publications

2025

1. PROCEEDINGS

   Day-Ahead Operational Forecast of Aggregated Solar Generation Assimilating Mesoscale Meteorology Information

   Guillermo Terrén-Serrano, Ranjit Deshmukh, and Manel Martínez-Ramón

   In 2025 IEEE PES Grid Edge Technologies Conference & Exposition (Grid Edge), Jan 2025

   Abs DOI Bib HTML

   System-Ievel (balancing area) forecasting errors can increase with growing shares of solar generation, thus increasing scheduling changes, area control errors, and system frequency variation. This investigation introduces a method to combine a spatial numerical weather forecast with solar generation time series to improve the performance of aggregated system-level day-ahead solar forecasts. The proposed method utilizes sparse learning to select spatial weather features (long-wave, short-wave, and clear-sky radiation) and Bayesian learning to predict the mean and variance in the forecast. The proposed method is probabilistic and preserves time structure in the predictive distribution. Different combinations of four sparse learning and three Bayesian learning methods are evaluated with four proper multivariate scoring rules to select the best model. Applying this method to the California Independent System Operator (CAISO) grid, the daily forecast improved by up to 10.4% relative to their forecast.

   @inproceedings{10887459,
     author = {Terrén-Serrano, Guillermo and Deshmukh, Ranjit and Martínez-Ramón, Manel},
     booktitle = {2025 IEEE PES Grid Edge Technologies Conference & Exposition (Grid Edge)},
     title = {Day-Ahead Operational Forecast of Aggregated Solar Generation Assimilating Mesoscale Meteorology Information},
     year = {2025},
     volume = {},
     number = {},
     pages = {1-5},
     keywords = {Uncertainty;Time series analysis;Predictive models;Probabilistic logic;Bayes methods;Numerical models;Solar power generation;Reliability;Wind forecasting;Meteorology;Sparse Learning;Bayesian Learning;Solar Forecast;Mesoscale Meteorology},
     doi = {10.1109/GridEdge61154.2025.10887459},
     issn = {},
     month = jan
   }

2. JOURNAL

   Extreme day-ahead renewables scenario selection in power grid operations

   Guillermo Terrén-Serrano and Michael Ludkovski

   Applied Energy, Jan 2025

   Abs DOI Bib HTML

   We propose and analyze the application of statistical functional depth metrics for the selection of extreme scenarios for realized electric load, as well as solar and wind generation in day-ahead grid planning. Our primary motivation is screening probabilistic scenarios to identify those most relevant for operational risk mitigation. To handle the high-dimensionality of the scenarios across asset classes and intra-day periods, we employ functional measures of depth to sub-select outlying scenarios that are most likely to be the riskiest for the grid operation. We investigate a range of functional depth measures, as well as a range of operational risks, including load shedding, operational costs, reserve shortfalls, and variable renewable energy curtailment. The effectiveness of the proposed screening approach is demonstrated through a case study on the realistic Texas-7k grid.

   @article{TERRENSERRANO2025125747,
     title = {Extreme day-ahead renewables scenario selection in power grid operations},
     journal = {Applied Energy},
     volume = {391},
     pages = {125747},
     year = {2025},
     issn = {0306-2619},
     doi = {https://doi.org/10.1016/j.apenergy.2025.125747},
     author = {Terrén-Serrano, Guillermo and Ludkovski, Michael},
     keywords = {Functional depth, Operational planning, Power grids, Renewable energy, Statistical extremality}
   }

2023

1. JOURNAL

   Kernel learning for intra-hour solar forecasting with infrared sky images and cloud dynamic feature extraction

