Next Generation Modelling and Forecasting of Variable Renewable Generation for Large-scale Integration in Energy Systems and Markets

Livrables

Cost-benefit methodology for RES forecasting value, investment in storage technology and grid management

This Deliverable presents the results of Task 64 where the aim is to define standardized costbenefit analysis CBA methodologies for assessing the RES forecasting value in combination with storage technologies and for grid constraints management In addition a costbenefit analysis tool will be developed to assess in an incremental way the benefits brought in the forecasting accuracy and in the value from the final use of forecasts from investing in integrating additional information in the forecasting process This refers to options like the addition of measuring equipment for weather variables ie sky cameras LiDAR alternative NWPs satellite images data from neighbour sites etc

Combined software and hardware in the loop tests for distribution grids and isolated power systems with high RES penetration

This Deliverable presents the results of Task 62 where the DNV GLs industrial laboratory test bed is setup for testing the predictive tools developed in Tasks 52 for economic dispatch and in Task 53 for grid management A softwareintheloop methodologytool will be developed to facilitate in this industrially relevant environment the development and validation of the tools under power system operating scenarios with high RES penetration considering RES storage and grid operations Further validation of the forecasting chain will be performed in Greek islands by HEDNO for testing the forecasting chain necessary control algorithms and ancillary services developed in Tasks 51 and 52

Report on final recommandations

This Deliverable presents the results of Task 74 where final recommendations of the project are produced that will be disseminated at the project final workshop

Toolbox of multi-source data approaches to short term RES forecasting

This Deliverable presents the results of Task 31 which is a toolbox of different complementary data sciencebased models that combine data collected from different sources and in different temporal domains aiming at improving RES power forecasting skill

Strategies for RES-oriented NWP models' enhancement

This Deliverable presents the results of Task 21 where the research objective is to improve the forecasting of RESoriented weather variables with NWP satellite and ground based allsky images It includes the results 1 of a first subtask on the refinement of the cloud radiative impact in atmospheric models which is expected to result in an optimised version of AROME mode for solar applications 2 of a second subtask where advanced tools are proposed to generate seamless ensemble forecasts appropriate for alternative energy applications

New trading strategies for RES and storage: humans and data together

This Deliverable presents the results of Task 54 where the aim is on the one hand to to design humanintheloop approaches in order to create knowledge intensive algorithms capable of providing fast advices and assistance to human operators and boost the integration of forecasting technologies

Lessons learnt and roadmap for new RES forecasting use cases - Gathering the main takeaways from the project

This Deliverable presents the results of Task 65 where the aim is to generate lessons learnt to be fed into future deploymentscalability of commercial forecastingdecisionaid tools and whenever applicable contribute to redesignadapt tools and business models defined in previous WPs WP2WP5 Moreover from the collected feedback it will be possible to define a roadmap for RES forecasting tools enhancement of grid codes and defining possible future work and paving the way for further developments

Project leaflet, corporate design and templates

This Deliverable includes a leaflet with information of the project for large-public diffusion, templates for the various documents (deliverables etc) of the project and corporate design guidelines.

Report on generic seamless forecasting approach for multiple time scales

This Deliverable presents the results of Task 32 where the aim is to propose novel approaches towards a seamless view of RES forecasting at various temporal and spatial granularity levels also in view of input data and information that naturally comes with different resolution and frequency updates The resulting approach is expected to simplify the stateof the art model chain where specific models are developed as a function of the time frame of the available data

Use cases, requirements and KPI for RES forecasting

This Deliverable presents the results of Task 1.1 where the requirements for RES forecasts for different applications and end-users are analysed. Several well-established, as well as innovative use-cases, are assessed with respect to the way RES forecasts are considered. The expected performance by end-users and corresponding metrics are also surveyed whereas KPIs for the project are defined.

Evaluation report on power and post-processing results

This Deliverable presents the results of Task 32 where the aim is to perform a direct evaluation of the benefits brought in forecasting RES production by the NWP improvements obtained in WP2 as revealed through power conversion and further postprocessing in particular for the use cases defined in WP1

Innovative data assimilation strategies of solar and wind energy production

This Deliverable presents the results of Task 24 where it is studied the assimilation of various types of observations into the LESbased weather forecasting model The various types of observations to assimilate range from the traditional measurements such as from ground station observations and satellite images more innovative observations such as LiDAR measurements and cloud camera images and lastly observations from RES systems themselves

