Ahead-of-time forecasting of the output power of power plants is essential
for the stability of the electricity grid and ensuring uninterrupted service.
However, forecasting renewable energy sources is difficult due to the chaotic
behavior of natural energy sources. This paper presents a new approach to
estimate short-term solar irradiance from sky images. The~proposed algorithm
extracts features from sky images and use learning-based techniques to estimate
the solar irradiance. The~performance of proposed machine learning (ML)
algorithm is evaluated using two publicly available datasets of sky images.
The~datasets contain over 350,000 images for an interval of 16 years, from 2004
to 2020, with the corresponding global horizontal irradiance (GHI) of each
image as the ground truth. Compared to the state-of-the-art computationally
heavy algorithms proposed in the literature, our approach achieves competitive
results with much less computational complexity for both nowcasting and
forecasting up to 4 h ahead of time.

本文提出了一种新的方法，通过从天空图像中提取特征并使用基于学习的技术来估计短期太阳辐照度，实现了对可再生能源的预测，该方法在计算复杂性方面比现有的算法具有更高的竞争力。

基于天空成像的太阳辐照度预测与机器学习

Sky Imager-Based Forecast of Solar Irradiance Using Machine Learning

This paper introduces a deep reinforcement learning (RL) framework for
optimizing the operations of power plants pairing renewable energy with
storage. The objective is to maximize revenue from energy markets while
minimizing storage degradation costs and renewable curtailment. The framework
handles complexities such as time coupling by storage devices, uncertainty in
renewable generation and energy prices, and non-linear storage models. The
study treats the problem as a hierarchical Markov Decision Process (MDP) and
uses component-level simulators for storage. It utilizes RL to incorporate
complex storage models, overcoming restrictions of optimization-based methods
that require convex and differentiable component models. A significant aspect
of this approach is ensuring policy actions respect system constraints,
achieved via a novel method of projecting potentially infeasible actions onto a
safe state-action set. The paper demonstrates the efficacy of this approach
through extensive experiments using data from US and Indian electricity
markets, comparing the learned RL policies with a baseline control policy and a
retrospective optimal control policy. It validates the adaptability of the
learning framework with various storage models and shows the effectiveness of
RL in a complex energy optimization setting, in the context of multi-market
bidding, probabilistic forecasts, and accurate storage component models.

该研究介绍了一种深度强化学习框架，用于优化可再生能源和储能发电厂的操作，以最大化能源市场收益，同时最小化储能损耗成本和可再生资源废弃。