   G. Terrén-Serrano and M. Martínez-Ramón

   Renewable and Sustainable Energy Reviews, Jan 2023

   Abs DOI Bib HTML

   Power grid operators incur additional costs to guarantee a reliable energy supply due to the uncertainty of the energy generated by photovoltaic systems. These additional costs are derived from the need for energy storage or increasing the planning reserve margin requirements. This investigation aims to decrease the costs by introducing a multi-task intra-hour solar forecast (feasible in real-time applications) to optimize energy dispatch and increase the participation of photovoltaics in power grids. The proposed method estimates the motion of clouds in a sequence of consecutive sky images by extracting features of cloud dynamics to forecast the global horizontal irradiance reaching a photovoltaic system. The sky images are acquired using a low-cost infrared sky imager mounted on a solar tracker. The solar forecasting algorithm is based on kernel learning methods and uses the clear sky index as the response variable and features extracted from clouds as covariates. The proposed algorithm achieved 16.48% forecasting skill 8 min ahead with a resolution of 1 min. Previous work reached 15.4% forecasting skill with 1 min resolution. Additionally, this investigation evaluates and compares the performances of multi-task Bayesian learning methods which provide a probabilistic forecast. The proposed solar forecasting algorithm can potentially assist grid operators in managing the inherent uncertainties of power grids with a high participation of solar energy resources.

   @article{TERRENSERRANO2023113125,
     title = {Kernel learning for intra-hour solar forecasting with infrared sky images and cloud dynamic feature extraction},
     journal = {Renewable and Sustainable Energy Reviews},
     volume = {175},
     pages = {113125},
     year = {2023},
     issn = {1364-0321},
     doi = {https://doi.org/10.1016/j.rser.2022.113125},
     author = {Terrén-Serrano, G. and Martínez-Ramón, M.},
     keywords = {Flow visualization, Girasol dataset, Kernel learning, Machine learning, Solar forecasting, Sky imaging}
   }

2. JOURNAL

   Detection of clouds in multiple wind velocity fields using ground-based infrared sky images

   Guillermo Terrén-Serrano and Manel Martínez-Ramón

   Knowledge-Based Systems, Jan 2023

   Abs DOI Bib HTML

   To improve the performance of an intra-hour global solar irradiance forecasting algorithm, it is important to detect multiple layers of clouds. Horizontal atmospheric wind shear causes wind velocity fields to have different directions and speeds. In images of clouds acquired using ground-based sky imagers, clouds may be moving in different wind layers. The information provided by a solar forecasting algorithm is necessary to optimize and schedule the solar generation resources and storage devices in a smart grid. This investigation studies the performance of unsupervised learning techniques when detecting the number of cloud layers in infrared sky images. The images are acquired using an innovative infrared sky imager mounted on a solar tracker. Different mixture models are used to infer the distribution of the cloud features. Multiple Bayesian metrics and a sequential hidden Markov model are implemented to find the optimal number of clusters in the mixture models, and their performances are compared. The motion vectors are computed using a probabilistic implementation of the Lucas-Kanade algorithm. The correlations between the cloud motion vectors and temperatures are analyzed to discover the method that leads to the most accurate results. The findings point that a sequential hidden Markov model outperforms the detection accuracy of standard Bayesian model selection metrics.

   @article{TERRENSERRANO2023110628,
     title = {Detection of clouds in multiple wind velocity fields using ground-based infrared sky images},
     journal = {Knowledge-Based Systems},
     volume = {274},
     pages = {110628},
     year = {2023},
     issn = {0950-7051},
     doi = {https://doi.org/10.1016/j.knosys.2023.110628},
     author = {Terrén-Serrano, Guillermo and Martínez-Ramón, Manel},
     keywords = {Cloud detection, Hidden Markov model, Mixture models, Sky imaging, Weighted Lucas-Kanade}
   }

3. JOURNAL

   Deep learning for intra-hour solar forecasting with fusion of features extracted from infrared sky images