Report on improved NWP with higher spatial and temporal resolution

This Deliverable presents the results of Task 22where two approaches aiming to improve forecasts of weather variables through an increase of the spatial and temporal resolution of the models are developed The models considered will be the classical NWP models advanced ensembles and also the largeeddy simulation approach

Approaches to distributed and collaborative renewable energy forecasting

This Deliverable presents the results of Task 41 where alternative approaches to distributed and collaborative learning and forecasting are proposed Statistical and machine learning approaches are to be considered Instead of sharing their data learning problems will be solved in a distributed manner One of the key challenges is to limit the computational burden and communication needs for those distributed learning problems Additional privacypreserving aspects will be considered to make sure that information may not be recovered through eg inverse optimization problems

New business models for RES forecasting

This Deliverable presents the results of Task 43 where new business models are analysed for various types of data providers weatherremotesensing data power data etc those who improve their forecasts and the system as a whole The complementary approaches proposed in Tasks 41 and 42 may lead to different business models for those collecting and potentially contributing to forecast improvement for others In parallel the business model of those agents that aim to improve their forecasts through collaborative learning or data purchase through a data marketplace is to be analysed Finally the analysis of those business models should focus on the system as a whole as it is expected that collaborative forecasting and data sharing will yield an improved equilibrium in terms of overall social welfare

Data markets and applications in renewable energy forecasting

This Deliverable presents the results of Task 42 where algorithmic solutions for data markets are developed This will allow different agents to sell and buy data of relevance for RES forecasting with pricing being a function of their value for other agents Alternative approaches to the design of such data marketplace will be considered On the other hand a blockchainbased platform with smart contracts will be implemented and enable platforms agents to 1 upload their forecasts and buy a forecast that is a combination of all submitted forecasts 2 collaborate using the algorithm developed in Task 41 and improve accuracy by using geographically distributed RES time series

Communication and dissemination Final report

This Deliverable contains the listing of all the communication and dissemination activities carried out by the partners of the project during its duration

Existing and new forecasting products: taxonomy and standardization

This Deliverable presents the results of Task 1.2 where new forecasting products are identified for WP2 and WP3 for different applications. Given the variety of applications where RES forecasts are used, this Deliverable aims to present results towards standardisation of the forecasting products so that the developed models are as replicable as possible in different contexts.

Communication and Dissemination Master Plan

This Deliverable presents the detailed Communication and Dissemination Master Plan (CDMP) developed in Task 7.1. It outlines the project’s communication and dissemination activities, detailing the composition of the target groups and defining the communication tools and distribution channels to reach them. The CDMP will be subject to two updates following the Innovation Board meetings, in order to fine tune the dissemination objectives with the content of the project results and include potential new communication tools that may appear over time.

Predictive dispatch of isolated systems to guarantee minimum FCR and system inertia requirements

This Deliverable presents the results of Task 52 which aims to 1 develop inertial response probabilistic forecasting at the RES power plant level by exploring information from high temporal resolution NWP and power forecasts from WP3 2 predictive dispatch of system inertia and FCR needs to cope with multiple disturbances in isolated power system A dynamic security constrained dispatching algorithm capable of coping with multiple disturbances in isolated power systems eg sudden loss of a large power plant shortcircuit in transmission line will be developed to maximize RES integration In order to provide fast information to human operators in control centres machinelearning techniques exploiting functional knowledge about the isolated power systems transientdynamic behaviour will be used

Methodologies for short-term solar resource forecasting by merging various inputs

This Deliverable presents the results of Task 23 and includes 1 The results of an advanced analysis of camera images for cloud characterisation New retrieval algorithms from sky camera networks are developed in order to provide highly spatially and temporally resolved nowcasts of solar irradiance able to cover large areas ie areas covered by distribution grids 2 The results of new algorithms developed to combine data from different sources sky imaging networks satellite and NWP products targeting to improve irradiance forecasts across different timescales

Predictive management of voltage and congestion problems under RES uncertainty

This Deliverable presents the results of Task 53 which aims at developing a logical decisionaid method to perform grid segmentation based on different criteria like the level of RES uncertainty exploit using imitation learning knowledge from past experiences and empirical rules for grid operation design exploration strategies eg sensitivity tree that search for better solutions in comparison to historic decisions data In addition an approach for gridaware forecasting of the flexibility at the TSODSO interface is proposed where the aggregated flexibility of distribution systems modelled as activereactive power capability curves at the grid connection is forecasted

