547 research outputs found
Assessment of Renewable Energy Resources with Remote Sensing
The development of renewable energy sources plays a fundamental role in the transition towards a low carbon economy. Considering that renewable energy resources have an intrinsic relationship with meteorological conditions and climate patterns, methodologies based on the remote sensing of the atmosphere are fundamental sources of information to support the energy sector in planning and operation procedures. This Special Issue is intended to provide a highly recognized international forum to present recent advances in remote sensing to data acquisition required by the energy sector. After a review, a total of eleven papers were accepted for publication. The contributions focus on solar, wind, and geothermal energy resource. This editorial presents a brief overview of each contribution.About the Editor .............................................. vii
Fernando Ramos Martins
Editorial for the Special Issue: Assessment of Renewable Energy Resources with
Remote Sensing
Reprinted from: Remote Sens. 2020, 12, 3748, doi:10.3390/rs12223748 ................. 1
André R. Gonçalves, Arcilan T. Assireu, Fernando R. Martins, Madeleine S. G. Casagrande, Enrique V. Mattos, Rodrigo S. Costa, Robson B. Passos, Silvia V. Pereira, Marcelo P. Pes, Francisco J. L. Lima and Enio B. Pereira
Enhancement of Cloudless Skies Frequency over a Large Tropical Reservoir in Brazil
Reprinted from: Remote Sens. 2020, 12, 2793, doi:10.3390/rs12172793 ................. 7
Anders V. Lindfors, Axel Hertsberg, Aku Riihelä, Thomas Carlund, Jörg Trentmann and Richard Müller
On the Land-Sea Contrast in the Surface Solar Radiation (SSR) in the Baltic Region
Reprinted from: Remote Sens. 2020, 12, 3509, doi:10.3390/rs12213509 ................. 33
Joaquín Alonso-Montesinos
Real-Time Automatic Cloud Detection Using a Low-Cost Sky Camera
Reprinted from: Remote Sens. 2020, 12, 1382, doi:10.3390/rs12091382 ................. 43
Román Mondragón, Joaquín Alonso-Montesinos, David Riveros-Rosas, Mauro Valdés, Héctor Estévez, Adriana E. González-Cabrera and Wolfgang Stremme
Attenuation Factor Estimation of Direct Normal Irradiance Combining Sky Camera Images and Mathematical Models in an Inter-Tropical Area
Reprinted from: Remote Sens. 2020, 12, 1212, doi:10.3390/rs12071212 ................. 61
Jinwoong Park, Jihoon Moon, Seungmin Jung and Eenjun Hwang
Multistep-Ahead Solar Radiation Forecasting Scheme Based on the Light Gradient Boosting Machine: A Case Study of Jeju Island
Reprinted from: Remote Sens. 2020, 12, 2271, doi:10.3390/rs12142271 ................. 79
Guojiang Xiong, Jing Zhang, Dongyuan Shi, Lin Zhu, Xufeng Yuan and Gang Yao
Modified Search Strategies Assisted Crossover Whale Optimization Algorithm with Selection Operator for Parameter Extraction of Solar Photovoltaic Models
Reprinted from: Remote Sens. 2019, 11, 2795, doi:10.3390/rs11232795 ................. 101
Alexandra I. Khalyasmaa, Stanislav A. Eroshenko, Valeriy A. Tashchilin, Hariprakash Ramachandran, Teja Piepur Chakravarthi and Denis N. Butusov
Industry Experience of Developing Day-Ahead Photovoltaic Plant Forecasting System Based on Machine Learning
Reprinted from: Remote Sens. 2020, 12, 3420, doi:10.3390/rs12203420 ................. 125
Ian R. Young, Ebru Kirezci and Agustinus Ribal
The Global Wind Resource Observed by Scatterometer
Reprinted from: Remote Sens. 2020, 12, 2920, doi:10.3390/rs12182920 ................. 147
Susumu Shimada, Jay Prakash Goit, Teruo Ohsawa, Tetsuya Kogaki and Satoshi Nakamura
Coastal Wind Measurements Using a Single Scanning LiDAR
Reprinted from: Remote Sens. 2020, 12, 1347, doi:10.3390/rs12081347 ................. 165
Cristina Sáez Blázquez, Pedro Carrasco García, Ignacio Martín Nieto, MiguelAngel ´ Maté-González, Arturo Farfán Martín and Diego González-Aguilera
Characterizing Geological Heterogeneities for Geothermal Purposes through Combined Geophysical Prospecting Methods
Reprinted from: Remote Sens. 2020, 12, 1948, doi:10.3390/rs12121948 ................. 189
Miktha Farid Alkadri, Francesco De Luca, Michela Turrin and Sevil Sariyildiz
A Computational Workflow for Generating A Voxel-Based Design Approach Based on Subtractive Shading Envelopes and Attribute Information of Point Cloud Data
Reprinted from: Remote Sens. 2020, 12, 2561, doi:10.3390/rs12162561 ................. 207Instituto do Ma
Streamflow prediction using an integrated methodology based on convolutional neural network and long short‑term memory networks
Streamflow (Qflow) prediction is one of the essential steps for the reliable and robust water resources planning and management. It is highly vital for hydropower operation, agricultural planning, and flood control. In this study, the convolution neural network (CNN) and Long-Short-term Memory network (LSTM) are combined to make a new integrated model called CNN-LSTM to predict the hourly Qflow (short-term) at Brisbane River and Teewah Creek, Australia. The CNN layers were used to extract the features of Qflow time-series, while the LSTM networks use these features from CNN for Qflow time
