Guillermo Terrén-Serrano

Bio: Guillermo Terrén-Serrano is an academic researcher from University of New Mexico. The author has contributed to research in topics: Solar irradiance & Sky. The author has an hindex of 5, co-authored 19 publications receiving 77 citations.

Data acquisition and image processing for solar irradiance forecasting

Abstract: 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.

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Feature selection in machine learning prediction systems for renewable energy applications

Abstract: This paper focuses on feature selection problems that arise in renewable energy applications. Feature selection is an important problem in machine learning, both in classification and regression problems. In renewable energy systems, feature selection appears related to prediction systems in the most important sources such as wind, solar and marine resources. The objective of the paper is twofold: first, a review of the most important prediction systems for renewable energy applications involving feature selection is carried out. Analysis and discussion of different feature selection problems in prediction systems are considered. We show that wrapper FSP approaches are those mostly used due to their higher performance. They include a diversity of algorithms, prevailing fast-training approaches. The lack of an uniform framework for FSP and the diversity of tackled problems impede a systematic assessment of the performance and properties of the applied methods. Thus, the simultaneously use of several global search mechanisms should be the preferred option. In a second part of the paper, we explore this possibility, by introducing a novel approach for feature selection based on a novel meta-heuristic, the Coral Reefs Optimization algorithm with Substrate Layer. This approach is able to combine different search mechanisms into a single algorithm, providing a global search procedure of high quality. We use an Extreme Learning Machine for prediction within this novel approach. The performance of the system is evaluated in a problem of wind speed prediction from numerical models input, using real data from a wind farm in Spain, where comparison with alternative regression algorithms is carried out. Improvements up to 20% in hourly and daily wind speed prediction are obtained with the proposed system versus the algorithms without the feature selection process considered.

Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm

Abstract: The current trend indicates that energy demand and supply will eventually be controlled by autonomous software that optimizes decision-making and energy distribution operations. New state-of-the-art machine learning (ML) technologies are integral in optimizing decision-making in energy distribution networks and systems. This study was conducted on data-driven probabilistic ML techniques and their real-time applications to smart energy systems and networks to highlight the urgency of this area of research. This study focused on two key areas: i) the use of ML in core energy technologies and ii) the use cases of ML for energy distribution utilities. The core energy technologies include the use of ML in advanced energy materials, energy systems and storage devices, energy efficiency, smart energy material manufacturing in the smart grid paradigm, strategic energy planning, integration of renewable energy, and big data analytics in the smart grid environment. The investigated ML area in energy distribution systems includes energy consumption and price forecasting, the merit order of energy price forecasting, and the consumer lifetime value. Cybersecurity topics for power delivery and utilization, grid edge systems and distributed energy resources, power transmission, and distribution systems are also briefly studied. The primary goal of this work was to identify common issues useful in future studies on ML for smooth energy distribution operations. This study was concluded with many energy perspectives on significant opportunities and challenges. It is noted that if the smart ML automation is used in its targeting energy systems, the utility sector and energy industry could potentially save from $237 billion up to $813 billion. • A study on data-driven probabilistic machine learning (ML) in sustainable smart energy/smart energy systems is conducted. • The use of probabilistic ML in core energy technologies are briefly studied. • The ML techniques play a key role in integrating thermal, electric, large-scale renewable energy resources and fuel gird.A variety of tools for implementing ML in energy systems control, efficient management, and operations are discussed. • Recent key developments of ML, its challenges, and state-of-art future research opportunities are briefly described.