The Experts below are selected from a list of 30726 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, 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, 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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probabilistic Energy Forecasting global Energy Forecasting competition 2014 and beyond
International Journal of Forecasting, 2016Co-Authors: Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli, Rob J HyndmanAbstract:The Energy industry has been going through a significant modernization process over the last decade. Its infrastructure is being upgraded rapidly. The supply, demand and prices are becoming more volatile and less predictable than ever before. Even its business model is being challenged fundamentally. In this competitive and dynamic environment, many decision-making processes rely on probabilistic forecasts to quantify the uncertain future. Although most of the papers in the Energy Forecasting literature focus on point or single-valued forecasts, the research interest in probabilistic Energy Forecasting research has taken off rapidly in recent years. In this paper, we summarize the recent research progress on probabilistic Energy Forecasting. A major portion of the paper is devoted to introducing the Global Energy Forecasting Competition 2014 (GEFCom2014), a probabilistic Energy Forecasting competition with four tracks on load, price, wind and solar Forecasting, which attracted 581 participants from 61 countries. We conclude the paper with 12 predictions for the next decade of Energy Forecasting.
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wind Energy Forecasting challenges for its operational management
Statistical Science, 2013Co-Authors: Pierre PinsonAbstract:Renewable Energy sources, especially wind Energy, are to play a larger role in providing electricity to industrial and domestic consumers. This is already the case today for a number of European countries, closely followed by the US and high growth countries, for example, Brazil, India and China. There exist a number of technological, environmental and political challenges linked to supplementing existing electricity generation capacities with wind Energy. Here, mathematicians and statisticians could make a substantial contribution at the interface of meteorology and decision-making, in connection with the generation of forecasts tailored to the various operational decision problems involved. Indeed, while wind Energy may be seen as an environmentally friendly source of Energy, full benefits from its usage can only be obtained if one is able to accommodate its variability and limited predictability. Based on a short presentation of its physical basics, the importance of considering wind power generation as a stochastic process is motivated. After describing representative operational decision-making problems for both market participants and system operators, it is underlined that forecasts should be issued in a probabilistic framework. Even though, eventually, the forecaster may only communicate single-valued predictions. The existing approaches to wind power Forecasting are subsequently described, with focus on single-valued predictions, predictive marginal densities and space–time trajectories. Upcoming challenges related to generating improved and new types of forecasts, as well as their verification and value to forecast users, are finally discussed.
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global Energy Forecasting competition 2012
HSC Research Reports, 2013Co-Authors: Tao Hong, Pierre Pinson, Shu Fan, Tao Hong, Pierre Pinson, Shu FanAbstract:The Global Energy Forecasting Competition (GEFCom2012) attracted hundreds of participants worldwide, who contributed many novel ideas to the Energy Forecasting field. This paper introduces both tracks of GEFCom2012, hierarchical load Forecasting and wind power Forecasting, with details on the aspects of the problem, the data, and a summary of the methods used by selected top entries. We also discuss the lessons learned from this competition from the organizers’ perspective. The complete data set, including the solution data, is published along with this paper, in an effort to establish a benchmark data pool for the community.
Juan-josé González-de-la-rosa - One of the best experts on this subject based on the ideXlab platform.
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Demand and Storage Management in a Prosumer Nanogrid Based on Energy Forecasting
Electronics, 2020Co-Authors: Eva Gonzalez-romera, Enrique Romero-cadaval, Joaquin Garrido-zafra, Olivia Florencias-oliveros, Mercedes Ruiz-cortes, Antonio Moreno-munoz, Juan-josé González-de-la-rosaAbstract:Energy efficiency and consumers’ role in the Energy system are among the strategic research topics in power systems these days. Smart grids (SG) and, specifically, microgrids, are key tools for these purposes. This paper presents a three-stage strategy for Energy management in a prosumer nanogrid. Firstly, Energy monitoring is performed and time-space compression is applied as a tool for Forecasting Energy resources and power quality (PQ) indices; secondly, demand is managed, taking advantage of smart appliances (SA) to reduce the electricity bill; finally, Energy storage systems (ESS) are also managed to better match the forecasted generation of each prosumer. Results show how these strategies can be coordinated to contribute to Energy management in the prosumer nanogrid. A simulation test is included, which proves how effectively the prosumers’ power converters track the power setpoints obtained from the proposed strategy.
Tong Niu - One of the best experts on this subject based on the ideXlab platform.
