Recently the interval forecasting of carbon price is investigated by advanced research since
it can better quantify the uncertainty and reliability of the forecast value in comparison with …

The future carbon price is crucial to relevant companies, investors, and carbon
policymakers, and the significance of carbon price prediction research is self-evident …

We propose an explainable machine learning (ML) model with uncertainty quantification
(UQ) to improve multi-step reservoir inflow forecasting. Traditional ML methods have …

The development of advanced technologies based on computer aid models in the domain of
crops and agriculture productively is a modern advancement. Machine learning (ML) based …

Climate change affects patterns and uncertainties associated with river water regimes, which
significantly impact hydropower generation and reservoir storage operation. Hence, reliable …

Study region Song bengue confluent in Cameroon regulates the river flow rate for hydro
energy production with input from four upstream reservoirs. Study focus Deep learning …

Accurately predicting hydrological runoff is crucial for water resource allocation and power
station scheduling. However, there is no perfect model that can accurately predict future …

This paper introduces a shape‐based inflow scenarios reduction framework applied in long‐
term hydro‐thermal scheduling. This scheduling problem involves strategically managing …

The main task of short-term scheduling in a free market is to provide decision support for
market bidding and unit commitment for the coming hours and days. In such a realm, an …

This paper proposes a framework consisting of a forepart precursor and the Scenario Fan
Problem (SFP) for long-term hydropower scheduling, using shape-based feature extraction …