Joint dispatch of RES and storage technologies towards a multi-service approach

This Deliverable presents the results of Task 51 where the aim is to develop a multiobjective optimization approach suitable for large RES power plant operators with integrated storage capacity The method considers RES forecast uncertainty and various options to valorise the produced energy like delivery contracts in place provision of flexibility etc The application of machine learning techniques are explored The Deliverable includes also an optimization approach that modifies the settings of specific services of battery storage and RES inverters by taking also under consideration power ensembles forecasts from WP3 In addition multiple services ie FCR FRR RR synthetic inertia etc will be specified and designed for a battery inverter which will cover the requirements of isolated systems

Website of the project

This Deliverable is the operational online public website of the project (www.smart4res.eu). The released version will contain information on the project are news from the first period of the project.

Publications

Seamless intra-day and day-ahead multivariate probabilistic forecasts at high temporal resolution

Auteurs: Van der Meer, Dennis; Camal, Simon; Kariniotakis, George
Publié dans: https://hal-mines-paristech.archives-ouvertes.fr/hal-03638925, Numéro 1, 2022
Éditeur: IEEE PES
DOI: 10.5281/zenodo.6451912

Generalizing Renewable Energy Forecasting Using Automatic Feature Selection and Combination

Auteurs: D. van der Meer, S. Camal, G. Kariniotakis
Publié dans: 17th International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2022, 2022
Éditeur: IEEE
DOI: 10.1109/pmaps53380.2022.9810647

Monetizing Customer Load Data for an Energy Retailer: A Cooperative Game Approach

Auteurs: Liyang, Han; Jalal, Kazempour; Pierre, Pinson
Publié dans: 2021 IEEE Madrid PowerTech Conference, 2021
Éditeur: IEEE

End-To-End Learning For Hierarchical Forecasting of Renewable Energy Production With Missing Values

Auteurs: A. Stratigakos, D. van der Meer, S. Camal, G. Kariniotakis.
Publié dans: 17th International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2022, 2022
Éditeur: IEEE
DOI: 10.1109/pmaps53380.2022.9810610

Data Science for Next Generation Renewable Energy Forecasting - Highlight Results from the Smart4RES Project

Auteurs: Kariniotakis, George; Camal, Simon; Sossan, Fabrizio; Nouri, Bijan; Lezaca, Jorge; Lange, Matthias; Alonzo, Bastien; Libois, Quentin; Pinson, Pierre; Bessa, Ricardo; Goncalves, Carla
Publié dans: 11th International Workshop on Integration of Solar Power into Power Systems, EnergyNautics GmbH, Sep 2021, Berlin, Germany, 2022, Page(s) 181-184, ISBN 978-1-83953-680-9
Éditeur: IET
DOI: 10.1049/icp.2021.2499

Trading Data for Wind Power Forecasting: A Regression Market with Lasso Regularization

Auteurs: Liyang Han, Pierre Pinson, Jalal Kazempour
Publié dans: Electric Power Systems Research, 2022, ISSN 0378-7796
Éditeur: Elsevier

Smart4RES: Next generation solutions for renewable energy forecasting and applications with focus on distribution grids

Auteurs: Simon Camal, Georges Kariniotakis, Fabrizio Sossan, Quentin Libois, Raphael Legrand, et al.
Publié dans: CIRED 2021 - The 26th International Conference and Exhibition on Electricity Distribution, 2021, ISBN 978-1-83953-591-8
Éditeur: IET
DOI: 10.1049/icp.2021.1829

Comparison of Grid Forming and Grid Following Control of A Central Bes In A Island System Operating In High Res Penetration

Auteurs: Dimitris Lagos; Nikos Hatziargyriou
Publié dans: Numéro 1, 2023
Éditeur: IEEE
DOI: 10.5281/zenodo.6794502

Data-Enabled Reactive Power Control of Distributed Energy Resources via a Copula Estimation of Distribution Algorithm

Auteurs: D. van der Meer, H. Valizadeh Haghi, J. Kleissl, J. Widén.
Publié dans: 2022 17th International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), 2022, ISSN 2642-6730
Éditeur: IEEE
DOI: 10.1109/pmaps53380.2022.9810636

A Value-Oriented Price Forecasting Approach to Optimize Trading of Renewable Generation

Auteurs: Akylas, Stratigakos; Andrea, Michiorri; Georges, Kariniotakis
Publié dans: 2021 IEEE Madrid PowerTech Conference, 2021
Éditeur: IEEE

