Deep Learning-Based Risk Prediction in Power Sector Financial Management Using Transformer and Actor-Critic Reinforcement Learning

Autor: Yuhuizi Xu, Dongzhu Zhu

Publikováno v: IEEE Access, Vol 12, Pp 137729-137745 (2024)

Precise short-term electricity demand forecasting is crucial for the grid’s operation and reliability management, while long-term forecasting is for future energy policymaking. However, the nonlinear and stochastic nature of economic and environmen

Externí odkaz: https://doaj.org/article/9e11c1b2b34d4b0683b95e14eb087b85

Powering Electricity Forecasting with Transfer Learning

Autor: Firuz Kamalov, Hana Sulieman, Sherif Moussa, Jorge Avante Reyes, Murodbek Safaraliev

Publikováno v: Energies, Vol 17, Iss 3, p 626 (2024)

Accurate forecasting is one of the keys to the efficient use of the limited existing energy resources and plays an important role in sustainable development. While most of the current research has focused on energy price forecasting, very few studies

Externí odkaz: https://doaj.org/article/663227d03415488294f818a586b2f80e

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Electricity Demand Forecasting - Probabilistic Demand Forecasting Using Principal Components of Seasonal Temperature Forecasts

Autor: Sjåvik, Eirik Hanssen

Publikováno v: Sjåvik, Eirik Hanssen. Electricity Demand Forecasting - Probabilistic Demand Forecasting Using Principal Components of Seasonal Temperature Forecasts. Master thesis, University of Oslo, 2023

Externí odkaz: http://hdl.handle.net/10852/105317

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