Accurate forecasts for day-ahead photovoltaic (PV) power generation are
crucial to support a high PV penetration rate in the local electricity grid and
to assure stability in the grid. We use state-of-the-art tree-based machine
learning methods to produce such forecasts and, unlike previous studies, we
hereby account for (i) the effects various meteorological as well as
astronomical features have on PV power production, and this (ii) at coarse as
well as granular spatial locations. To this end, we use data from Belgium and
forecast day-ahead PV power production at an hourly resolution. The insights
from our study can assist utilities, decision-makers, and other stakeholders in
optimizing grid operations, economic dispatch, and in facilitating the
integration of distributed PV power into the electricity grid.

用最先进的基于树的机器学习方法来预测光伏发电的日前准确性非常关键，该研究还考虑了气象和天文学因素对光伏发电产量的影响，以及粗粒度和细粒度空间位置的影响。这项研究的结果可以帮助电网运营商、决策者和其他利益相关者优化电网运营、经济调度，并促进分布式光伏发电的整合。

基于树形预测的细粒度气象特征日前太阳能发电预测

Tree-based Forecasting of Day-ahead Solar Power Generation from Granular  Meteorological Features

Photovoltaic and wind power forecasts in power systems with a high share of
renewable energy are essential in several applications. These include stable
grid operation, profitable power trading, and forward-looking system planning.
However, there is a lack of publicly available datasets for research on machine
learning based prediction methods. This paper provides an openly accessible
time series dataset with realistic synthetic power data. Other publicly and
non-publicly available datasets often lack precise geographic coordinates,
timestamps, or static power plant information, e.g., to protect business
secrets. On the opposite, this dataset provides these. The dataset comprises
120 photovoltaic and 273 wind power plants with distinct sides all over Germany
from 500 days in hourly resolution. This large number of available sides allows
forecasting experiments to include spatial correlations and run experiments in
transfer and multi-task learning. It includes side-specific, power
source-dependent, non-synthetic input features from the ICON-EU weather model.
A simulation of virtual power plants with physical models and actual
meteorological measurements provides realistic synthetic power measurement time
series. These time series correspond to the power output of virtual power
plants at the location of the respective weather measurements. Since the
synthetic time series are based exclusively on weather measurements, possible
errors in the weather forecast are comparable to those in actual power data. In
addition to the data description, we evaluate the quality of
weather-prediction-based power forecasts by comparing simplified physical
models and a machine learning model. This experiment shows that forecasts
errors on the synthetic power data are comparable to real-world historical
power measurements.

本论文提出一个可公开获取的带有真实合成功率数据的时间序列数据集，其中包括 120 个太阳能和 273 个风电站，并使用物理模型和真实气象测量值进行了虚拟电厂的仿真，同时结合物理模型和机器学习模型对天气预报为基础的功率预测进行了评估。

光伏和风力发电合成预测数据

Synthetic Photovoltaic and Wind Power Forecasting Data

Due to the different losses caused by various photovoltaic (PV) array faults,
accurate diagnosis of fault types is becoming increasingly important. Compared
with a single one, multiple PV stations collect sufficient fault samples, but
their data is not allowed to be shared directly due to potential conflicts of
interest. Therefore, federated learning can be exploited to train a
collaborative fault diagnosis model. However, the modeling efficiency is
seriously affected by the model update mechanism since each PV station has a
different computing capability and amount of data. Moreover, for the safe and
stable operation of the PV system, the robustness of collaborative modeling
must be guaranteed rather than simply being processed on a central server. To
address these challenges, a novel asynchronous decentralized federated learning
(ADFL) framework is proposed. Each PV station not only trains its local model
but also participates in collaborative fault diagnosis by exchanging model
parameters to improve the generalization without losing accuracy. The global
model is aggregated distributedly to avoid central node failure. By designing
the asynchronous update scheme, the communication overhead and training time
are greatly reduced. Both the experiments and numerical simulations are carried
out to verify the effectiveness of the proposed method.

为应对分布式光伏阵列的故障诊断问题，本文提出了一种异步分散式联邦学习框架，用于同时训练各个 PV 站的本地模型并逐步更新全局模型，以优化模型的泛化性能和准确性，降低通信开销和训练时间。实验结果表明，该方法是有效的。