干扰风暴时间（DST）指数已被广泛用作环电流强度的代理，因此被用作地磁活性的量度。它是由地磁赤道区域中四个地面磁力计测量得出的。我们提出了一种新的模型，用于预测$ DST $，在1到6个小时之间提前时间。该模型首先是使用封闭式复发单元（GRU）网络开发的，该网络是使用太阳风参数训练的。然后，使用Ackrue方法估算$ DST $模型的不确定性[Camporeale等。2021]。最后，开发了一种多保真提升方法，以提高模型的准确性并降低其相关的不确定性。结果表明，开发的模型可以通过13.54 $ \ mathrm {nt} $的根平方（RMSE）（RMSE）预测$ DST $ 6小时。这比持久性模型和简单的GRU模型要好得多。

With the evolution of power systems as it is becoming more intelligent and interactive system while increasing in flexibility with a larger penetration of renewable energy sources, demand prediction on a short-term resolution will inevitably become more and more crucial in designing and managing the future grid, especially when it comes to an individual household level. Projecting the demand for electricity for a single energy user, as opposed to the aggregated power consumption of residential load on a wide scale, is difficult because of a considerable number of volatile and uncertain factors. This paper proposes a customized GRU (Gated Recurrent Unit) and Long Short-Term Memory (LSTM) architecture to address this challenging problem. LSTM and GRU are comparatively newer and among the most well-adopted deep learning approaches. The electricity consumption datasets were obtained from individual household smart meters. The comparison shows that the LSTM model performs better for home-level forecasting than alternative prediction techniques-GRU in this case. To compare the NN-based models with contrast to the conventional statistical technique-based model, ARIMA based model was also developed and benchmarked with LSTM and GRU model outcomes in this study to show the performance of the proposed model on the collected time series data.

The geospace environment is volatile and highly driven. Space weather has effects on Earth's magnetosphere that cause a dynamic and enigmatic response in the thermosphere, particularly on the evolution of neutral mass density. Many models exist that use space weather drivers to produce a density response, but these models are typically computationally expensive or inaccurate for certain space weather conditions. In response, this work aims to employ a probabilistic machine learning (ML) method to create an efficient surrogate for the Thermosphere Ionosphere Electrodynamics General Circulation Model (TIE-GCM), a physics-based thermosphere model. Our method leverages principal component analysis to reduce the dimensionality of TIE-GCM and recurrent neural networks to model the dynamic behavior of the thermosphere much quicker than the numerical model. The newly developed reduced order probabilistic emulator (ROPE) uses Long-Short Term Memory neural networks to perform time-series forecasting in the reduced state and provide distributions for future density. We show that across the available data, TIE-GCM ROPE has similar error to previous linear approaches while improving storm-time modeling. We also conduct a satellite propagation study for the significant November 2003 storm which shows that TIE-GCM ROPE can capture the position resulting from TIE-GCM density with < 5 km bias. Simultaneously, linear approaches provide point estimates that can result in biases of 7 - 18 km.

预测基金绩效对投资者和基金经理都是有益的，但这是一项艰巨的任务。在本文中，我们测试了深度学习模型是否比传统统计技术更准确地预测基金绩效。基金绩效通常通过Sharpe比率进行评估，该比例代表了风险调整的绩效，以确保基金之间有意义的可比性。我们根据每月收益率数据序列数据计算了年度夏普比率，该数据的时间序列数据为600多个投资于美国上市大型股票的开放式共同基金投资。我们发现，经过现代贝叶斯优化训练的长期短期记忆（LSTM）和封闭式复发单元（GRUS）深度学习方法比传统统计量相比，预测基金的Sharpe比率更高。结合了LSTM和GRU的预测的合奏方法，可以实现所有模型的最佳性能。有证据表明，深度学习和结合能提供有希望的解决方案，以应对基金绩效预测的挑战。

对极端事件的风险评估需要准确估算超出历史观察范围的高分位数。当风险取决于观察到的预测因子的值时，回归技术用于在预测器空间中插值。我们提出的EQRN模型将来自神经网络和极值理论的工具结合到能够在存在复杂预测依赖性的情况下外推的方法中。神经网络自然可以在数据中融合其他结构。我们开发了EQRN的经常性版本，该版本能够在时间序列中捕获复杂的顺序依赖性。我们将这种方法应用于瑞士AARE集水区中洪水风险的预测。它利用从时空和时间上的多个协变量中利用信息，以提供对回报水平和超出概率的一日预测。该输出从传统的极值分析中补充了静态返回水平，并且预测能够适应不断变化的气候中经历的分配变化。我们的模型可以帮助当局更有效地管理洪水，并通过预警系统最大程度地减少其灾难性影响。

