arXiv subjects

Guillermo Terrén-Serrano

Publications and source records attributed to Guillermo Terrén-Serrano.

14 recordsLinked to original sources

arXiv2026

Nearest Neighbor Functional Curve Completion for Intraday Probabilistic Revisions of Renewable Generation Forecasts

We propose a nonparametric approach that fuses deterministic initial forecasts and a partial realization into a probabilistic forecast using the framework of functional curve completion. Our motivation is to construct intra-day forecasts for renewable energy generation, which is a key operational task in modern power grids. The aim is to combine the day-ahead (DA) forecast and the morning realized generation profile to create trajectories for the rest of the day, quantifying operational uncertainty. Starting with a large database of complete forecast-actual pairs across many assets, we adopt a functional point of view and construct a weighted neighborhood based on similarity metrics for the observed partial curve, the DA forecast, the day, and the geographical location. The selected complete nearest-neighbor curves provide a full predictive distribution, including functional focal envelopes and predictive bands based on functional depth. The hyperparameters governing neighborhood size, distances, and fusion rates are tuned across forecast update times and directly target probabilistic calibration in terms of the predictive probability integral transform. Three case studies using synthetic wind and solar assets, as well as real-life regional wind generation, showcase that our algorithm is able to achieve excellent calibration.

cs.LG↗

arXiv2026

A multi-model framework for identifying affordable low-carbon electricity pathways for India under uncertainty

To meet climate goals, countries must decarbonize their electricity systems. Yet, uncertainty in future technology costs, electricity demand, and renewable energy generation complicates power system planning and makes the affordability of clean electricity uncertain. Here, we develop a multi-model framework to examine cost-optimal pathways for generation, storage, and transmission expansion in India under alternative technology costs, electricity demand projections, and clean energy and carbon-emission targets. Across all scenarios, real average system costs remain below 2020 levels throughout 2030-2050, even when carbon emissions decline linearly to 90\% below current levels by 2050. Solar PV comprises over half to three-quarters of total installed capacity by 2050 supported by large-scale deployment of short-duration battery storage. Expanding green hydrogen, pumped hydro storage, and nuclear reduces costs by less than 2\%, whereas demand response lowers costs by up to 10\%. Declining renewable and battery costs, together with demand response, make deep electricity decarbonization affordable across a range of realistic scenarios.

cs.CE↗

arXiv2023

Extreme Scenario Selection in Day-Ahead Power Grid Operational Planning

We propose and analyze the application of statistical functional depth metrics for the selection of extreme scenarios in day-ahead grid planning. Our primary motivation is screening of probabilistic scenarios for realized load and renewable generation, in order to identify scenarios 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, reserves shortfall and variable renewable energy curtailment. The effectiveness of the proposed screening approach is demonstrated through a case study on the realistic Texas-7k grid.

stat.ML↗

arXiv2021

Processing of Global Solar Irradiance and Ground-Based Infrared Sky Images for Solar Nowcasting and Intra-Hour Forecasting Applications

The projection of shadows from moving clouds in the troposphere impacts energy generation in power grids using photovoltaic systems. This investigation proposes an efficient method of data processing for the statistical quantification of cloud features using infrared images and global solar irradiance measurements. The infrared images and the global solar irradiance measurements are acquired using a sky imager equipped with a commercial low-cost long-wave infrared radiometric camera and a pyranometer. The enclosure of the infrared camera is mounted on a solar tracker so that the Sun stays in the center of images throughout the day. We explain how to remove cyclostationary biases in global solar irradiance measurements. Seasonal trends are removed from the global solar irradiance time series, using the theoretical global solar irradiance to obtain the clear sky index time series. We introduce an atmospheric model to remove the effect of atmospheric scattering and the effect of the Sun's direct irradiance from infrared images. Scattering is produced by water spots and dust particles on the germanium lens of the camera enclosure. We explain how to remove the scattering effect produced by the germanium lens attached to the data acquisition system enclosure window of the infrared camera. An atmospheric condition model classifies the sky conditions in four different categories: clear sky, cumulus, stratus and nimbus. When an infrared image is classified in the category of clear sky, it is used to model the scattering effect produced by the germanium lens.

astro-ph.IM↗

arXiv2021

Geospatial Perspective Reprojections for Ground-Based Sky Imaging System

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 normal. Thus, the pixels in the image contain information from different areas of the sky within the imaging system field of view. The area of the field of view 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 plane to the geospatial atmosphere cross-section where the sky imager field of view 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 \emph{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 $30^\circ$.

