As the use of solar power increases, having accurate and timely forecasts will be essential for smooth grid operators. There are many proposed methods for forecasting solar irradiance / solar power production. However, many of these methods formulate the problem as a time-series, relying on near real-time access to observations at the location of interest to generate forecasts. This requires both access to a real-time stream of data and enough historical observations for these methods to be deployed. In this paper, we propose the use of Global methods to train our models in a generalised way, enabling them to generate forecasts for unseen locations. We apply this approach to both classical ML and state of the art methods. Using data from 20 locations distributed throughout the UK and widely available weather data, we show that it is possible to build systems that do not require access to this data. We utilise and compare both satellite and ground observations (e.g. temperature, pressure) of weather data. Leveraging weather observations and measurements from other locations we show it is possible to create models capable of accurately forecasting solar irradiance at new locations. This could facilitate use planning and optimisation for both newly deployed solar farms and domestic installations from the moment they come online. Additionally, we show that training a single global model for multiple locations can produce a more robust model with more consistent and accurate results across locations.
Improving day-ahead Solar Irradiance Time Series Forecasting by Leveraging Spatio-Temporal Context
Mila - Québec AI Institute（米拉-魁北克人工智能研究所）；Université de Montréal（蒙特利尔大学）；Université du Québec à Rimouski（魁北克大学里穆斯基分校）；NASA Goddard Space Flight Center（美国国家航空航天局戈达德太空飞行中心）
Short-Term Solar Irradiance Forecasting Under Data Transmission Constraints
The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Institute for Computational Engineering and Sciences, The University of Texas at Austin（德克萨斯大学奥斯汀分校计算工程与科学研究所）；Energy Institute, The University of Texas at Austin（德克萨斯大学奥斯汀分校能源研究所）
An Explainable, Attention-Enhanced, Bidirectional Long Short-Term Memory Neural Network for Joint 48-Hour Forecasting of Temperature, Irradiance, and Relative Humidity
SolarSeer: Ultrafast and accurate 24-hour solar irradiance forecasts outperforming numerical weather prediction across the USA
Microsoft（微软公司）；Tsinghua University（清华大学）；Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院）；University of California, Berkeley（加州大学伯克利分校）；Taiyuan University of Technology（太原科技大学）
Single-stage joint diffusion of sky images and clear-sky index for ultra-short-term irradiance forecasting: what the frame-to-irradiance stage discards
Intraday spatiotemporal PV power prediction at national scale using satellite-based solar forecast models
School of Engineering and Computer Science, Bern University of Applied Sciences（工程与计算机科学学院，伯恩应用科学大学）；Department of Geosciences and Remote Sensing, TU Delft（地球科学与遥感系，代尔夫特理工大学）
Forecasting the Ionosphere from Sparse GNSS Data with Temporal-Fusion Transformers
Advanced Concepts Team European Space Agency（欧洲航天局高级概念团队）；Department of Physics Università degli Studi di Roma Sapienza（罗马大学物理系）；Department of Engineering Science University of Oxford（牛津大学工程科学系）；Department of Information and Computer Science University of Hawai’i at Mānoa（夏威夷大学信息与计算机科学系）；Free Flight Research Lab（自由飞行研究实验室）；NASA Headquarters（美国国家航空航天局总部）；NASA Jet Propulsion Laboratory（美国国家航空航天局喷气推进实验室）；University of New Hampshire（新罕布什尔大学）；Department of Computer Science University of Oxford, UK（牛津大学计算机科学系）；Department of Computer Science University of York & King’s College London（约克大学计算机科学系及伦敦国王学院计算机科学系）
Characterization and forecasting of national-scale solar power ramp events
School of Engineering and Computer Science, Bern University of Applied Sciences（伯尔尼应用科学大学工程与计算机科学学院）；Department of Geosciences and Remote Sensing, TU Delft（代尔夫特理工大学地球科学与遥感系）
Quantum Fourier Transform Based Kernel for Solar Irrandiance Forecasting
Ecole Polytechnique Fédérale de Lausanne (EPFL)（瑞士联邦理工学院）；Mechanical Engineering Department, Universidad de Chile（智利大学机械工程系）；Departamento de Ingeniería Mecánica y Metalúrgica, Escuela de Ingeniería, Pontificia Universidad Católica de Chile（智利天主教大学机械工程与冶金系）；Garrick Institute for the Risk Sciences, University of California（加州大学风险科学研究所）
Climate-Invariant Conformal Prediction Intervals for Multi-Horizon Solar and Wind Forecasting
Department of Computer Science and Engineering, Dr. B.R. Ambedkar National Institute of Technology Jalandhar, India（计算机科学与工程系，Dr. B.R. 阿姆贝卡尔国立技术学院贾兰德哈尔，印度）
SolarTformer: A Transformer Based Deep Learning Approach for Short Term Solar Power Forecasting
Positional Astronomy Centre, India Meteorological Department, Kolkata, India（印度气象局位置天文学中心）；Department of Computer Science and Engineering, Jadavpur University, Kolkata, India（贾瓦德普大学计算机科学与工程系）；Department of Computer Science and Engineering (Artificial Intelligence and Machine Learning), Heritage Institute of Technology, Kolkata, India（遗产理工学院计算机科学与工程系（人工智能和机器学习））；Department of Electronics and Communication Engineering, Heritage Institute of Technology, Kolkata, India（遗产理工学院电子与通信工程系）；Department of Mechanical Engineering, Heritage Institute of Technology, Kolkata, India（遗产理工学院机械工程系）