Adaptive Generalized Logit-Normal Distributions for Wind Power Short-Term Forecasting

Auteurs: Pierrot, Amandine; Pinson, Pierre
Publié dans: 2021 IEEE Madrid PowerTech Conference, Numéro 1, 2021
Éditeur: IEEE

Probabilistic Forecasting of Regional Wind Power Generation for the EEM20 Competition: a Physics-oriented Machine Learning Approach

Auteurs: Kevin Bellinguer, Valentin Mahler, Simon Camal, Georges Kariniotakis
Publié dans: 2020 17th International Conference on the European Energy Market (EEM), 2020, Page(s) 1-6, ISBN 978-1-7281-6919-4
Éditeur: IEEE
DOI: 10.1109/eem49802.2020.9221960

Online forecast reconciliation in wind power prediction

Auteurs: Chiara Di Modica, Pierre Pinson, Souhaib Ben Taieb
Publié dans: Electric Power Systems Research, Numéro 190, 2021, Page(s) 106637, ISSN 0378-7796
Éditeur: Elsevier BV
DOI: 10.1016/j.epsr.2020.106637

Vulnerability and Impact of Machine Learning-based Inertia Forecasting Under Cost-Oriented Data Integrity Attack

Auteurs: Y. Chen, M. Sun, Z. Chu, S. Camal, G. Kariniotakis, et al
Publié dans: IEEE Transactions on Smart Grid, 2022, ISSN 1949-3053
Éditeur: Institute of Electrical and Electronics Engineers
DOI: 10.1109/tsg.2022.3207517

Online decision-making for trading wind energy

Auteurs: M. A. Muñoz, P. Pinson, J. Kazempour
Publié dans: Computational Management Science, 2023, ISSN 1619-697X
Éditeur: Springer Verlag
DOI: 10.1007/s10287-023-00462-2

Improving Dynamic Security in Islanded Power Systems: Quantification of Minimum Synchronous Inertia Considering Fault-Induced Frequency Deviations

Auteurs: José Gouveia; Carlos L. Moreira; João A. Peças Lopes
Publié dans: Electricity; Volume 4; Numéro 2; Pages: 114-133, Numéro 1, 2023, ISSN 2673-4826
Éditeur: MDPI
DOI: 10.3390/electricity4020008

A market for trading forecasts: A wagering mechanism

Auteurs: Aitazaz Ali Raja; Pierre Pinson; Jalal Kazempour; Sergio Grammatico
Publié dans: International Journal of Forecasting, Numéro 2, 2023, ISSN 1872-8200
Éditeur: Elsevier
DOI: 10.1016/j.ijforecast.2023.01.007

Forecasting and Market Design Advances: Supporting an Increasing Share of Renewable Energy.

Auteurs: Jack Fox; Erik Ela; Ben Hobbs; Justin Sharp; Josh Novacheck; Amber Motley; Ricardo J. Bessa; Pierre Pinson; Georges Kariniotakis
Publié dans: IEEE Power and Energy Magazine, 2021, ISSN 1558-4216
Éditeur: IEEE
DOI: 10.1109/mpe.2021.3104132

A Hybrid Solar Irradiance Nowcasting Approach: Combining All Sky Imager Systems and Persistence Irradiance Models for Increased Accuracy

Auteurs: Bijan Nouri, Niklas Blum, Stefan Wilbert, Luis F. Zarzalejo
Publié dans: Solar RRL, 2021, Page(s) 2100442, ISSN 2367-198X
Éditeur: Wiley-VCH GmbH
DOI: 10.1002/solr.202100442

Measurement of diffuse and plane of array irradiance by a combination of a pyranometer and an all-sky imager

Auteurs: Blum, Niklas; Wilbert, Stefan; Nouri, Bijan; Lezaca, Jorge; Huckebrink, David; Kazantzidis, Andreas; Heinemann, Detlev; Zarzalejo, L. F.; Jiménez, María José; Pitz-Paal, Robert
Publié dans: Solar Energy, Numéro 232, 2022, ISSN 0038-092X
Éditeur: Pergamon Press Ltd.
DOI: 10.1016/j.solener.2021.11.064

Reliable Provision of Ancillary Services from Aggregated Variable Renewable Energy Sources through Forecasting of Extreme Quantiles

Auteurs: S. Camal, A. Michiorri and G. Kariniotakis
Publié dans: IEEE Transactions on Power Systems, 2023, ISSN 0885-8950
Éditeur: Institute of Electrical and Electronics Engineers
DOI: 10.1109/tpwrs.2022.3198839

