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Machine Learning Approach to Day-Ahead Scheduling for Multiperiod Energy Markets Under Renewable Energy Generation Uncertainty.

, , , , , and . CDC, page 4020-4025. IEEE, (2018)

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Day-ahead Strategic Marketing of Energy Prosumption: A Machine Learning Approach Based on Neural Networks., , , , , and . ECC, page 3910-3915. IEEE, (2019)Geolocation Correction for Geostationary Satellite Observations by a Phase-Only Correlation Method Using a Visible Channel., , , and . Remote. Sens., 12 (15): 2472 (2020)Hourly GPP Estimation in Australia Using Himawari-8 AHI Products., , , , , and . IGARSS, page 4513-4515. IEEE, (2020)Geolocation Accuracy Assessment of Himawari-8/AHI Imagery for Application to Terrestrial Monitoring., , , and . Remote. Sens., 12 (9): 1372 (2020)An Introduction to the Geostationary-NASA Earth Exchange (GeoNEX) Products: 1. Top-of-Atmosphere Reflectance and Brightness Temperature., , , , , , and . Remote. Sens., 12 (8): 1267 (2020)Machine Learning Approach to Day-Ahead Scheduling for Multiperiod Energy Markets Under Renewable Energy Generation Uncertainty., , , , , and . CDC, page 4020-4025. IEEE, (2018)GeoNEX: A Geostationary Earth Observatory at NASA Earth Exchange: Earth Monitoring from Operational Geostationary Satellite Systems., , , , , , , , , and 4 other author(s). IGARSS, page 128-131. IEEE, (2020)