熟练的水流预测可以为水政策和管理各个领域的决策提供信息。我们集成了数值天气预测集合和分布式水文模型，以在中范围的交货时间（1-7天）下生成集合流量预测。我们展示了一项用于在美国东部的Susquehanna河流盆地的后处理过程中进行机器学习应用的案例研究。为了进行预测验证，我们使用不同的指标，例如技能得分和可靠性图，以提前时间，流量阈值和季节为条件。验证结果表明，机器学习后处理器可以改善相对于低复杂性预测（例如气候和时间持久性）以及确定性和原始集合预测的水流预测。与原始合奏相比，与较短的交货时间相比，在中等时间表的相对增益在后期时间表通常更高。与低压流相比，高流量和与凉爽的流量相比。总体而言，我们的结果突出了机器学习在许多方面的好处，以提高流量预测的技能和可靠性。

生产精确的天气预报和不确定的不确定性的可靠量化是一个开放的科学挑战。到目前为止，集团预测是最成功的方法，以产生相关预测的方法以及估计其不确定性。集合预测的主要局限性是高计算成本，难以捕获和量化不同的不确定性来源，特别是与模型误差相关的源。在这项工作中，进行概念证据模型实验，以检查培训的ANN的性能，以预测系统的校正状态和使用单个确定性预测作为输入的状态不确定性。我们比较不同的培训策略：一个基于使用集合预测的平均值和传播作为目标的直接培训，另一个依赖于使用确定性预测作为目标的决定性预测，其中来自数据隐含地学习不确定性。对于最后一种方法，提出和评估了两个替代损失函数，基于数据观察似然和基于误差的本地估计来评估另一个丢失功能。在不同的交货时间和方案中检查网络的性能，在没有模型错误的情况下。使用Lorenz'96模型的实验表明，ANNS能够模拟集合预测的一些属性，如最不可预测模式的过滤和预测不确定性的状态相关量化。此外，ANNS提供了在模型误差存在下的预测不确定性的可靠估计。

Forecasting time series with extreme events has been a challenging and prevalent research topic, especially when the time series data are affected by complicated uncertain factors, such as is the case in hydrologic prediction. Diverse traditional and deep learning models have been applied to discover the nonlinear relationships and recognize the complex patterns in these types of data. However, existing methods usually ignore the negative influence of imbalanced data, or severe events, on model training. Moreover, methods are usually evaluated on a small number of generally well-behaved time series, which does not show their ability to generalize. To tackle these issues, we propose a novel probability-enhanced neural network model, called NEC+, which concurrently learns extreme and normal prediction functions and a way to choose among them via selective back propagation. We evaluate the proposed model on the difficult 3-day ahead hourly water level prediction task applied to 9 reservoirs in California. Experimental results demonstrate that the proposed model significantly outperforms state-of-the-art baselines and exhibits superior generalization ability on data with diverse distributions.

PV power forecasting models are predominantly based on machine learning algorithms which do not provide any insight into or explanation about their predictions (black boxes). Therefore, their direct implementation in environments where transparency is required, and the trust associated with their predictions may be questioned. To this end, we propose a two stage probabilistic forecasting framework able to generate highly accurate, reliable, and sharp forecasts yet offering full transparency on both the point forecasts and the prediction intervals (PIs). In the first stage, we exploit natural gradient boosting (NGBoost) for yielding probabilistic forecasts, while in the second stage, we calculate the Shapley additive explanation (SHAP) values in order to fully comprehend why a prediction was made. To highlight the performance and the applicability of the proposed framework, real data from two PV parks located in Southern Germany are employed. Comparative results with two state-of-the-art algorithms, namely Gaussian process and lower upper bound estimation, manifest a significant increase in the point forecast accuracy and in the overall probabilistic performance. Most importantly, a detailed analysis of the model's complex nonlinear relationships and interaction effects between the various features is presented. This allows interpreting the model, identifying some learned physical properties, explaining individual predictions, reducing the computational requirements for the training without jeopardizing the model accuracy, detecting possible bugs, and gaining trust in the model. Finally, we conclude that the model was able to develop complex nonlinear relationships which follow known physical properties as well as human logic and intuition.