Towards Resilient Energy Forecasting: A Robust Optimization Approach

Auteurs: Akylas Stratigakos , Panagiotis Andrianesis , Andrea Michiorri , Georges Kariniotakis
Publié dans: IEEE Transactions on Smart Grid, 2023, ISSN 1949-3053
Éditeur: Institute of Electrical and Electronics Engineers
DOI: 10.1109/tsg.2023.3272379

Distributionally Robust Trading Strategies for Renewable Energy Producers

Auteurs: Pierre Pinson
Publié dans: IEEE Transactions on Energy Markets, Policy and Regulation, 2023, ISSN 2771-9626
Éditeur: IEEE
DOI: 10.1109/tempr.2023.3241232

Uncertainty of shortwave cloud radiative impact due to the parameterization of liquid cloud optical properties

Auteurs: Jahangir, E., Libois, Q., Couvreux, F., Vié, B., & Saint-Martin, D
Publié dans: Journal of Advances in Modeling Earth Systems, Numéro 13, 2021, ISSN 1942-2466
Éditeur: American Geophysical Union
DOI: 10.1029/2021ms002742

A critical overview of privacy-preserving approaches for collaborative forecasting

Auteurs: Carla Gonçalves, Ricardo J. Bessa, Pierre Pinson
Publié dans: International Journal of Forecasting, Numéro 37/1, 2021, Page(s) 322-342, ISSN 0169-2070
Éditeur: Elsevier BV
DOI: 10.1016/j.ijforecast.2020.06.003

Cloud height measurement by a network of all-sky imagers

Auteurs: Niklas Benedikt Blum, Bijan Nouri, Stefan Wilbert, Thomas Schmidt, Ontje Lünsdorf, Jonas Stührenberg, Detlev Heinemann, Andreas Kazantzidis, Robert Pitz-Paal
Publié dans: Atmospheric Measurement Techniques, Numéro 14/7, 2021, Page(s) 5199-5224, ISSN 1867-8548
Éditeur: Atmospheric Measurement Techniques
DOI: 10.5194/amt-14-5199-2021

Infinite hidden Markov model for short-term solar irradiance forecasting

Auteurs: Â. Frimane, J. Munkhammar, D. van der Meer
Publié dans: Solar Energy 2022, 2022, ISSN 0038-092X
Éditeur: Pergamon Press Ltd.
DOI: 10.1016/j.solener.2022.08.041

Prescriptive Trees for Integrated Forecasting and Optimization Applied in Trading of Renewable Energy.

Auteurs: Stratigakos, Akylas; Camal, Simon; Michiorri, Andrea & Kariniotakis, George.
Publié dans: IEEE Transactions on Power Systems, 2022, ISSN 1558-0679
Éditeur: IEEE
DOI: 10.1109/tpwrs.2022.3152667

Privacy-Preserving Distributed Learning for Renewable Energy Forecasting

Auteurs: Carla Gonçalves; Ricardo J. Bessa; Pierre Pinson
Publié dans: IEEE Transactions on Sustainable Energy, Numéro 12, 2021, Page(s) 1777-1787, ISSN 1949-3037
Éditeur: IEEE
DOI: 10.1109/tste.2021.3065117

Forecasting conditional extreme quantiles for wind energy

Auteurs: Carla Gonçalves, Laura Cavalcante, Margarida Brito, Ricardo J. Bessa, João Gama
Publié dans: Electric Power Systems Research, Numéro 190, 2021, ISSN 0378-7796
Éditeur: Elsevier BV
DOI: 10.1016/j.epsr.2020.106636

Analyzing Spatial Variations of Cloud Attenuation by a Network of All-Sky Imagers

Auteurs: Blum, Niklas; Wilbert, Stefan; Nouri, Bijan; Stührenberg, Jonas; Lezaca, Jorge; Schmidt, Thomas; Heinemann, Detlev; Vogt, Thomas; Kazantzidis, Andreas; Pitz-Paal, Robert
Publié dans: Remote Sensing; Volume 14; Numéro 22; Pages: 5685, Numéro 1, 2022, ISSN 2072-4292
Éditeur: Multidisciplinary Digital Publishing Institute (MDPI)
DOI: 10.3390/rs14225685

