The Experts below are selected from a list of 5286 Experts worldwide ranked by ideXlab platform
Pierre Pinson - One of the best experts on this subject based on the ideXlab platform.
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towards data markets in Renewable Energy Forecasting
IEEE Transactions on Sustainable Energy, 2021Co-Authors: Carla Gonçalves, Pierre Pinson, Ricardo J. BessaAbstract:Geographically distributed wind turbines, photovoltaic panels and sensors (e.g., pyranometers) produce large volumes of data that can be used to improve Renewable Energy sources (RES) Forecasting skill. However, data owners may be unwilling to share their data, even if privacy is ensured, due to a form of prisoner's dilemma: all could benefit from data sharing, but in practice no one is willing to do do. Our proposal hence consists of a data marketplace, to incentivize collaboration between different data owners through the monetization of data. We adapt here an existing auction mechanism to the case of RES Forecasting data. It accommodates the temporal nature of the data, i.e., lagged time-series act as covariates and models are updated continuously using a sliding window. A test case with wind Energy data is presented to illustrate and assess the effectiveness of such data markets. All agents (or data owners) are shown to benefit in terms of higher revenue resulting from the combination of electricity and data markets. The results support the idea that data markets can be a viable solution to promote data exchange between RES agents and contribute to reducing system imbalance costs.
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Towards Data Markets in Renewable Energy Forecasting
IEEE Transactions on Sustainable Energy, 2020Co-Authors: Carla Gonçalves, Pierre Pinson, Ricardo J. BessaAbstract:Geographically distributed wind turbines, photovoltaic panels and sensors (e.g., pyranometers) produce large volumes of data that can be used to improve Renewable Energy Forecasting skill. However, data owners may be unwilling to share their data, even if privacy is ensured, due to a form of prisoner's dilemma: all could benefit from data sharing, but in practice no one is willing to do do. Our proposal hence consists of a data marketplace, to incentivize collaboration between different data owners through the monetization of data.We adapt here an existing auction mechanism to the case of Renewable Energy Forecasting data. It accommodates the temporal nature of the data, i.e., lagged time-series act as covariates and models are updated continuously using a sliding window. A test case with wind Energy data is presented to illustrate and assess the effectiveness of such data markets. All agents (or data owners) are shown to benefit in terms of higher revenue resulting from the combination of electricity and data markets. The results support the idea that data markets can be a viable solution to promote data exchange between Renewable Energy agents and contribute to reducing system imbalance costs.
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Smart4RES: Towards next generation Forecasting tools of Renewable Energy production
2020Co-Authors: Georges Kariniotakis, Pierre Pinson, Ricardo J. Bessa, Simon Camal, Gregor Giebel, Quentin Libois, Raphaël Legrand, Matthias Lange, Stefan Wilbert, Bijan NouriAbstract:The aim of this paper is to present the objectives, research directions and first highlight results of the Smart4RES project, which was launched in November 2019, under the Horizon 2020 Framework Programme. Smart4RES is a research project that aims to bring substantial performance improvements to the whole model and value chain in Renewable Energy (RES) Forecasting, with particular emphasis placed on optimizing synergies with storage and to support power system operation and participation in electricity markets. For that, it concentrates on a number of disruptive proposals to support ambitious objectives for the future of Renewable Energy Forecasting. This is thought of in a context with steady increase in the quantity of data being collected and computational capabilities. And, this comes in combination with recent advances in data science and approaches to meteorological Forecasting. Smart4RES concentrates on novel developments towards very high-resolution and dedicated weather Forecasting solutions. It makes optimal use of varied and distributed sources of data e.g. remote sensing (sky imagers, satellites, etc), power and meteorological measurements, as well as high-resolution weather forecasts, to yield high-quality and seamless approaches to Renewable Energy Forecasting. The project accommodates the fact that all these sources of data are distributed geographically and in terms of ownership, with current restrictions preventing sharing. Novel alternative approaches are to be developed and evaluated to reach optimal forecast accuracy in that context, including distributed and privacy-preserving learning and Forecasting methods, as well as the advent of platform-enabled data-markets, with associated pricing strategies. Smart4RES places a strong emphasis on maximizing the value from the use of forecasts in applications through advanced decision making and optimization approaches. This also goes through approaches to streamline the definition of new Forecasting products balancing the complexity of forecast information and the need of forecast users. Focus is on developing models for applications involving storage, the provision of ancillary services, as well as market participation.
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Application of Postprocessing for Renewable Energy
Statistical Postprocessing of Ensemble Forecasts, 2018Co-Authors: Pierre Pinson, Jakob W. MessnerAbstract:Abstract Renewable Energy generation capacities are being deployed at a rapid pace, now reaching a total of more than 800 GW worldwide, if adding up generation capacities for wind and solar power. Power generation from those sources is tightly linked to weather conditions, hence making Renewable Energy Forecasting tightly linked to meteorological Forecasting. Even though probabilistic forecasts of Renewable power generation still often rely on deterministic weather forecasts, ensemble forecasts will necessarily be an increasing trend in order to issue the most complete information for future power generation. The basic and key concepts of postprocessing ensemble weather forecasts for Renewable Energy applications are described and discussed here, based on the example of wind power generation. These include the conversion of ensemble weather forecasts to power and generation of calibrated forecast products. Finally, perspectives regarding future developments in this area are given.
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very short term nonparametric probabilistic Forecasting of Renewable Energy generation with application to solar Energy
IEEE Transactions on Power Systems, 2016Co-Authors: Faranak Golestaneh, Pierre Pinson, H B GooiAbstract:Due to the inherent uncertainty involved in Renewable Energy Forecasting, uncertainty quantification is a key input to maintain acceptable levels of reliability and profitability in power system operation. A proposal is formulated and evaluated here for the case of solar power generation, when only power and meteorological measurements are available, without sky-imaging and information about cloud passages. Our empirical investigation reveals that the distribution of forecast errors do not follow any of the common parametric densities. This therefore motivates the proposal of a nonparametric approach to generate very short-term predictive densities, i.e., for lead times between a few minutes to one hour ahead, with fast frequency updates. We rely on an Extreme Learning Machine (ELM) as a fast regression model, trained in varied ways to obtain both point and quantile forecasts of solar power generation. Four probabilistic methods are implemented as benchmarks. Rival approaches are evaluated based on a number of test cases for two solar power generation sites in different climatic regions, allowing us to show that our approach results in generation of skilful and reliable probabilistic forecasts in a computationally efficient manner.
Georges Kariniotakis - One of the best experts on this subject based on the ideXlab platform.
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Smart4RES: Towards next generation Forecasting tools of Renewable Energy production
2020Co-Authors: Georges Kariniotakis, Pierre Pinson, Ricardo J. Bessa, Simon Camal, Gregor Giebel, Quentin Libois, Raphaël Legrand, Matthias Lange, Stefan Wilbert, Bijan NouriAbstract:The aim of this paper is to present the objectives, research directions and first highlight results of the Smart4RES project, which was launched in November 2019, under the Horizon 2020 Framework Programme. Smart4RES is a research project that aims to bring substantial performance improvements to the whole model and value chain in Renewable Energy (RES) Forecasting, with particular emphasis placed on optimizing synergies with storage and to support power system operation and participation in electricity markets. For that, it concentrates on a number of disruptive proposals to support ambitious objectives for the future of Renewable Energy Forecasting. This is thought of in a context with steady increase in the quantity of data being collected and computational capabilities. And, this comes in combination with recent advances in data science and approaches to meteorological Forecasting. Smart4RES concentrates on novel developments towards very high-resolution and dedicated weather Forecasting solutions. It makes optimal use of varied and distributed sources of data e.g. remote sensing (sky imagers, satellites, etc), power and meteorological measurements, as well as high-resolution weather forecasts, to yield high-quality and seamless approaches to Renewable Energy Forecasting. The project accommodates the fact that all these sources of data are distributed geographically and in terms of ownership, with current restrictions preventing sharing. Novel alternative approaches are to be developed and evaluated to reach optimal forecast accuracy in that context, including distributed and privacy-preserving learning and Forecasting methods, as well as the advent of platform-enabled data-markets, with associated pricing strategies. Smart4RES places a strong emphasis on maximizing the value from the use of forecasts in applications through advanced decision making and optimization approaches. This also goes through approaches to streamline the definition of new Forecasting products balancing the complexity of forecast information and the need of forecast users. Focus is on developing models for applications involving storage, the provision of ancillary services, as well as market participation.