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hybrid wind Energy Forecasting and analysis system based on divide and conquer scheme a case study in china
Journal of Cleaner Production, 2019Co-Authors: Jianzhou Wang, Wendong Yang, Tong NiuAbstract:Abstract Wind Energy, acknowledged as a promising form of renewable Energy and the fastest-growing clean method for electricity generation, has attracted considerable attention from many scientists and researchers in recent decades. However, wind Energy Forecasting is still a challenging task owing to its inherent features of non-linearity and randomness. Therefore, this study develops a hybrid wind Energy Forecasting and analysis system including a deterministic Forecasting module and an uncertainty analysis module to mitigate the challenges in existing studies. In particular, these challenges are as follows: (1) It is difficult to guarantee that the data characteristics underlying the time series are effectively extracted; (2) in the modeling of each subseries, i.e., when the original data is decomposed into some time series, Forecasting accuracy and stability are not simultaneously considered, and thus, they are not properly modeled; and (3) the best function to perform a deterministic Forecasting and uncertainty analysis based on the forecaster of each subseries is unknown. The developed hybrid system consists of three steps: First, data preprocessing is conducted to capture and mine the main feature of the wind Energy time series and weaken the noises’ negative effects; second, multi-objective optimization is proposed to achieve the Forecasting of each subseries with improvements in accuracy and stability; finally, search for the best function, which obtains the deterministic Forecasting and uncertainty analysis results using an optimized extreme learning machine based on different modeling objectives, is conducted. Experimental simulations are performed using data from three sites in a real wind farm, which indicate that the developed system has a better performance in engineering applications than that of other methods. Furthermore, this system could not only be used as an effective tool for wind Energy deterministic Forecasting and uncertainty analysis, but also for other engineering application areas in the future.
Jianzhou Wang - One of the best experts on this subject based on the ideXlab platform.
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hybrid wind Energy Forecasting and analysis system based on divide and conquer scheme a case study in china
Journal of Cleaner Production, 2019Co-Authors: Jianzhou Wang, Wendong Yang, Tong NiuAbstract:Abstract Wind Energy, acknowledged as a promising form of renewable Energy and the fastest-growing clean method for electricity generation, has attracted considerable attention from many scientists and researchers in recent decades. However, wind Energy Forecasting is still a challenging task owing to its inherent features of non-linearity and randomness. Therefore, this study develops a hybrid wind Energy Forecasting and analysis system including a deterministic Forecasting module and an uncertainty analysis module to mitigate the challenges in existing studies. In particular, these challenges are as follows: (1) It is difficult to guarantee that the data characteristics underlying the time series are effectively extracted; (2) in the modeling of each subseries, i.e., when the original data is decomposed into some time series, Forecasting accuracy and stability are not simultaneously considered, and thus, they are not properly modeled; and (3) the best function to perform a deterministic Forecasting and uncertainty analysis based on the forecaster of each subseries is unknown. The developed hybrid system consists of three steps: First, data preprocessing is conducted to capture and mine the main feature of the wind Energy time series and weaken the noises’ negative effects; second, multi-objective optimization is proposed to achieve the Forecasting of each subseries with improvements in accuracy and stability; finally, search for the best function, which obtains the deterministic Forecasting and uncertainty analysis results using an optimized extreme learning machine based on different modeling objectives, is conducted. Experimental simulations are performed using data from three sites in a real wind farm, which indicate that the developed system has a better performance in engineering applications than that of other methods. Furthermore, this system could not only be used as an effective tool for wind Energy deterministic Forecasting and uncertainty analysis, but also for other engineering application areas in the future.
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combined Forecasting models for wind Energy Forecasting a case study in china
Renewable & Sustainable Energy Reviews, 2015Co-Authors: Ling Xiao, Jianzhou Wang, Yao DongAbstract:Abstract As the Energy crisis becomes a greater concern, wind Energy, as one of the most promising renewable Energy resources, becomes more widely used. Thus, wind Energy Forecasting plays an important role in wind Energy utilization, especially wind speed Forecasting, which is a vital component of wind Energy management. In view of its importance, numerous wind speed forecasts have been proposed, each with advantages and disadvantages. Searching for more effective wind speed forecasts in wind Energy management is a challenging task. As proposed, combined models have desirable Forecasting abilities for wind speed. This paper reviewed the combined models for wind speed predictions and classified the combined wind speed Forecasting approaches. To further study the combined models, two combination models, the no negative constraint theory (NNCT) combination model and the artificial intelligence algorithm combination model, are proposed. The hourly average wind speed data of three wind turbines in the Chengde region of China are used to illustrate the effectiveness of the proposed combination models, and the results show that the proposed combination models can always provide desirable Forecasting results compared to the existing traditional combination models.
Eva Gonzalez-romera - One of the best experts on this subject based on the ideXlab platform.
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Demand and Storage Management in a Prosumer Nanogrid Based on Energy Forecasting
Electronics, 2020Co-Authors: Eva Gonzalez-romera, Enrique Romero-cadaval, Joaquin Garrido-zafra, Olivia Florencias-oliveros, Mercedes Ruiz-cortes, Antonio Moreno-munoz, Juan-josé González-de-la-rosaAbstract:Energy efficiency and consumers’ role in the Energy system are among the strategic research topics in power systems these days. Smart grids (SG) and, specifically, microgrids, are key tools for these purposes. This paper presents a three-stage strategy for Energy management in a prosumer nanogrid. Firstly, Energy monitoring is performed and time-space compression is applied as a tool for Forecasting Energy resources and power quality (PQ) indices; secondly, demand is managed, taking advantage of smart appliances (SA) to reduce the electricity bill; finally, Energy storage systems (ESS) are also managed to better match the forecasted generation of each prosumer. Results show how these strategies can be coordinated to contribute to Energy management in the prosumer nanogrid. A simulation test is included, which proves how effectively the prosumers’ power converters track the power setpoints obtained from the proposed strategy.