The energy market relies on forecasting capabilities of both demand and power generation that need to be kept in dynamic balance. Today, when it comes to renewable energy generation, such decisions are increasingly made in a liberalized electricity market environment, where future power generation must be offered through contracts and auction mechanisms, hence based on forecasts. The increased share of highly intermittent power generation from renewable energy sources increases the uncertainty about the expected future power generation. Point forecast does not account for such uncertainties. To account for these uncertainties, it is possible to make probabilistic forecasts. This work first presents important concepts and approaches concerning probabilistic forecasts with deep learning. Then, deep learning models are used to make probabilistic forecasts of day-ahead power generation from a wind power plant located in Northern Norway. The performance in terms of obtained quality of the prediction intervals is compared for different deep learning models and sets of covariates. The findings show that the accuracy of the predictions improves when historical data on measured weather and numerical weather predictions (NWPs) were included as exogenous variables. This allows the model to auto-correct systematic biases in the NWPs using the historical measurement data. Using only NWPs, or only measured weather as exogenous variables, worse prediction performances were obtained.
Forecasting precipitation in the Arctic using probabilistic machine learning informed by causal climate drivers
Department of Science and Engineering, Sorbonne University, Abu Dhabi, United Arab Emirates（Sorbonne大学科学与工程系）；Physics and Applied Mathematics Unit, Indian Statistical Institute, Kolkata 700108, India（印度统计研究院应用数学与物理单元）；Sorbonne Center for Artificial Intelligence, Sorbonne University, Paris, 75005, France（Sorbonne大学人工智能中心）；Indian Institute of Technology Gandhinagar, Gandhinagar 382355, India（印度理工学院冈丁加尔分校）；National Centre for Polar and Ocean Research, Ministry of Earth Sciences, Vasco da Gama, 403804, India（国家极地与海洋研究中心）；Center for Computational Natural Science and Bioinformatics, International Institute of Information Technology, Gachibowli, Hyderabad-500032, India（国际信息技术研究所计算自然科学与生物信息中心）；Department of Dynamics, Lodz University of Technology, Stefanowskiego 1/15, 90–537 Lodz, Poland（Lodz技术大学动力学系）；Centre for Mathematical Biology and Ecology, Department of Mathematics, Jadavpur University, Kolkata 700032, India（贾瓦德普大学数学生物学与生态中心）
Applied AI Center（应用人工智能中心）；Skolkovo Institute of Science and Technology（斯科尔科沃科学技术学院）；Shirshov Institute of Oceanology（希尔绍夫海洋研究所）；Moscow Institute of Physics and Technology（莫斯科物理技术学院）；Gazprom Neft（俄罗斯天然气工业石油公司）
Varda-single-1.0: deterministic data-driven weather forecasting at 1 km resolution over Switzerland's complex topography
Federal Office of Meteorology and Climatology MeteoSwiss（瑞士联邦气象与气候办公室（MeteoSwiss））；Swiss Data Science Center (SDSC), ETH Zürich（苏黎世联邦理工学院瑞士数据科学中心（SDSC））；Center for Climate Systems Modeling (C2SM), ETH Zürich（苏黎世联邦理工学院气候系统建模中心（C2SM））；European Centre for Medium-Range Weather Forecasts (ECMWF)（欧洲中期天气预报中心（ECMWF））；Norwegian Meteorological Institute (MET Norway)（挪威气象研究所（MET Norway））；Royal Netherlands Meteorological Institute (KNMI)（荷兰皇家气象研究所（KNMI））；Institute for Atmospheric and Climate Science (IAC), ETH Zürich（苏黎世联邦理工学院大气与气候科学研究所（IAC））
Learning to forecast vegetation greenness at fine resolution over Africa with ConvLSTMs
Max-Planck-Institute for Biogeochemistry（马克斯·普朗克生物地球化学研究所）；ELLIS Unit Jena（耶拿欧洲学习与智能系统实验室）；Michael-Stifel-Center Jena for Data-driven and Simulation Science（耶拿米夏埃尔·施蒂费尔数据驱动与模拟科学中心）
PeakWeather: MeteoSwiss Weather Station Measurements for Spatiotemporal Deep Learning
Università della Svizzera italiana（瑞士大学）；IDSIA（智能系统研究所）；Federal Office of Meteorology and Climatology MeteoSwiss（联邦气象与气候办公室MeteoSwiss）；ETH Zürich（苏黎世联邦理工学院）；EPFL（洛桑联邦理工学院）；Swiss Data Science Center（瑞士数据科学中心）；Politecnico di Milano（米兰理工学院）
Data-Driven Uncertainty-Aware Forecasting of Sea Ice Conditions in the Gulf of Ob Based on Satellite Radar Imagery
Applied AI Center, Skolkovo Institute of Science and Technology, Moscow, Russia（斯克尔科沃科学与技术研究所应用人工智能中心）；Moscow Institute of Physics and Technology, Dolgoprudny, Russia（莫斯科物理技术学院）；Autonomous Non-Profit Organization Artificial Intelligence Research Institute (AIRI), Moscow, Russia（自主非营利组织人工智能研究 institute（AIRI））
TopoFlow: Topography-aware Pollutant Flow Learning for High-Resolution Air Quality Prediction
Department of Computational Engineering, Lappeenranta–Lahti University of Technology LUT（拉彭兰塔-拉赫蒂理工大学计算工程系）；Atmospheric Modelling Centre Lahti, Lahti University Campus（拉赫蒂大学园区大气模拟中心）；Institute for Atmospheric and Earth System Research, University of Helsinki（赫尔辛基大学大气与地球系统研究所）；Advanced Micro Devices (AMD)（超威半导体公司）；Technical University of Munich（慕尼黑工业大学）
EarthNet2021: A large-scale dataset and challenge for Earth surface forecasting as a guided video prediction task
Max-Planck-Institute for Biogeochemistry（马克斯·普朗克生物地球化学研究所）；German Aerospace Center (DLR)（德国航空航天中心）；University of Jena（耶拿大学）；Michael-Stifel-Center Jena for Data-driven and Simulation Science（耶拿米夏埃尔·施蒂费尔数据驱动与仿真科学中心）；Technische Universität Berlin（柏林工业大学）
Deep Learning for Wildfire Risk Prediction: Integrating Remote Sensing and Environmental Data
Schulich School of Engineering, Department of Geomatics Engineering, University of Calgary（Schulich 工程学院，测绘工程系，卡尔加里大学）；Department of Geography and Environmental Management, University of Waterloo（地理与环境管理系，滑铁卢大学）；Department of Systems Design Engineering, University of Waterloo（系统设计工程系，滑铁卢大学）；School of Emergency Management, Nanjing University of Information Science and Technology（应急管理学院，南京信息工程大学）；Division of Geoinformatics, KTH Royal Institute of Technology（地理信息学系，皇家理工学院）；School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology（遥感与测绘工程学院，南京信息工程大学）；Department of Software Engineering, University of Waterloo（软件工程系，滑铁卢大学）；Department of Management Science and Engineering, University of Waterloo（管理科学与工程系，滑铁卢大学）