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Renewable Energy Forecasting: From Models to Applications
2017Co-Authors: Georges KariniotakisAbstract:Renewable Energy Forecasting: From Models to Applications provides an overview of the state-of-the-art of Renewable Energy Forecasting technology and its applications. After an introduction to the principles of meteorology and Renewable Energy generation, groups of chapters address Forecasting models, very short-term Forecasting, Forecasting of extremes, and longer term Forecasting. The final part of the book focuses on important applications of Forecasting for power system management and in Energy markets. Due to shrinking fossil fuel reserves and concerns about climate change, Renewable Energy holds an increasing share of the Energy mix. Solar, wind, wave, and hydro Energy are dependent on highly variable weather conditions, so their increased penetration will lead to strong fluctuations in the power injected into the electricity grid, which needs to be managed. Reliable, high quality forecasts of Renewable power generation are therefore essential for the smooth integration of large amounts of solar, wind, wave, and hydropower into the grid as well as for the profitability and effectiveness of such Renewable Energy projects.
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The “Weather Intelligence for Renewable Energies” Benchmarking Exercise on Short-Term Forecasting of Wind and Solar Power Generation
Energies, 2015Co-Authors: Simone Sperati, Stefano Alessandrini, Pierre Pinson, Georges KariniotakisAbstract:A benchmarking exercise was organized within the framework of the European Action Weather Intelligence for Renewable Energies (“WIRE”) with the purpose of evaluating the performance of state of the art models for short-term Renewable Energy Forecasting. The exercise consisted in Forecasting the power output of two wind farms and two photovoltaic power plants, in order to compare the merits of forecasts based on different modeling approaches and input data. It was thus possible to obtain a better knowledge of the state of the art in both wind and solar power Forecasting, with an overview and comparison of the principal and the novel approaches that are used today in the field, and to assess the evolution of forecast performance with respect to previous benchmarking exercises. The outcome of this exercise consisted then in proposing new challenges in the Renewable power Forecasting field and identifying the main areas for improving accuracy in the future.
Stefano Alessandrini - One of the best experts on this subject based on the ideXlab platform.
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Combining Artificial Intelligence with Physics-Based Methods for Probabilistic Renewable Energy Forecasting
Energies, 2020Co-Authors: Sue Ellen Haupt, Tyler Mccandless, Susan Dettling, Stefano Alessandrini, Jared A. Lee, Seth Linden, William Petzke, Thomas Brummet, Nhi Nguyen, Branko KosovicAbstract:A modern Renewable Energy Forecasting system blends physical models with artificial intelligence to aid in system operation and grid integration. This paper describes such a system being developed for the Shagaya Renewable Energy Park, which is being developed by the State of Kuwait. The park contains wind turbines, photovoltaic panels, and concentrated solar Renewable Energy technologies with storage capabilities. The fully operational Kuwait Renewable Energy Prediction System (KREPS) employs artificial intelligence (AI) in multiple portions of the Forecasting structure and processes, both for short-range Forecasting (i.e., the next six hours) as well as for forecasts several days out. These AI methods work synergistically with the dynamical/physical models employed. This paper briefly describes the methodology used for each of the AI methods, how they are blended, and provides a preliminary assessment of their relative value to the prediction system. Each operational AI component adds value to the system. KREPS is an example of a fully integrated state-of-the-science Forecasting system for Renewable Energy.
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An ultra-fast way of searching weather analogs for Renewable Energy Forecasting
Solar Energy, 2019Co-Authors: Dazhi Yang, Stefano AlessandriniAbstract:Abstract Analogs—weather patterns that highly resemble each other—have been widely adopted by the meteorology and Renewable Energy communities for predictive applications. It has been repeatedly demonstrated that by searching for and using past analogs, an analog ensemble (AnEn) can be constructed, circumventing or complementing computationally expensive dynamical ensemble systems. Of course, the pattern matching required by the AnEn benefits from larger historical datasets. However, brute-force analog searches become impractical with larger training datasets. To overcome this challenge, this paper introduces a rapid method for finding analogs. This method is referred to as Mueen’s algorithm for similarity search (MASS). MASS has many desirable properties in that it is exact, non-parametric, scalable, parallelizable and most notably, free from the curse of dimensionality. MASS can also be easily extended to multivariate cases. In a case study, 20 years of 1-h averaged ground-based multivariate weather data are used to exemplify a typical AnEn setup. It is found that MASS is about 100 times faster than the brute-force algorithm. MASS is suitable for all Euclidean distance-based pattern-matching tasks.
