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[HTML][HTML] Exploring the Preference for Discrete over Continuous Reinforcement Learning in Energy Storage Arbitrage
J Jeong, TY Ku, WK Park - Energies, 2024 - mdpi.com
In recent research addressing energy arbitrage with energy storage systems (ESS s),
discrete reinforcement learning (RL) has often been employed, while the underlying reasons …
discrete reinforcement learning (RL) has often been employed, while the underlying reasons …
Electricity Load Forecasting using Hybrid Datasets with Linear Interpolation and Synthetic Data
Electricity load forecasting is an important aspect of power system management. Improving
forecasting accuracy ensures reliable electricity supply, grid operations, and cost savings …
forecasting accuracy ensures reliable electricity supply, grid operations, and cost savings …
Techniques for Handling Missing Values in Customers Electricity Data: A Systematic Literature Review
MDA Rusbandi, S Fauziati… - 2024 2nd International …, 2024 - ieeexplore.ieee.org
The lack of complete data is a widespread challenge when analyzing authentic, real-world
datasets. In fact, the missing values can substantially degrade the precision and …
datasets. In fact, the missing values can substantially degrade the precision and …
Research on Power Grid Flow Section Completion Algorithm Based on Transformer
J Song, X Chen, X Sun, Q He, S Zhang… - … Seminar on Artificial …, 2024 - ieeexplore.ieee.org
In order to solver the problem of partial missing in power grid flow section data, this article
proposes a transformer-based power grid flow section completion algorithm. Based on the …
proposes a transformer-based power grid flow section completion algorithm. Based on the …