   Guillermo Terrén-Serrano and Manel Martínez-Ramón

   Information Fusion, Jan 2023

   Abs DOI Bib HTML

   The increasing penetration of solar energy leaves power grids vulnerable to fluctuations in the solar radiation that reaches the surface of the Earth due to the projection of cloud shadows. Therefore, an intra-hour solar forecasting algorithm is necessary to reduce power instabilities caused by the impact of moving clouds on energy generation. The most accurate intra-hour solar forecasting methods apply convolutional neural networks to a series of visible light sky images. Instead, this investigation uses data acquired by a novel infrared sky imager on a solar tracker, which is capable of maintaining the Sun in the center of the images throughout the day and, at the same time, reducing the scattering effect produced by the Sun’s direct radiation. In addition, infrared sky images allow the derivation and extraction of physical cloud features. The cloud dynamics are analyzed in sequences of images to compute the probability of the Sun intercepting air parcels in the sky images (i.e., voxels). The method introduced in this investigation fuses sky condition information from multiple sensors (i.e., pyranometer, sky imager, solar tracker, weather station) and feature sources using a multi-task deep learning architecture based on recurrent neural networks. The proposed deterministic and Bayesian architectures reduce computation time by avoiding convolutional filters. The proposed intra-hour solar forecasting algorithm reached a forecast skill of 18.6% with a forecasting horizon of 8 min. Consequently, the proposed intra-hour solar forecasting method can potentially reduce the operational costs of power grids with high participation of solar energy.

   @article{TERRENSERRANO202342,
     title = {Deep learning for intra-hour solar forecasting with fusion of features extracted from infrared sky images},
     journal = {Information Fusion},
     volume = {95},
     pages = {42-61},
     year = {2023},
     issn = {1566-2535},
     doi = {https://doi.org/10.1016/j.inffus.2023.02.006},
     author = {Terrén-Serrano, Guillermo and Martínez-Ramón, Manel},
     keywords = {Bayesian networks, Bayesian optimization, Deep learning, Girasol dataset, Solar forecasting, Sky imaging}
   }

4. JOURNAL

   Processing of global solar irradiance and ground-based infrared sky images for solar nowcasting and intra-hour forecasting applications

   Guillermo Terrén-Serrano and Manel Martínez-Ramón

   Solar Energy, Jan 2023

   Abs DOI Bib HTML

   Shadows from moving clouds in the troposphere impact the energy generated by photovoltaic systems. intra-hour solar forecast can be used to regulate solar energy dispatch. This investigation develops a data processing method for cloud dynamic feature extraction from raw sky images and Global Solar Irradiance (GSI) measurements that can be integrated into solar forecasting algorithms to reduce the operational supervision of hardware. Sky images and GSI measurements are acquired from a low-cost long-wave infrared radiometric camera and a pyranometer. This sky imager is mounted on a solar tracker that maintains the Sun in the center of sky images throughout the day. Multiple processing methods are proposed here that take advantage of a hybrid approach to approximate the optimal parameters of physical models using computationally inexpensive machine learning models. A signal processing method removes cyclostationary biases in high-resolution clear sky index values found when detrending GSI measurements using the clear sky GSI. Image processing methods are then used to remove the effects of atmospheric radiation and the Sun’s direct radiation from infrared sky images, plus the radiation effect emitted by debris on the sky imager’s germanium outdoor lens. The result is an adaptive solar forecasting algorithm that can reduce the operational cost of power grids with the high participation of solar energy in the generation mix.

   @article{TERRENSERRANO2023111968,
     title = {Processing of global solar irradiance and ground-based infrared sky images for solar nowcasting and intra-hour forecasting applications},
     journal = {Solar Energy},
     volume = {264},
     pages = {111968},
     year = {2023},
     issn = {0038-092X},
     doi = {https://doi.org/10.1016/j.solener.2023.111968},
     author = {Terrén-Serrano, Guillermo and Martínez-Ramón, Manel},
     keywords = {Image processing, Long-wave infrared, Machine learning, Sky imaging, Sun tracking}
   }