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The “Weather Intelligence for Renewable Energies” Benchmarking Exercise on Short-Term Forecasting of Wind and Solar Power Generation
Energies, 2015Co-Authors: Simone Sperati, Stefano Alessandrini, Pierre Pinson, Georges KariniotakisAbstract:A benchmarking exercise was organized within the framework of the European Action Weather Intelligence for Renewable Energies (“WIRE”) with the purpose of evaluating the performance of state of the art models for short-term Renewable Energy Forecasting. The exercise consisted in Forecasting the power output of two wind farms and two photovoltaic power plants, in order to compare the merits of forecasts based on different modeling approaches and input data. It was thus possible to obtain a better knowledge of the state of the art in both wind and solar power Forecasting, with an overview and comparison of the principal and the novel approaches that are used today in the field, and to assess the evolution of forecast performance with respect to previous benchmarking exercises. The outcome of this exercise consisted then in proposing new challenges in the Renewable power Forecasting field and identifying the main areas for improving accuracy in the future.
Ricardo J. Bessa - One of the best experts on this subject based on the ideXlab platform.
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towards data markets in Renewable Energy Forecasting
IEEE Transactions on Sustainable Energy, 2021Co-Authors: Carla Gonçalves, Pierre Pinson, Ricardo J. BessaAbstract:Geographically distributed wind turbines, photovoltaic panels and sensors (e.g., pyranometers) produce large volumes of data that can be used to improve Renewable Energy sources (RES) Forecasting skill. However, data owners may be unwilling to share their data, even if privacy is ensured, due to a form of prisoner's dilemma: all could benefit from data sharing, but in practice no one is willing to do do. Our proposal hence consists of a data marketplace, to incentivize collaboration between different data owners through the monetization of data. We adapt here an existing auction mechanism to the case of RES Forecasting data. It accommodates the temporal nature of the data, i.e., lagged time-series act as covariates and models are updated continuously using a sliding window. A test case with wind Energy data is presented to illustrate and assess the effectiveness of such data markets. All agents (or data owners) are shown to benefit in terms of higher revenue resulting from the combination of electricity and data markets. The results support the idea that data markets can be a viable solution to promote data exchange between RES agents and contribute to reducing system imbalance costs.
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Towards Data Markets in Renewable Energy Forecasting
IEEE Transactions on Sustainable Energy, 2020Co-Authors: Carla Gonçalves, Pierre Pinson, Ricardo J. BessaAbstract:Geographically distributed wind turbines, photovoltaic panels and sensors (e.g., pyranometers) produce large volumes of data that can be used to improve Renewable Energy Forecasting skill. However, data owners may be unwilling to share their data, even if privacy is ensured, due to a form of prisoner's dilemma: all could benefit from data sharing, but in practice no one is willing to do do. Our proposal hence consists of a data marketplace, to incentivize collaboration between different data owners through the monetization of data.We adapt here an existing auction mechanism to the case of Renewable Energy Forecasting data. It accommodates the temporal nature of the data, i.e., lagged time-series act as covariates and models are updated continuously using a sliding window. A test case with wind Energy data is presented to illustrate and assess the effectiveness of such data markets. All agents (or data owners) are shown to benefit in terms of higher revenue resulting from the combination of electricity and data markets. The results support the idea that data markets can be a viable solution to promote data exchange between Renewable Energy agents and contribute to reducing system imbalance costs.
