23 research outputs found
SOLAR IRRADIANCE FORECASTING IN THE TROPICS USING NUMERICAL WEATHER PREDICTION AND STATISTICAL LEARNING
No full textPh.DDOCTOR OF PHILOSOPHY (NGS
Solar irradiance forecasting in the tropics using numerical weather prediction and statistical learning
No full textInternational audienc
Probabilistic accumulated irradiance forecast for Singapore using ensemble techniques
No full textInternational audienc
The value of solar forecasting for energy-related applications: a treasure box of literature yet to be opened
No full textInternational audienc
Comparisons of next-day solar forecasting for Singapore using 3DVAR and 4DVAR data assimilation approaches with the WRF model
No full textInternational audienc
Probabilistic forecasting of day-ahead solar irradiance using quantile gradient boosting
No full textInternational audienc
Statistical learning for NWP post-processing: A benchmark for solar irradiance forecasting
No full textInternational audienc
Leveraging Deep-Learning Approaches with Spatial Context for Enhanced Surface Solar Irradiance Estimation from Third-Generation Geostationary Satellite Imagery
No full textInternational audienceThe accurate estimation of Surface Solar Irradiance (SSI) is crucial in domains as diverse as climatology, solar energy, agriculture, and architecture. Traditional SSI estimation methods are primarily based on physical models and cloud-index models. These approaches rely on the Independent Pixel Approximation (IPA) and neglect the intricate inter-pixel interactions, 3D effects of clouds, or parallax effects. This reliance on IPA and oversight of spatial dynamics could introduce limitations to traditional methods. These limitations are expected to increase with the advent of third-generation geostationary satellites like the GOES series, which offer enhanced spatial resolution. This work introduces a deep learning framework leveraging the increased spectral, spatial, and temporal resolution offered by third-generation geostationary satellites, without IPA, to improve SSI estimation.We developed a method using convolutional neural networks (CNNs) to analyze large satellite imagery, high-dimensional in spatial, spectral, and temporal domains, using contextual and multispectral image for SSI estimation. A comprehensive dataset, combining GOES-16 satellite imagery with 5-min global horizontal irradiance (GHI) in-situ measurements from 31 pyranometric stations in the U.S.A. over three years, was constructed and used for model training and validation, allowing for a direct comparison with PSM3, a state-of-the-art physical SSI-satellite-retrieval model from NREL. Our approach combines CNNs for image analysis and fully connected neural networks (FCNs) for processing tabular auxiliary data such as solar angles and positions, exploring various data fusion techniques. We thoroughly assess the model performance using a broad set of metrics, across various conditions and test stations, as well as the influence of varying image sizes on performance.Results demonstrate the potential of deep learning to outperform traditional models like PSM3 with traditional comparison metrics, especially under cloudy conditions, showing a 25% RMSE improvement. Our analysis highlights the importance of spatial context and the influence of image size in model performance, challenging the adequacy of IPA in traditional methods. A significant improvement is the effect of rotating input images, which substantially enhanced test performance and spatial generalization.For 5-min GHI estimation, our models achieved a test RMSE of 80 W/m^2, compared to 97 W/m^2 for PSM3, and demonstrated their robustness across diverse evaluation metrics, in most test stations and under various sky conditions. However, the mixed performance in MBE across all sky conditions, as well as other metrics under clear sky conditions and at specific test stations, indicate areas for further improvements in the representativity of the underlying physical process of SSI.While initial results are promising, further research is needed to refine model architectures and enhance generalization capabilities across different geographical locations Exploring physically informed and probabilistic deep learning methods could be a valuable direction for future research to enhance the spatial generalization, reliability, and interpretability of SSI estimation with deep learning
Assessing the Potential and Limitations of Deep Learning for Solar Irradiance Nowcasting across Large Geographical Areas
No full textInternational audienceIn energy systems with a high share of renewable energy supply (RES), accurate forecasting methods are very important for a secure and economical energy supply. For applications such as demand-supply balancing, short-term forecast (nowcasting) of spatial RES power generation is particularly important. In the literature, most models addressing this need use satellite-derived SSI estimations as input and optical flow or block-matching algorithms to predict their motion. Recently, new families of algorithms based on deep learning have appeared with great potential for improving the performance of nowcasting systems. It is thus of relevance to assess and understand the potential and limitations of deep learning approaches. Furthermore, when maps of solar irradiance are predicted, the metrics used to evaluate the forecasts are usually computed pixel-wise and thus ignore important spatial features, such as the consistency of spatial variability and spatial resolution. In this work, we investigate the potential of deep-learning algorithms for the forecasting of maps of 15-min solar surface irradiance (SSI) over short-term horizons, from nowcasts of SSI provided by CAMS radiation. We focus on a state-of-the-art deep learning model, designed for spatio-temporal processes: the convolutional long-term short-term network (ConvLSTM). We compare it to a “classic” forecasting model, based on an optical-flow algorithm (TVL1). We first perform a pixel-wise analysis of the models’ accuracy for forecasting horizons between 15 minutes and 3 hours. We then use Fourier spectral analysis to quantify the impact of each forecasting model on the spatial features of the SSI. Finally, we investigate the impact of the loss function used to train the convLSTM. Our results show that convLSTM and TVL1 have similar pixel-wise performances for short time horizons (15 and 30 minutes ahead), whereas, for larger horizons, convLSTM has a significantly lower RMSE and higher correlation. Fourier analysis, however, reveals that this improvement in pixel-wise accuracy comes with a degradation of the spatial features of SSI. TVL1 forecasts indeed have realistic spatial variability for all tested horizons, but convLSTM produces increasingly smooth predictions: for horizons beyond 2 hours, and despite its higher accuracy, convLSTM acts as a low-pass filter and fully ignores high spatial frequencies. This shows that the gains in accuracy are obtained at the expense of the fine spatial structure, which is a well-known phenomenon in forecasting. However, highlighting and quantifying this effect is important because smoothing can be problematic in some applications such as variability or ramp forecasting. Using hybrid loss functions penalizing the lack of variability in the forecasts indeed improves the spatial behavior of the deep-learning model without significantly reducing its pixel-wise performance. At large forecast horizons, however, such hybrid loss functions cannot prevent a substantial loss of spatial variability and convLSTM predictions remain overly smooth