5. JOURNAL

   Advances in solar forecasting: Computer vision with deep learning

   Quentin Paletta, Guillermo Terrén-Serrano, Yuhao Nie, and 6 more authors

   Advances in Applied Energy, Jan 2023

   Abs DOI Bib HTML

   Renewable energy forecasting is crucial for integrating variable energy sources into the grid. It allows power systems to address the intermittency of the energy supply at different spatiotemporal scales. To anticipate the future impact of cloud displacements on the energy generated by solar facilities, conventional modeling methods rely on numerical weather prediction or physical models, which have difficulties in assimilating cloud information and learning systematic biases. Augmenting computer vision with machine learning overcomes some of these limitations by fusing real-time cloud cover observations with surface measurements acquired from multiple sources. This Review summarizes recent progress in solar forecasting from multisensor Earth observations with a focus on deep learning, which provides the necessary theoretical framework to develop architectures capable of extracting relevant information from data generated by ground-level sky cameras, satellites, weather stations, and sensor networks. Overall, machine learning has the potential to significantly improve the accuracy and robustness of solar energy meteorology; however, more research is necessary to realize this potential and address its limitations.

   @article{PALETTA2023100150,
     title = {Advances in solar forecasting: Computer vision with deep learning},
     journal = {Advances in Applied Energy},
     volume = {11},
     pages = {100150},
     year = {2023},
     issn = {2666-7924},
     doi = {https://doi.org/10.1016/j.adapen.2023.100150},
     author = {Paletta, Quentin and Terrén-Serrano, Guillermo and Nie, Yuhao and Li, Binghui and Bieker, Jacob and Zhang, Wenqi and Dubus, Laurent and Dev, Soumyabrata and Feng, Cong},
     keywords = {Solar forecasting, Computer vision, Deep learning, Satellite imagery, Sky images, Solar irradiance}
   }

2022

1. JOURNAL

   Geospatial Perspective Reprojections for Ground-Based Sky Imaging Systems

   Guillermo Terrén-Serrano and Manel Martínez-Ramón

   IEEE Transactions on Geoscience and Remote Sensing, 2022

   Abs DOI Bib HTML

   The intermittency of solar energy produces instabilities in power grids. These instabilities are reduced with an intrahour solar forecast that uses ground-based sky imaging systems. Sky imaging systems use lenses to acquire images concentrating light beams in a sensor. The light beams received by the sky imager have an elevation angle with respect to the device’s normal. Thus, the pixels in the image contain information from different areas of the sky within the imaging system field of view (FOV). The area of the FOV contained in the pixels increases as the elevation angle of the incident light beams decreases. When the sky imager is mounted on a solar tracker, the light beam’s angle of incidence in a pixel varies over time. This investigation formulates and compares two geospatial reprojections that transform the original Euclidean frame of the imager’s plane to the geospatial atmosphere cross section where the sky imager’s FOV intersects the cloud layer. One assumes that an object (i.e., cloud) moving in the troposphere is sufficiently far so the Earth’s surface is approximated flat. The other transformation takes into account the curvature of the Earth in the portion of the atmosphere (i.e., voxel) that is recorded. The results show that the differences between the dimensions calculated by both geospatial transformations are in the order of magnitude of kilometers when the Sun’s elevation angle is below 30°.

   @article{9721281,
     author = {Terrén-Serrano, Guillermo and Martínez-Ramón, Manel},
     journal = {IEEE Transactions on Geoscience and Remote Sensing},
     title = {Geospatial Perspective Reprojections for Ground-Based Sky Imaging Systems},
     year = {2022},
     volume = {60},
     number = {},
     pages = {1-7},
     keywords = {Cloud computing;Lenses;Cameras;Geospatial analysis;Earth;Clouds;Sun;Perspective reprojection;sky imaging;solar forecasting;solar tracking},
     doi = {10.1109/TGRS.2022.3154710},
     issn = {1558-0644},
     month = {}
   }

