
The core message of this article is to propose a value-oriented renewable energy forecasting approach that aligns the forecast model training with the objective of minimizing the overall operation costs, rather than solely focusing on statistical accuracy.


coremsg

improving-renewable-energy-dispatch-through-value-oriented-forecasting


Improving Renewable Energy Dispatch through Value-oriented Forecasting


title_rewrite


This paper proposes a nonparametric end-to-end method for probabilistic forecasting of distributed renewable generation outputs that effectively handles missing data through iterative imputation and end-to-end training.


nonparametric-end-to-end-probabilistic-forecasting-of-distributed-generation-outputs-with-missing-data-imputation


Nonparametric End-to-End Probabilistic Forecasting of Distributed Generation Outputs with Missing Data Imputation



MATNet proposes a novel self-attention transformer-based architecture for day-ahead PV generation forecasting, combining historical and forecast weather data with PV production data to improve accuracy significantly.


MATNet-Multi-Level-Fusion-Transformer-Based-Model-for-Day-Ahead-PV-Generation-Forecasting


MATNet: Multi-Level Fusion Transformer-Based Model for Day-Ahead PV Generation Forecasting