将间歇性可再生能源集成到大量的电网中是具有挑战性的。旨在解决这一困难的建立良好的方法涉及即将到来的能源供应可变性以适应电网的响应。在太阳能中，可以在全天空摄像机（前方30分钟）和卫星观测（提前6小时）的不同时间尺度上预测由遮挡云引起的短期变化。在这项研究中，我们将这两种互补的观点集成到单个机器学习框架中的云覆盖物上，以改善时间内（最高60分钟）的辐照度预测。确定性和概率预测均在不同的天气条件（晴朗，多云，阴天）以及不同的输入配置（天空图像，卫星观测和/或过去的辐照度值）中进行评估。我们的结果表明，混合模型在晴朗的条件下有益于预测，并改善了长期预测。这项研究为将来的新颖方法奠定了基础，即在单个学习框架中将天空图像和卫星观测结合起来，以推动太阳现象。

机器学习（ML）近年来往往应用于太空天气（SW）问题。 SW起源于太阳能扰动，包括由此产生的复杂变化，它们导致太阳和地球之间的系统。这些系统紧密耦合并不太了解。这为熟练的模型创造了具有关于他们预测的信心的知识。这种动态系统的一个例子是热层，地球上层大气的中性区域。我们无法预测其在低地球轨道中对象的卫星拖拽和碰撞操作的背景下具有严重的影响。即使使用（假设）完美的驾驶员预测，我们对系统的不完全知识也会导致往往是不准确的中性质量密度预测。正在进行持续努力来提高模型准确性，但密度模型很少提供不确定性的估计。在这项工作中，我们提出了两种技术来开发非线性ML模型以预测热散，同时提供校准的不确定性估计：蒙特卡罗（MC）丢失和直接预测概率分布，既使用预测密度（NLPD）损耗函数的负对数。我们展示了在本地和全局数据集上培训的模型的性能。这表明NLPD为这两种技术提供了类似的结果，但是直接概率方法具有更低的计算成本。对于在集合HASDM密度数据库上回归的全局模型，我们在具有良好校准的不确定性估计的独立测试数据上实现11％的错误。使用原位校准密度数据集，这两种技术都提供了13％的测试误差。 CHAMP模型（独立数据）占测试所有预测间隔的完美校准的2％。该模型也可用于获得具有给定时期的不确定性的全局预测。

最近实现了更准确的短期预测的数据驱动的空气质量预测。尽管取得了成功，但大多数目前的数据驱动解决方案都缺乏适当的模型不确定性的量化，以传达信任预测的程度。最近，在概率深度学习中已经制定了几种估计不确定性的实用工具。但是，在空气质量预测领域的域中没有经验应用和广泛的比较这些工具。因此，这项工作在空气质量预测的真实环境中应用了最先进的不确定性量化。通过广泛的实验，我们描述了培训概率模型，并根据经验性能，信心可靠性，置信度估计和实际适用性评估其预测性不确定性。我们还使用空气质量数据中固有的“自由”对抗培训和利用时间和空间相关性提出改善这些模型。我们的实验表明，所提出的模型比以前的工作更好地在量化数据驱动空气质量预测中的不确定性方面表现出。总体而言，贝叶斯神经网络提供了更可靠的不确定性估计，但可能挑战实施和规模。其他可扩展方法，如深合奏，蒙特卡罗（MC）辍学和随机重量平均-Gaussian（SWAG）可以执行良好，如果正确应用，但具有不同的权衡和性能度量的轻微变化。最后，我们的结果表明了不确定性估计的实际影响，并证明了，实际上，概率模型更适合提出知情决策。代码和数据集可用于\ url {https:/github.com/abdulmajid-murad/deep_probabilistic_forecast}

We introduce a machine-learning (ML)-based weather simulator--called "GraphCast"--which outperforms the most accurate deterministic operational medium-range weather forecasting system in the world, as well as all previous ML baselines. GraphCast is an autoregressive model, based on graph neural networks and a novel high-resolution multi-scale mesh representation, which we trained on historical weather data from the European Centre for Medium-Range Weather Forecasts (ECMWF)'s ERA5 reanalysis archive. It can make 10-day forecasts, at 6-hour time intervals, of five surface variables and six atmospheric variables, each at 37 vertical pressure levels, on a 0.25-degree latitude-longitude grid, which corresponds to roughly 25 x 25 kilometer resolution at the equator. Our results show GraphCast is more accurate than ECMWF's deterministic operational forecasting system, HRES, on 90.0% of the 2760 variable and lead time combinations we evaluated. GraphCast also outperforms the most accurate previous ML-based weather forecasting model on 99.2% of the 252 targets it reported. GraphCast can generate a 10-day forecast (35 gigabytes of data) in under 60 seconds on Cloud TPU v4 hardware. Unlike traditional forecasting methods, ML-based forecasting scales well with data: by training on bigger, higher quality, and more recent data, the skill of the forecasts can improve. Together these results represent a key step forward in complementing and improving weather modeling with ML, open new opportunities for fast, accurate forecasting, and help realize the promise of ML-based simulation in the physical sciences.