2. DISSERTATION

   Intra-hour solar forecasting using cloud dynamics features extracted from ground-based infrared sky images

   Guillermo Terrén-Serrano

   The University of New Mexico, 2022

   Abs Bib HTML

   Due to the increasing use of photovoltaic systems, power grids are vulnerable to the projection of shadows from moving clouds. An intra-hour solar forecast provides power grids with the capability of automatically controlling the dispatch of energy, reducing the additional cost for a guaranteed, reliable supply of energy (i.e., energy storage). This dissertation introduces a novel sky imager consisting of a long-wave radiometric infrared camera and a visible light camera with a fisheye lens. The imager is mounted on a solar tracker to maintain the Sun in the center of the images throughout the day, reducing the scattering effect produced by the Sun’s direct radiation. Features of the cloud dynamics are analyzed to compute the probability of the Sun intercepting air parcels in the sky images. Probabilistic and deterministic multi-task intra-hour solar forecasting algorithms are introduced, based on kernel and deep learning methods, to increase the penetration of photovoltaic systems in power grids.

   @phdthesis{terren2022intra,
     title = {Intra-hour solar forecasting using cloud dynamics features extracted from ground-based infrared sky images},
     author = {Terr{\'e}n-Serrano, Guillermo},
     year = {2022},
     school = {The University of New Mexico},
     keywords = {computer vision, deep learning, kernel learning, machine learning, sky imaging, solar forecasting.},
   }

2021

1. JOURNAL

   Comparative analysis of methods for cloud segmentation in ground-based infrared images

   Guillermo Terrén-Serrano and Manel Martínez-Ramón

   Renewable Energy, 2021

   Abs DOI Bib HTML

   The increasing penetration of photovoltaic systems in the power grid makes it vulnerable to cloud shadow projection. Real-time cloud segmentation in ground-based infrared images is important to reduce the noise in intra-hour global solar irradiance forecasting. We present a comparison between discriminative and generative models for cloud segmentation. The performances of supervised and unsupervised learning methods in cloud segmentation are evaluated. The discriminative models are solved in the primal formulation to make them feasible in real-time applications. The performances are compared using the j-statistic. Infrared image preprocessing to remove stationary artifacts increases the overall performance in the analyzed methods. The inclusion of features from neighboring pixels in the feature vectors leads to a performance improvement in some of the cases. Markov Random Fields achieve the best performance in both unsupervised and supervised generative models. Discriminative models solved in the primal yield a dramatically lower computing time along with high performance in the segmentation. Generative and discriminative models are comparable when preprocessing is applied to the infrared images.

   @article{TERRENSERRANO20211025,
     title = {Comparative analysis of methods for cloud segmentation in ground-based infrared images},
     journal = {Renewable Energy},
     volume = {175},
     pages = {1025-1040},
     year = {2021},
     issn = {0960-1481},
     doi = {https://doi.org/10.1016/j.renene.2021.04.141},
     author = {Terrén-Serrano, Guillermo and Martínez-Ramón, Manel},
     keywords = {Cloud segmentation, Machine learning, Markov random field, Sky imaging, Solar forecasting}
   }

2. JOURNAL

   Girasol, a sky imaging and global solar irradiance dataset

   Guillermo Terrén-Serrano, Adnan Bashir, Trilce Estrada, and 1 more author

   Data in Brief, 2021

   Abs DOI Bib HTML

   The energy available in a microgrid that is powered by solar energy is tightly related to the weather conditions at the moment of generation. A very short-term forecast of solar irradiance provides the microgrid with the capability of automatically controlling the dispatch of energy. We propose a dataset to forecast Global Solar Irradiance (GSI) using a data acquisition system (DAQ) that simultaneously records sky imaging and GSI measurements, with the objective of extracting features from clouds and use them to forecast the power produced by a Photovoltaic (PV) system. The DAQ system is nicknamed the Girasol Machine (Girasol means Sunflower in Spanish). The sky imaging system consists of a longwave infrared (IR) camera and a visible (VI) light camera with a fisheye lens attached to it. The cameras are installed inside a weatherproof enclosure that it is mounted on a solar tracker. The tracker updates its pan and tilt every second using a solar position algorithm to maintain the Sun in the center of the IR and VI images. A pyranometer is situated on a horizontal mount next to the DAQ system to measure GSI. The dataset, composed of IR images, VI images, GSI measurements, and the Sun’s positions, has been tagged with timestamps.