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Smart4RES: Towards next generation Forecasting tools of Renewable Energy production
2020Co-Authors: Georges Kariniotakis, Pierre Pinson, Ricardo J. Bessa, Simon Camal, Gregor Giebel, Quentin Libois, Raphaël Legrand, Matthias Lange, Stefan Wilbert, Bijan NouriAbstract:The aim of this paper is to present the objectives, research directions and first highlight results of the Smart4RES project, which was launched in November 2019, under the Horizon 2020 Framework Programme. Smart4RES is a research project that aims to bring substantial performance improvements to the whole model and value chain in Renewable Energy (RES) Forecasting, with particular emphasis placed on optimizing synergies with storage and to support power system operation and participation in electricity markets. For that, it concentrates on a number of disruptive proposals to support ambitious objectives for the future of Renewable Energy Forecasting. This is thought of in a context with steady increase in the quantity of data being collected and computational capabilities. And, this comes in combination with recent advances in data science and approaches to meteorological Forecasting. Smart4RES concentrates on novel developments towards very high-resolution and dedicated weather Forecasting solutions. It makes optimal use of varied and distributed sources of data e.g. remote sensing (sky imagers, satellites, etc), power and meteorological measurements, as well as high-resolution weather forecasts, to yield high-quality and seamless approaches to Renewable Energy Forecasting. The project accommodates the fact that all these sources of data are distributed geographically and in terms of ownership, with current restrictions preventing sharing. Novel alternative approaches are to be developed and evaluated to reach optimal forecast accuracy in that context, including distributed and privacy-preserving learning and Forecasting methods, as well as the advent of platform-enabled data-markets, with associated pricing strategies. Smart4RES places a strong emphasis on maximizing the value from the use of forecasts in applications through advanced decision making and optimization approaches. This also goes through approaches to streamline the definition of new Forecasting products balancing the complexity of forecast information and the need of forecast users. Focus is on developing models for applications involving storage, the provision of ancillary services, as well as market participation.
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Data Economy for Prosumers in a Smart Grid Ecosystem
2019Co-Authors: Ricardo J. Bessa, Carla Gonçalves, Jose R Andrade, D. Rua, Cláudia Abreu, Paulo Machado, Rui Pinto, Marisa ReisAbstract:Smart grids technologies are enablers of new business models for domestic consumers with local flexibility (generation, loads, storage) and where access to data is a key requirement in the value stream. However, legislation on personal data privacy and protection imposes the need to develop local models for flexibility modeling and Forecasting and exchange models instead of personal data. This paper describes the functional architecture of an home Energy management system (HEMS) and its optimization functions. A set of data-driven models, embedded in the HEMS, are discussed for improving Renewable Energy Forecasting skill and modeling multi-period flexibility of distributed Energy resources.
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e-Energy - Data Economy for Prosumers in a Smart Grid Ecosystem
Proceedings of the Ninth International Conference on Future Energy Systems, 2018Co-Authors: Ricardo J. Bessa, Carla Gonçalves, Jose R Andrade, D. Rua, Cláudia Abreu, Paulo Machado, Rui Pinto, Marisa ReisAbstract:Smart grids technologies are enablers of new business models for domestic consumers with local flexibility (generation, loads, storage) and where access to data is a key requirement in the value stream. However, legislation on personal data privacy and protection imposes the need to develop local models for flexibility modeling and Forecasting and exchange models instead of personal data. This paper describes the functional architecture of an home Energy management system (HEMS) and its optimization functions. A set of data-driven models, embedded in the HEMS, are discussed for improving Renewable Energy Forecasting skill and modeling multi-period flexibility of distributed Energy resources.
H B Gooi - One of the best experts on this subject based on the ideXlab platform.
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very short term nonparametric probabilistic Forecasting of Renewable Energy generation with application to solar Energy
IEEE Transactions on Power Systems, 2016Co-Authors: Faranak Golestaneh, Pierre Pinson, H B GooiAbstract:Due to the inherent uncertainty involved in Renewable Energy Forecasting, uncertainty quantification is a key input to maintain acceptable levels of reliability and profitability in power system operation. A proposal is formulated and evaluated here for the case of solar power generation, when only power and meteorological measurements are available, without sky-imaging and information about cloud passages. Our empirical investigation reveals that the distribution of forecast errors do not follow any of the common parametric densities. This therefore motivates the proposal of a nonparametric approach to generate very short-term predictive densities, i.e., for lead times between a few minutes to one hour ahead, with fast frequency updates. We rely on an Extreme Learning Machine (ELM) as a fast regression model, trained in varied ways to obtain both point and quantile forecasts of solar power generation. Four probabilistic methods are implemented as benchmarks. Rival approaches are evaluated based on a number of test cases for two solar power generation sites in different climatic regions, allowing us to show that our approach results in generation of skilful and reliable probabilistic forecasts in a computationally efficient manner.