In this section
@inproceedings{Kariniotakis:2021:10.1049/icp.2021.2499,
author = {Kariniotakis, G and Camal, S and Sossan, F and Nouri, B and Lezaca, J and Lange, M and Alonzo, B and Libois, Q and Pinson, P and Bessa, R and Goncalves, C},
doi = {10.1049/icp.2021.2499},
pages = {181--184},
title = {Data Science for Next Generation Renewable Energy Forecasting - Highlight Results from the Smart4RES Project},
url = {http://dx.doi.org/10.1049/icp.2021.2499},
year = {2021}
}
TY - CPAPER
AB - Smart4RES is a European Horizon2020 project developing next generation solutions for renewable energy forecasting. This paper presents highlight results obtained during the first year of the project. Data science is used throughout the proposed solutions in order to process the large amount of heterogeneous data available to forecasters, and derive model-free approaches of forecasting and decision-aid tasks. This paper presents a series of solutions addressing relevant for Photovoltaics (PV) and storage applications. High-resolution Numerical Weather Predictions and regional solar irradiance forecasting provide detailed information on local weather conditions and their variability. PV power forecasting benefits from such new data sources, but also the proposed collaborative data exchange. Finally, data-driven methods simplify decision-making for trading in short-term markets and for grid management.
AU - Kariniotakis,G
AU - Camal,S
AU - Sossan,F
AU - Nouri,B
AU - Lezaca,J
AU - Lange,M
AU - Alonzo,B
AU - Libois,Q
AU - Pinson,P
AU - Bessa,R
AU - Goncalves,C
DO - 10.1049/icp.2021.2499
EP - 184
PY - 2021///
SP - 181
TI - Data Science for Next Generation Renewable Energy Forecasting - Highlight Results from the Smart4RES Project
UR - http://dx.doi.org/10.1049/icp.2021.2499
ER -
Dyson School of Design Engineering
Imperial College London
25 Exhibition Road
South Kensington
London
SW7 2DB
design.engineering@imperial.ac.uk
Tel: +44 (0) 20 7594 8888