   @article{TERRENSERRANO2021106914,
     title = {Girasol, a sky imaging and global solar irradiance dataset},
     journal = {Data in Brief},
     volume = {35},
     pages = {106914},
     year = {2021},
     issn = {2352-3409},
     doi = {https://doi.org/10.1016/j.dib.2021.106914},
     author = {Terrén-Serrano, Guillermo and Bashir, Adnan and Estrada, Trilce and Martínez-Ramón, Manel},
     keywords = {Sky Imaging, Global Solar Irradiance, Fisheye Lens Camera, Long-wave Infrared Camera, Data Acquisition System, Solar Forecasting, Smart Grids, Sun-Tracking}
   }

3. JOURNAL

   Multi-layer wind velocity field visualization in infrared images of clouds for solar irradiance forecasting

   Guillermo Terrén-Serrano and Manel Martínez-Ramón

   Applied Energy, 2021

   Abs DOI Bib HTML

   The energy available in a solar energy powered grid is uncertain due to the weather conditions at the time of generation. Forecasting global solar irradiance could address this problem by providing the power grid with the capability of scheduling the storage and dispatch of energy. The occlusion of the Sun by clouds is the main cause of instabilities in the generation of solar energy. This investigation proposes a method to visualize the wind velocity field in sequences of longwave infrared images of clouds when there are multiple wind velocity fields in an image. This method can be used to forecast the occlusion of the Sun by clouds, providing stability in the generation of solar energy. Unsupervised learning is implemented to infer the distribution of the clouds’ velocity vectors and heights in multiple wind velocity fields in an infrared image. A multi-output weighted support vector machine with flow constraints is used to extrapolate the wind velocity fields to the entire frame, visualizing the path of the clouds. The proposed method is capable of approximating the wind velocity field in a small air parcel using the velocity vectors and physical features of clouds extracted from infrared images. Assuming that the streamlines are pathlines, the visualization of the wind velocity field can be used for forecasting cloud occlusions of the Sun. This is of importance when considering ways of increasing the stability of solar energy generation.

   @article{TERRENSERRANO2021116656,
     title = {Multi-layer wind velocity field visualization in infrared images of clouds for solar irradiance forecasting},
     journal = {Applied Energy},
     volume = {288},
     pages = {116656},
     year = {2021},
     issn = {0306-2619},
     doi = {https://doi.org/10.1016/j.apenergy.2021.116656},
     author = {Terrén-Serrano, Guillermo and Martínez-Ramón, Manel},
     keywords = {Cloud tracking, Machine learning, Flow visualization, Beta mixture model, Sky imaging, Solar forecasting}
   }

4. PROCEEDINGS

   Explicit basis function kernel methods for cloud segmentation in infrared sky images

   Guillermo Terrén-Serrano and Manel Martínez-Ramón

   Energy Reports, 2021

   2021 The 4th International Conference on Electrical Engineering and Green Energy

   Abs DOI Bib HTML

   Photovoltaic systems are sensitive to cloud shadow projection, which needs to be forecasted to reduce the noise impacting the intra-hour forecast of global solar irradiance. We present a comparison between different kernel discriminative models for cloud detection. The models are solved in the primal formulation to make them feasible in real-time applications. The performances are compared using the j-statistic. The infrared cloud images have been preprocessed to remove debris, which increases the performance of the analyzed methods. The use of the pixels’ neighboring features also leads to a performance improvement. Discriminative models solved in the primal yield a dramatically lower computing time along with high performance in the segmentation.

