An Open-access Database and Machine Learning Toolbox for Solar Energy Forecasting across the Globe
An Open-access Database and Machine Learning Toolbox for Solar Energy Forecasting across the Globe
Yuhao Nie1, Stephen Campbell2, Quentin Paletta3, Liwenbo Zhang4, Tao Jing5, Samer Chaaraoui6, Jonathan Giezendanner7, Andea Scott8, Max Aragon9, Jack Camier10, Adam Jensen11, Florian Kotthoff12, Mengying Li13, Stefanie Meilinger14, Yupeng Wu4, Adam R Brandt15 and Sherrie Wang16, (1)Massachusetts Institute of Technology, Institute for Data, Systems, and Society, Cambridge, MA, United States, (2)Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, Cambridge, United States, (3)European Space Agency, ECSAT, Oxfordshire, United Kingdom, (4)University of Nottingham, Department of Architecture and Built Environment, Nottingham, United Kingdom, (5)Hong Kong Polytechnic University, Department of Mechanical Engineering, Hong Kong, Hong Kong, (6)Hochschule Bonn-Rhein-Sieg University of Applied Sciences, International Center for Sustainable Development, Sankt Augustin, Germany, (7)Massachusetts Institute of Technology, Institute for Data, Systems, and Society, Cambridge, United States, (8)Stanford University, Department of Energy Science and Engineering, Stanford, United States, (9)Mines ParisTech, Paris, France, (10)Independent researcher, Dallas, United States, (11)Technical University of Denmark, Department of Civil and Mechanical Engineering, Kgs Lyngby, Denmark, (12)OFFIS Institute for Information Technology, Oldenburg, Germany, (13)The Hong Kong Polytechnic University, Hong Kong, Hong Kong, (14)Hochschule Bonn-Rhein-Sieg University of Applied Sciences, Sankt Augustin, Germany, (15)Stanford University, Department of Energy Science and Engineering, Stanford, CA, United States, (16)Massachusetts Institute of Technology, Mechanical Engineering, Cambridge, United States