Statistical Review of World Energy 2018 (BP plc., 2018).
World Energy Outlook 2018 (International Energy Agency, 2018).
Renewable Capacity Statistics 2019 (International Renewable Energy Agency, 2019).
Byers, L. et al. A Global Database of Power Plants (World Resources Institute, 2018).
Barbose, G. & Darghouth, N. Tracking the Sun (Berkeley Lab, 2019); https://openpv.nrel.gov/tracking-the-sun
Yu, J., Wang, Z., Majumdar, A. & Rajagopal, R. DeepSolar: a machine learning framework to efficiently construct a solar deployment database in the United States. Joule 2, 2605–2617 (2018).
Hou, X. et al. Solarnet: a deep learning framework to map solar plants in China from satellite imagery. In ICLR 2020 Workshop on Tackling Climate Change with Machine Learning (ICLR, 2020); https://www.climatechange.ai/papers/iclr2020/6.html
Platts, S. G. World Electric Power Plant Database (2018); https://www.spglobal.com/platts/en/products-services/electric-power/world-electric-power-plants-database
Electric Plants (IHSMarkit, 2020); https://catalogue.datalake.ihsmarkit.com/
Renewables 2019 (International Energy Agency, 2019); https://www.iea.org/reports/renewables-2019
Bolinger, M., Seel, J. & Robson, D. Utility-Scale Solar: Empirical Trends in Project Technology, Cost, Performance, and PPA Pricing in the United States (Berkeley Lab, 2019).
Fukushima, K. Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift …….