage of our algorithms is that they estimate soiling, without needing to train on labelled data. Our experimental evaluation shows that we significantly outperform current state-of-the-art methods for estimating soiling ratio.
  arXiv  Detail & Related papers  (2023-01-30T14:35:47Z)
• A Moment in the Sun: Solar Nowcasting from Multispectral Satellite Data using Self-Supervised Learning [4.844946519309793]
  We develop a general model for solar nowcasting from abundant and readily available multispectral satellite data using self-supervised learning. Our model estimates a location's future solar irradiance based on satellite observations. We evaluate our approach for different coverage areas and forecast horizons across 25 solar sites.
  arXiv  Detail & Related papers  (2021-12-28T03:13:44Z)
• Prediction of Solar Radiation Using Artificial Neural Network [0.0]
  This paper presents an algorithm that can be used to predict an hourly activity of solar radiation. The dataset consists of temperature of air, time, humidity, wind speed, atmospheric pressure, direction of wind and solar radiation data. Two models are created to efficiently create a system capable of interpreting patterns through supervised learning data.
  arXiv  Detail & Related papers  (2021-04-01T20:41:27Z)
• Solar Radiation Anomaly Events Modeling Using Spatial-Temporal Mutually Interactive Processes [11.119904876394399]
  Solar ramping events are impacted by weather conditions such as temperature, humidity, and cloud density. We propose a novel method to model and model solar radiation data based on spatial-temporal interactive Bernoulli process.
  arXiv  Detail & Related papers  (2021-01-27T03:02:39Z)
• Short term solar energy prediction by machine learning algorithms [0.47791962198275073]
  We report daily prediction of solar energy by exploiting the strength of machine learning techniques. Forecast models of base line regressors including linear, ridge, lasso, decision tree, random forest and artificial neural networks have been implemented. It has been observed that improved accuracy is achieved through random forest and ridge regressor for both grid sizes.
  arXiv  Detail & Related papers  (2020-10-25T17:56:03Z)

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.