Towards Data Markets in Renewable Energy Forecasting

Auteurs: Carla Goncalves, Pierre Pinson, Ricardo J. Bessa
Publié dans: IEEE Transactions on Sustainable Energy, Numéro 12/1, 2021, Page(s) 533-542, ISSN 1949-3029
Éditeur: Institute of Electrical and Electronics Engineers
DOI: 10.1109/tste.2020.3009615

A benchmark for multivariate probabilistic solar irradiance forecasts

Auteurs: Dennis van der Meer; Dennis van der Meer
Publié dans: Solar Energy, Numéro 225, 2021, ISSN 0038-092X
Éditeur: Pergamon Press Ltd.
DOI: 10.1016/j.solener.2021.07.010

Day-Ahead Parametric Probabilistic Forecasting of Wind and Solar Power Generation using Bounded Probability Distributions and Hybrid Neural Networks

Auteurs: Theodoros Konstantinou, Nikos Hatziargyriou
Publié dans: IEEE Transactions on Sustainable Energy, 2023, ISSN 1949-3029
Éditeur: Institute of Electrical and Electronics Engineers
DOI: 10.1109/tste.2023.3270968

Regression markets and application to energy forecasting

Auteurs: Pinson, P., Han, L. & Kazempour, J.
Publié dans: TOP, 2022, ISSN 1863-8279
Éditeur: Springer
DOI: 10.1007/s11750-022-00631-7

Photovoltaic Power Forecasting: Assessment of the Impact of Multiple Sources of Spatio-Temporal Data on Forecast Accuracy

Auteurs: Agoua, X.G.; Girard, R.; Kariniotakis, G.
Publié dans: Energies, 2021, ISSN 1996-1073
Éditeur: Multidisciplinary Digital Publishing Institute (MDPI)
DOI: 10.3390/en14051432

Day-ahead probabilistic forecasting at a co-located wind and solar power park in Sweden: Trading and forecast verification

Auteurs: Lindberg O, Lingfors D, Arnqvist J, van der Meer D, Munkhammar J
Publié dans: Advances in Applied Energy, 2023, ISSN 2666-7924
Éditeur: Elsevier
DOI: 10.1016/j.adapen.2022.100120

Applying self-supervised learning for semantic cloud segmentation of all-sky images

Auteurs: Yann Fabel, Bijan Nouri, Stefan Wilbert, Niklas Blum, Rudolph Triebel, Marcel Hasenbalg, Pascal Kuhn, Luis F. Zarzalejo, and Robert Pitz-Paal
Publié dans: Atmos. Meas. Tech., Numéro 15, 2022, Page(s) 797-809, ISSN 1867-8548
Éditeur: Copernicus Publications
DOI: 10.5194/amt-15-797-2022

Generalizing Renewable Energy Forecasting Using Automatic Feature Selection and Combination

Auteurs: Van der Meer, Dennis; Camal, Simon; Kariniotakis, George
Publié dans: 17th International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2022, 2022
Éditeur: PMAPS
DOI: 10.5281/zenodo.6451891

Smart4RES: Towards next generation forecasting tools of renewable energy production

Auteurs: Georges Kariniotakis; Simon Camal; Ricardo J. Bessa; Pierre Pinson; Gregor Giebel; Quentin Libois; Raphaël Legrand; Matthias Lange; Stefan Wilbert; Bijan Nouri; Alexandre Neto; Remco Verzijlbergh; Ganesh Sauba; George Sideratos; Efrosyni Korka; Stéphanie Petit
Publié dans: EGU General Assembly 2020, 2020
Éditeur: EGU
DOI: 10.5194/egusphere-egu2020-20205

Seamless intra-day and day-ahead multivariate probabilistic forecasts at high temporal resolution

Auteurs: Van der Meer, Dennis; Camal, Simon; Kariniotakis, George
Publié dans: 17th International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2022, 2022
Éditeur: PMAPS International Society
DOI: 10.5281/zenodo.6451913

Smart4RES: Improved weather modelling and forecasting dedicated to renewable energy applications

Auteurs: Kariniotakis, George; Camal, Simon; Bessa, Ricardo; Pinson, Pierre; Giebel, Gregor; Libois, Quentin; Legrand, Raphael; Cassas, Marie; Raynaud, Laure; Lange, Matthias; Wilbert, Stefan; Nouri, Bijan; Neto, Alexandre; Verzijlbergh, Remco; Sauba, Ganesh; Sideratos, George; Korka, Efrosyni; Petit, Stephanie
Publié dans: EGU General Assembly 2021, 2021
Éditeur: EGU
DOI: 10.5194/egusphere-egu21-16219

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