series prediction. The proposed CNN-LSTM model is benchmarked against the standalone model CNN, LSTM, and Deep Neural Network models and several conventional artificial intelligence (AI) models. Qflow prediction is conducted for different time intervals with the length of 1-Week, 2-Weeks, 4-weeks, and 9-Months, respectively. With the help of different performance metrics and graphical analysis visualization, the experimental results reveal that with small residual error between the actual and predicted Qflow, the CNN-LSTM model outperforms all the benchmarked conventional AI models as
well as ensemble models for all the time intervals. With 84% of Qflow prediction error below the range of 0.05 m3 s− 1, CNN-LSTM demonstrates a better performance compared to 80% and 66% for LSTM and DNN, respectively. In summary, the results reveal that the proposed CNN-LSTM model based on the novel framework yields more accurate predictions. Thus, CNN-LSTM has significant practical value in Qflow prediction
Renewable Energy Resource Assessment and Forecasting
In recent years, several projects and studies have been launched towards the development and use of new methodologies, in order to assess, monitor, and support clean forms of energy. Accurate estimation of the available energy potential is of primary importance, but is not always easy to achieve. The present Special Issue on ‘Renewable Energy Resource Assessment and Forecasting’ aims to provide a holistic approach to the above issues, by presenting multidisciplinary methodologies and tools that are able to support research projects and meet today’s technical, socio-economic, and decision-making needs. In particular, research papers, reviews, and case studies on the following subjects are presented: wind, wave and solar energy; biofuels; resource assessment of combined renewable energy forms; numerical models for renewable energy forecasting; integrated forecasted systems; energy for buildings; sustainable development; resource analysis tools and statistical models; extreme value analysis and forecasting for renewable energy resources
Advanced Optimization Methods and Big Data Applications in Energy Demand Forecast
The use of data collectors in energy systems is growing more and more. For example, smart sensors are now widely used in energy production and energy consumption systems. This implies that huge amounts of data are generated and need to be analyzed in order to extract useful insights from them. Such big data give rise to a number of opportunities and challenges for informed decision making. In recent years, researchers have been working very actively in order to come up with effective and powerful techniques in order to deal with the huge amount of data available. Such approaches can be used in the context of energy production and consumption considering the amount of data produced by all samples and measurements, as well as including many additional features. With them, automated machine learning methods for extracting relevant patterns, high-performance computing, or data visualization are being successfully applied to energy demand forecasting. In light of the above, this Special Issue collects the latest research on relevant topics, in particular in energy demand forecasts, and the use of advanced optimization methods and big data techniques. Here, by energy, we mean any kind of energy, e.g., electrical, solar, microwave, or win
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Residential Demand Response using Electricity Smart Meter Data
The electricity industry is currently undergoing changes in a transitioning period characterised by Energy 3D: Digitalisation, Decentralisation, and Decarbonisation. Smart meters are the vital infrastructure necessary to digitalise the energy system as well as enable advancements in decentralisation and decarbonisation. As of today, more than 500 million smart meters have been installed worldwide, with that number expected to rise to several billion installations over the decade. Smart meters enable electricity load to be measured with half-hourly granularity, providing an opportunity for demand-side management innovations that are likely to be advantageous for both utility companies and customers. Among these innovations, time-of- use (TOU) tariffs are widely considered to be the most promising solution for optimising energy consumption in the residential sector, however actual use is still limited.
The objective of this thesis is to investigate opportunities and problems related to TOU tariffs utilising smart meter data at the national level. The authors have identified four major research gaps which need to be filled in order to expand commercial applications of TOU tariffs. These gaps are the described and addressed in the following chapters: the "TOU load adaptation forecasting problem", the "TOU winner detection problem", the "TOU public dataset problem", and the "excess generation forecasting problem".
