Regional solar power forecasting, which involves predicting the total power
generation from all rooftop photovoltaic systems in a region holds significant
importance for various stakeholders in the energy sector. However, the vast
amount of solar power generation and weather time series from geographically
dispersed locations that need to be considered in the forecasting process makes
accurate regional forecasting challenging. Therefore, previous work has limited
the focus to either forecasting a single time series (i.e., aggregated time
series) which is the addition of all solar generation time series in a region,
disregarding the location-specific weather effects or forecasting solar
generation time series of each PV site (i.e., individual time series)
independently using location-specific weather data, resulting in a large number
of forecasting models. In this work, we propose two deep-learning-based
regional forecasting methods that can effectively leverage both types of time
series (aggregated and individual) with weather data in a region. We propose
two hierarchical temporal convolutional neural network architectures (HTCNN)
and two strategies to adapt HTCNNs for regional solar power forecasting. At
first, we explore generating a regional forecast using a single HTCNN. Next, we
divide the region into multiple sub-regions based on weather information and
train separate HTCNNs for each sub-region; the forecasts of each sub-region are
then added to generate a regional forecast. The proposed work is evaluated
using a large dataset collected over a year from 101 locations across Western
Australia to provide a day ahead forecast. We compare our approaches with
well-known alternative methods and show that the sub-region HTCNN requires
fewer individual networks and achieves a forecast skill score of 40.2% reducing
a statistically significant error by 6.5% compared to the best counterpart.

本文提出了两种基于深度学习的区域太阳能发电预测方法，分别使用单一分层时序卷积神经网络和基于子区域的时序卷积神经网络，通过利用聚合和个体数据，以及区域内的天气数据进行预测。利用 101 个西澳大利亚地点收集的大量数据进行评估，与其他方法相比，子区域时序卷积神经网络在减少个体网络数量的同时，能够以 40.2% 的预测技巧得分降低 6.5% 的统计显著误差。

利用历史发电和天气数据的分层时序卷积神经网络进行区域光伏日前功率预测

Day-ahead regional solar power forecasting with hierarchical temporal  convolutional neural networks using historical power generation and weather  data

Weather forecasting requires not only accuracy but also the ability to
perform probabilistic prediction. However, deterministic weather forecasting
methods do not support probabilistic predictions, and conversely, probabilistic
models tend to be less accurate. To address these challenges, in this paper, we
introduce the \textbf{\textit{D}}eterministic \textbf{\textit{G}}uidance
\textbf{\textit{D}}iffusion \textbf{\textit{M}}odel (DGDM) for probabilistic
weather forecasting, integrating benefits of both deterministic and
probabilistic approaches. During the forward process, both the deterministic
and probabilistic models are trained end-to-end. In the reverse process,
weather forecasting leverages the predicted result from the deterministic
model, using as an intermediate starting point for the probabilistic model. By
fusing deterministic models with probabilistic models in this manner, DGDM is
capable of providing accurate forecasts while also offering probabilistic
predictions. To evaluate DGDM, we assess it on the global weather forecasting
dataset (WeatherBench) and the common video frame prediction benchmark (Moving
MNIST). We also introduce and evaluate the Pacific Northwest Windstorm
(PNW)-Typhoon weather satellite dataset to verify the effectiveness of DGDM in
high-resolution regional forecasting. As a result of our experiments, DGDM
achieves state-of-the-art results not only in global forecasting but also in
regional forecasting. The code is available at:
https://github.com/DongGeun-Yoon/DGDM.

该论文介绍了一种用于天气预报的确定性导向扩散模型（DGDM），它整合了确定性和概率方法的优势，既能提供准确预测，又能进行概率预测。通过在正向过程中训练确定性模型和概率模型，并利用确定性模型的预测结果作为概率模型的起始点，在全球和区域预测方面，DGDM 取得了最先进的结果。