astro-ph.IM↗

arXiv2021

Review of Kernel Learning for Intra-Hour Solar Forecasting with Infrared Sky Images and Cloud Dynamic Feature Extraction

The uncertainty of the energy generated by photovoltaic systems incurs an additional cost for a guaranteed, reliable supply of energy (i.e., energy storage). This investigation aims to decrease the additional cost by introducing probabilistic multi-task intra-hour solar forecasting (feasible in real time applications) to increase the penetration of photovoltaic systems in power grids. The direction of moving clouds is estimated in consecutive sequences of sky images by extracting features of cloud dynamics with the objective of forecasting the global solar irradiance that reaches photovoltaic systems. 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 predictor and features extracted from clouds as feature vectors. The proposed solar forecasting algorithm achieved 16.45\% forecasting skill 8 minutes ahead with a resolution of 15 seconds. In contrast, previous work reached 15.4\% forecasting skill with the resolution of 1 minute. Therefore, this solar forecasting algorithm increases the performances with respect to the state-of-the-art, providing grid operators with the capability of managing the inherent uncertainties of power grids with a high penetration of photovoltaic systems.

cs.LG↗

arXiv2021

Segmentation Algorithms for Ground-Based Infrared Cloud Images

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 real-time 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.

eess.IV↗

arXiv2021

Detection of Clouds in Multiple Wind Velocity Fields using Ground-based Infrared Sky Images

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. To increase the performance of an intra-hour global solar irradiance forecasting algorithm, it is important to detect multiple layers of clouds. 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. The optimal decision criterion to find the number of clusters in the mixture models is analyzed and compared between different Bayesian metrics and a sequential hidden Markov model. The motion vectors are computed using a weighted implementation of the Lucas-Kanade algorithm. The correlations between the cloud velocity vectors and temperatures are analyzed to find the method that leads to the most accurate results. We have found that the sequential hidden Markov model outperformed the detection accuracy of the Bayesian metrics.

eess.IV↗

arXiv2021

Explicit Basis Function Kernel Methods for Cloud Segmentation in Infrared Sky Images

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 neighboring features of the pixels 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.

eess.IV↗

arXiv2021

Comparative Analysis of Methods for Cloud Segmentation in Ground-Based Infrared Images

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.

eess.IV↗

arXiv2021

Wind Flow Estimation in Thermal Sky Images for Sun Occlusion Prediction

Moving clouds affect the global solar irradiance 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.

eess.IV↗

arXiv2021

Girasol, a Sky Imaging and Global Solar Irradiance Dataset

eess.IV↗

arXiv2021

Multi-Layer Wind Velocity Field Visualization in Infrared Images of Clouds for Solar Irradiance Forecasting

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.

eess.IV↗

arXiv2020

Data acquisition and image processing for solar irradiance forecasting

The energy available in Micro Grid (MG) that is powered by solar energy is tightly related to the weather conditions in the moment of generation. Very short-term forecast of solar irradiance provides the MG with the capability of automatically controlling the dispatch of energy. To achieve this, we propose a method for statistical quantification of cloud features extracted from long-ware infrared (IR) images to forecast the Clear Sky Index (CSI). The images are obtained using a data acquisition system (DAQ) mounted on a solar tracker. We explain how to remove cyclostationary bias in the data caused by the devices in the own DAQ. We investigate a method to obtain the CSI, after the detrending of Global Horizontal Irradiance (GHI) measurements. We propose a method to fusion multiple exposures of circumsolar visible (VI) light images. We implement a method for extracting physical features using radiometric measurements of the IR camera. We introduce a model to remove from IR images both the effect of the atmosphere scatter radiation, and the effect of the Sun direct radiation. We explain how to model of diffuse radiation of the IR camera window, which is produce by water spots and dust particles stack to the germanium lens of the DAQ enclosure. The frames, that were used to model the camera window, are selected using an atmospheric condition model. This model classifies the sky four different categories: clear, cumulus, stratus, and nimbus. We introduce a geometric transformation of the size of the pixels to their actual dimension in a plane of the atmosphere which is at a given height. This transformation is performed according to the elevation angle of the Sun and field of view (FOV) of the camera. We compare the error between the transformation and anapproximation of transformation.

eess.IV↗