This thesis demonstrates three modelling approaches and one new TOU dataset (CAMSL). A significant contribution to the field is through the discover of new summary statistical features (statistical moments) and assesses the capacity of these to encapsulate other more widely used explanatory variables of demand response. The thesis is concluded by discussing future works and policy implications, such as the necessity of the more tailored modelling works and public live-stream of smart meter data, which could accelerate the roll-out of the demand side management at the residential sector.EPC
Solar Power System Plaing & Design
Photovoltaic (PV) and concentrated solar power (CSP) systems for the conversion of solar energy into electricity are technologically robust, scalable, and geographically dispersed, and they possess enormous potential as sustainable energy sources. Systematic planning and design considering various factors and constraints are necessary for the successful deployment of PV and CSP systems. This book on solar power system planning and design includes 14 publications from esteemed research groups worldwide. The research and review papers in this Special Issue fall within the following broad categories: resource assessments, site evaluations, system design, performance assessments, and feasibility studies
Persistence in complex systems
Persistence is an important characteristic of many complex systems in nature, related to how long the system remains at a certain state before changing to a different one. The study of complex systems’ persistence involves different definitions and uses different techniques, depending on whether short-term or long-term persistence is considered. In this paper we discuss the most important definitions, concepts, methods, literature and latest results on persistence in complex systems. Firstly, the most used definitions of persistence in short-term and long-term cases are presented. The most relevant methods to characterize persistence are then discussed in both cases. A complete literature review is also carried out. We also present and discuss some relevant results on persistence, and give empirical evidence of performance in different detailed case studies, for both short-term and long-term persistence. A perspective on the future of persistence concludes the work.This research has been partially supported by the project PID2020-115454GB-C21 of the Spanish Ministry of Science and Innovation (MICINN). This research has also been partially supported by Comunidad de Madrid, PROMINT-CM project (grant ref: P2018/EMT-4366). J. Del Ser would like to thank the Basque Government for its funding support through the EMAITEK and ELKARTEK programs (3KIA project, KK-2020/00049), as well as the consolidated research group MATHMODE (ref. T1294-19). GCV work is supported by the European Research Council (ERC) under the ERC-CoG-2014 SEDAL Consolidator grant (grant agreement 647423)
Persistence in complex systems
Persistence is an important characteristic of many complex systems in nature, related to how long the system remains at a certain state before changing to a different one. The study of complex systems' persistence involves different definitions and uses different techniques, depending on whether short-term or long-term persistence is considered. In this paper we discuss the most important definitions, concepts, methods, literature and latest results on persistence in complex systems. Firstly, the most used definitions of persistence in short-term and long-term cases are presented. The most relevant methods to characterize persistence are then discussed in both cases. A complete literature review is also carried out. We also present and discuss some relevant results on persistence, and give empirical evidence of performance in different detailed case studies, for both short-term and long-term persistence. A perspective on the future of persistence concludes the work.This research has been partially supported by the project PID2020-115454GB-C21 of the Spanish Ministry of Science
and Innovation (MICINN). This research has also been partially supported by Comunidad de Madrid, PROMINT-CM
project (grant ref: P2018/EMT-4366). J. Del Ser would like to thank the Basque Government for its funding support
through the EMAITEK and ELKARTEK programs (3KIA project, KK-2020/00049), as well as the consolidated research group
MATHMODE (ref. T1294-19). GCV work is supported by the European Research Council (ERC) under the ERC-CoG-2014
SEDAL Consolidator grant (grant agreement 647423)
Big Data Analysis application in the renewable energy market: wind power
Entre as enerxías renovables, a enerxía eólica e unha das tecnoloxías
mundiais de rápido crecemento. Non obstante, esta incerteza debería minimizarse para programar e xestionar
mellor os activos de xeración tradicionais para compensar a falta de electricidade nas redes electricas. A aparición
de técnicas baseadas en datos ou aprendizaxe automática deu a capacidade de proporcionar predicións espaciais
e temporais de alta resolución da velocidade e potencia do vento. Neste traballo desenvólvense tres modelos
diferentes de ANN, abordando tres grandes problemas na predición de series de datos con esta técnica: garantía
de calidade de datos e imputación de datos non válidos, asignación de hiperparámetros e selección de funcións.
Os modelos desenvolvidos baséanse en técnicas de agrupación, optimización e procesamento de sinais para
proporcionar predicións de velocidade e potencia do vento a curto e medio prazo (de minutos a horas)
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