The increasing global demand for clean and environmentally friendly energy
resources has caused increased interest in harnessing solar power through
photovoltaic (PV) systems for smart grids and homes. However, the inherent
unpredictability of PV generation poses problems associated with smart grid
planning and management, energy trading and market participation, demand
response, reliability, etc. Therefore, solar irradiance forecasting is
essential for optimizing PV system utilization. This study proposes the
next-generation machine learning algorithms such as random forests, Extreme
Gradient Boosting (XGBoost), Light Gradient Boosted Machine (lightGBM)
ensemble, CatBoost, and Multilayer Perceptron Artificial Neural Networks
(MLP-ANNs) to forecast solar irradiance. Besides, Bayesian optimization is
applied to hyperparameter tuning. Unlike tree-based ensemble algorithms that
select the features intrinsically, MLP-ANN needs feature selection as a
separate step. The simulation results indicate that the performance of the
MLP-ANNs improves when feature selection is applied. Besides, the random forest
outperforms the other learning algorithms.

利用机器学习算法如随机森林、极端梯度提升、轻量级梯度提升机、CatBoost 和多层感知器人工神经网络来预测太阳辐照度，并通过特征选择进一步提高预测性能。

智能电网中太阳辐射预测的机器学习算法比较分析

Comparative Analysis of Machine Learning Algorithms for Solar Irradiance  Forecasting in Smart Grids

This paper proposes an anticipative transformer-based model for short-term
solar irradiance forecasting. Given a sequence of sky images, our proposed
vision transformer encodes features of consecutive images, feeding into a
transformer decoder to predict irradiance values associated with future unseen
sky images. We show that our model effectively learns to attend only to
relevant features in images in order to forecast irradiance. Moreover, the
proposed anticipative transformer captures long-range dependencies between sky
images to achieve a forecasting skill of 21.45 % on a 15 minute ahead
prediction for a newly introduced dataset of all-sky images when compared to a
smart persistence model.

本文提出了一种基于 Transformer 的短期太阳辐射预测模型，通过对连续的天空图像进行特征编码，并将其输入 Transformer 解码器，预测与未来未见天空图像相关的辐照值，同时捕捉天空图像之间的长程依赖关系，可实现对所有天空图像数据集在 15 分钟内的辐射预测精度提高至 21.45%，优于智能持久性模型。

太阳辐照预测变压器

Solar Irradiance Anticipative Transformer

Translational invariance induced by pooling operations is an inherent
property of convolutional neural networks, which facilitates numerous computer
vision tasks such as classification. Yet to leverage rotational invariant
tasks, convolutional architectures require specific rotational invariant layers
or extensive data augmentation to learn from diverse rotated versions of a
given spatial configuration. Unwrapping the image into its polar coordinates
provides a more explicit representation to train a convolutional architecture
as the rotational invariance becomes translational, hence the visually distinct
but otherwise equivalent rotated versions of a given scene can be learnt from a
single image. We show with two common vision-based solar irradiance forecasting
challenges (i.e. using ground-taken sky images or satellite images), that this
preprocessing step significantly improves prediction results by standardising
the scene representation, while decreasing training time by a factor of 4
compared to augmenting data with rotations. In addition, this transformation
magnifies the area surrounding the centre of the rotation, leading to more
accurate short-term irradiance predictions.

对图像进行极坐标变换后，利用卷积神经网络进行太阳辐射预测比进行数据旋转增广的方法具有更高的预测准确率和更短的训练时间。