Despite the advances in solar forecasting methods, and their ever-increasing accuracy, little
is known about their value for real applications, eg, bidding in the electricity market, power …

Reducing peak demand on the utility grid benefits both grid operators and consumers.
However, achieving this goal while maintaining human comfort presents a significant …

A recent key research area in renewable energy integration is the development of tools and
methods to capture and accommodate the uncertainty associated with the forecast errors …

Highly competitiveness of solar power plants in the energy market requires addressing the
active research problem of solar energy forecasting. To make precise forecasts, however …

We conduct a comparative study of deterministic-to-probabilistic (D2P) and probabilistic-to-
probabilistic (P2P) forecasting methods for photovoltaic (PV) power generation. In this …

This paper proposes a data-driven method to estimate real-time regulation reserve
requirements in the California Independent System Operator (CAISO) balancing authority …

The large scale integration of renewable energy sources and energy storage technologies is
driven by energy transition. The integrated technologies pose multiple uncertainties and …

The growing penetration of variable renewable energy sources (VRES) requires additional
flexibility reserve to ensure reliable power system operations. Current industry practice …

An increase in renewable energy demand and its energy mix caused the use of solar power
to become crucial. However, the uncertainty of solar power generation due to weather …

This paper explores the field of machine learning (ML) techniques to improve the precision
of renewable energy forecasting, with a focus on hydroelectric power production. Since …