Conference Agenda

Monitoring Hydropower Plants with LSTM-Based Time-series Forecasting

FATEMEH HAJIMOHAMMADALI, Mauro Tucci, Nunzia Fontana, Emanuele Crisostomi

University of Pisa, Italy

Forecasting Day-Ahead PV Generation and Load Demand for an Individual Residential Consumer

Elias Mandefro Getie, Geert Deconinck

KU Leuven,Belgium

A comparison of machine learning algorithms for the optimization of a day-ahead photovoltaic power forecast

Katrin Schulte, Lars Engel, Lars Quakernack, Fynn Liegmann, Jens Haubrock

Bielefeld University of Applied Sciences and Arts, Germany

Forecasting vertical grid load using machine learning algorithms

Marie-Louise Kloubert

Amprion GmbH, Germany

The Impacts of the Effective Load Data Preprocessing in Long-Term Load Forecasting

Airam Perez Guillen, Jovica V. Milanović

The University of Manchester, United Kingdom

Multi-step ahead wind power forecasting based on N-BEATS model

Luis Fernando Rodrigues Agottani¹, Matheus Melara Girardi², Wendel Rafael de Souza Chaves¹, Lucas de Azevedo Takara³, Leandro dos Santos Coelho³, Viviana Cocco Mariani¹

¹Graduate Program in Mechanical Engineering, Federal University of Parana, Brazil; ²Department of Computer Science, Pontifical Catholic University of Parana, Brazil; ³Graduate Program in Electrical Engineering, Federal University of Parana, Brazil

Ultra short term power prediction of offshore wind power based on ICEEMD-KPCA-LSTM

Lingling Huang, Zhangjie Fu, Yang Liu, Shurong Wei, Feixiang Ying

Shanghai university of electric power, China, People's Republic of