   @article{TERRENSERRANO2021442,
     title = {Explicit basis function kernel methods for cloud segmentation in infrared sky images},
     journal = {Energy Reports},
     volume = {7},
     pages = {442-450},
     year = {2021},
     note = {2021 The 4th International Conference on Electrical Engineering and Green Energy},
     issn = {2352-4847},
     doi = {https://doi.org/10.1016/j.egyr.2021.08.020},
     author = {Terrén-Serrano, Guillermo and Martínez-Ramón, Manel},
     keywords = {Cloud segmentation, Machine learning, Kernel methods, Sky imaging, Solar forecasting}
   }

5. PROCEEDINGS

   Segmentation Algorithms for Ground-Based Infrared Cloud Images

   Guillermo Terrén-Serrano and Manel Martínez-Ramón

   In 2021 IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe), Oct 2021

   Abs DOI Bib HTML

   The increasing number of Photovoltaic (PV) systems connected to the power grid are vulnerable to the projection of shadows from moving clouds. Global Solar Irradiance (GSI) forecasting allows smart grids to optimize the energy dispatch, preventing energy shortages caused by occlusion of the sun. This investigation compares the performances of machine learning algorithms (not requiring labelled images for training) for realtime segmentation of clouds in images acquired using a ground-based infrared sky imager. Real-time segmentation is utilized to extract cloud features using only the pixels in which clouds are detected.

   @inproceedings{9639922,
     author = {Terrén-Serrano, Guillermo and Martínez-Ramón, Manel},
     booktitle = {2021 IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe)},
     title = {Segmentation Algorithms for Ground-Based Infrared Cloud Images},
     year = {2021},
     volume = {},
     number = {},
     pages = {01-06},
     keywords = {Training;Image segmentation;Machine learning algorithms;Clouds;Atmospheric modeling;Europe;Feature extraction;Cloud Segmentation;Machine Learning;Solar Forecasting;Sky Imager},
     doi = {10.1109/ISGTEurope52324.2021.9639922},
     issn = {},
     month = oct
   }

6. PROCEEDINGS

   Wind Flow Estimation in Thermal Sky Images for Sun Occlusion Prediction

   Guillermo Terrén-Serrano and Manel Martínez-Ramón

   In 2021 IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe), Oct 2021

   Abs DOI Bib HTML

   Moving clouds affect the Global Solar Irradiance (GSI) that reaches the surface of the Earth. As a consequence, the amount of resources available to meet the energy demand in a smart grid powered using Photovoltaic (PV) systems depends on the shadows projected by passing clouds. This research introduces an algorithm for tracking clouds to predict Sun occlusion. Using thermal images of clouds, the algorithm is capable of estimating multiple wind velocity fields with different altitudes, velocity magnitudes and directions.

   @inproceedings{9640045,
     author = {Terrén-Serrano, Guillermo and Martínez-Ramón, Manel},
     booktitle = {2021 IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe)},
     title = {Wind Flow Estimation in Thermal Sky Images for Sun Occlusion Prediction},
     year = {2021},
     volume = {},
     number = {},
     pages = {1-5},
     keywords = {Photovoltaic systems;Visualization;Wind speed;Europe;Streaming media;Prediction algorithms;Feature extraction;Cloud Tracking;Machine Learning;Flow Visualization;Solar Forecasting;Sky Imaging},
     doi = {10.1109/ISGTEurope52324.2021.9640045},
     issn = {},
     month = oct
   }

2019

1. JOURNAL

   An experimental method to merge far-field images from multiple longwave infrared sensors for short-term solar forecasting

   Andrea Mammoli, Guillermo Terrén-Serrano, Anthony Menicucci, and 2 more authors

   Solar Energy, Oct 2019

   Abs DOI Bib HTML

   In a system used for short-term forecasting of solar irradiance, multiple longwave infrared sensors are used to acquire an image of a large, continuous section of the sky dome. The field of view of each sensor is directed at a particular, fixed portion of the sky dome, with some overlap between adjacent edges of each field of view to ensure that a continuous image can be acquired. Because of unavoidable imperfections in the optical components and in the alignment of the sensors, and because of the complex optics, it is difficult to pre-determine adjustable parameters in the geometric transformations required to merge the multiple images into a single one. Instead, it is possible to do this experimentally, by rotating the imaging sensor array via a two-axis rotating platform, using the sun itself as a convenient far-field reference object, and using the images collected to generate relations that map each sensor pixel into an altitude-azimuth direction, α and ϕ respectively. The experimental proof of concept of this method is described here.

   @article{MAMMOLI2019254,
     title = {An experimental method to merge far-field images from multiple longwave infrared sensors for short-term solar forecasting},
     journal = {Solar Energy},
     volume = {187},
     pages = {254-260},
     year = {2019},
     issn = {0038-092X},
     doi = {https://doi.org/10.1016/j.solener.2019.05.052},
     author = {Mammoli, Andrea and Terr{\'e}n-Serrano, Guillermo and Menicucci, Anthony and Caudell, Thomas P. and Martínez-Ramón, Manel},
     keywords = {Solar forecasting, Sky imaging, Longwave infrared, Image stitching}
   }

2018

1. JOURNAL

   Evaluation of dimensionality reduction methods applied to numerical weather models for solar radiation forecasting

   O. García-Hinde, G. Terrén-Serrano, M.Á. Hombrados-Herrera, and 6 more authors

   Engineering Applications of Artificial Intelligence, Oct 2018

   Abs DOI Bib HTML

   The interest in solar radiation prediction has increased greatly in recent times among the scientific community. In this context, Machine Learning techniques have shown their ability to learn accurate prediction models. The aim of this paper is to go one step further and automatically achieve interpretability during the learning process by performing dimensionality reduction on the input variables. To this end, three non standard multivariate feature selection approaches are applied, based on the adaptation of strong learning algorithms to the feature selection task, as well as a battery of classic dimensionality reduction models. The goal is to obtain robust sets of features that not only improve prediction accuracy but also provide more interpretable and consistent results. Real data from the Weather Research and Forecasting model, which produces a very large number of variables, is used as the input. As is to be expected, the results prove that dimensionality reduction in general is a useful tool for improving performance, as well as easing the interpretability of the results. In fact, the proposed non standard methods offer important accuracy improvements and one of them provides with an intuitive and reduced selection of features and mesoscale nodes (around 10% of the initial variables centered on three specific nodes).

   @article{GARCIAHINDE2018157,
     title = {Evaluation of dimensionality reduction methods applied to numerical weather models for solar radiation forecasting},
     journal = {Engineering Applications of Artificial Intelligence},
     volume = {69},
     pages = {157-167},
     year = {2018},
     issn = {0952-1976},
     doi = {https://doi.org/10.1016/j.engappai.2017.12.003},
     author = {García-Hinde, O. and Terrén-Serrano, G. and Hombrados-Herrera, M.Á. and Gómez-Verdejo, V. and Jiménez-Fernández, S. and Casanova-Mateo, C. and Sanz-Justo, J. and Martínez-Ramón, M. and Salcedo-Sanz, S.},
     keywords = {Dimensionality reduction, Interpretability, Solar radiation forecast, Weather research and forecasting model, Support vector regression, Restricted Boltzmann machine}
   }

2016

1. THESIS

   Machine Learning Approach to Forecast Global Solar Radiation Time Series

   Guillermo Terren-Serrano

   Oct 2016

   Bib HTML

   @article{terren2016machine,
     title = {Machine Learning Approach to Forecast Global Solar Radiation Time Series},
     author = {Terren-Serrano, Guillermo},
     year = {2016}
   }