我们提出了一种利用分布人工神经网络的概率电价预测（EPF）的新方法。EPF的新型网络结构基于包含概率层的正则分布多层感知器（DMLP）。使用TensorFlow概率框架，神经网络的输出被定义为一个分布，是正常或可能偏斜且重尾的Johnson的SU（JSU）。在预测研究中，将该方法与最新基准进行了比较。该研究包括预测，涉及德国市场的日常电价。结果显示了对电价建模时较高时刻的重要性的证据。

我们基准了一个简单学习模型的亚季节预测工具包，该工具包优于操作实践和最先进的机器学习和深度学习方法。这些模型，由Mouatadid等人引入。 （2022），包括（a）气候++，这是气候学的一种适应性替代品，对于降水而言，准确性9％，比美国运营气候预测系统（CFSV2）高9％，熟练250％； （b）CFSV2 ++，一种学习的CFSV2校正，可将温度和降水精度提高7-8％，技能提高50-275％； （c）持久性++是一种增强的持久性模型，将CFSV2预测与滞后测量相结合，以将温度和降水精度提高6-9％，技能提高40-130％。在整个美国，气候++，CFSV2 ++和持久性++工具包始终优于标准气象基准，最先进的机器和深度学习方法，以及欧洲中等范围的天气预报集合中心。

在智能电网和负载平衡的背景下，每日峰值负荷预测已成为能源行业利益相关者的关键活动。对峰值幅度和时序的理解对于实现峰值剃须等智能电网策略至关重要。本文提出的建模方法利用了高分辨率和低分辨率信息来预测每日峰值需求规模和时序。由此产生的多分辨率建模框架可以适应不同的模型类。本文的主要贡献是一般性和正式介绍多分辨率建模方法，b）关于通过广义添加剂模型和神经网络和C）实验结果的不同决议的建模方法的讨论英国电力市场。结果证实，建议的建模方法的预测性能与低分辨率和高分辨率替代品具有竞争力。

了解极端事件及其可能性是研究气候变化影响，风险评估，适应和保护生物的关键。在这项工作中，我们开发了一种方法来构建极端热浪的预测模型。这些模型基于卷积神经网络，对极长的8，000年气候模型输出进行了培训。由于极端事件之间的关系本质上是概率的，因此我们强调概率预测和验证。我们证明，深度神经网络适用于法国持续持续14天的热浪，快速动态驱动器提前15天（500 hpa地球电位高度场），并且在慢速较长的交货时间内，慢速物理时间驱动器（土壤水分）。该方法很容易实现和通用。我们发现，深神经网络选择了与北半球波数字3模式相关的极端热浪。我们发现，当将2米温度场添加到500 HPA地球电位高度和土壤水分场中时，2米温度场不包含任何新的有用统计信息。主要的科学信息是，训练深层神经网络预测极端热浪的发生是在严重缺乏数据的情况下发生的。我们建议大多数其他应用在大规模的大气和气候现象中都是如此。我们讨论了处理缺乏数据制度的观点，例如罕见的事件模拟，以及转移学习如何在后一种任务中发挥作用。

Wind power forecasting helps with the planning for the power systems by contributing to having a higher level of certainty in decision-making. Due to the randomness inherent to meteorological events (e.g., wind speeds), making highly accurate long-term predictions for wind power can be extremely difficult. One approach to remedy this challenge is to utilize weather information from multiple points across a geographical grid to obtain a holistic view of the wind patterns, along with temporal information from the previous power outputs of the wind farms. Our proposed CNN-RNN architecture combines convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to extract spatial and temporal information from multi-dimensional input data to make day-ahead predictions. In this regard, our method incorporates an ultra-wide learning view, combining data from multiple numerical weather prediction models, wind farms, and geographical locations. Additionally, we experiment with global forecasting approaches to understand the impact of training the same model over the datasets obtained from multiple different wind farms, and we employ a method where spatial information extracted from convolutional layers is passed to a tree ensemble (e.g., Light Gradient Boosting Machine (LGBM)) instead of fully connected layers. The results show that our proposed CNN-RNN architecture outperforms other models such as LGBM, Extra Tree regressor and linear regression when trained globally, but fails to replicate such performance when trained individually on each farm. We also observe that passing the spatial information from CNN to LGBM improves its performance, providing further evidence of CNN's spatial feature extraction capabilities.

In this paper, we propose a new short-term load forecasting (STLF) model based on contextually enhanced hybrid and hierarchical architecture combining exponential smoothing (ES) and a recurrent neural network (RNN). The model is composed of two simultaneously trained tracks: the context track and the main track. The context track introduces additional information to the main track. It is extracted from representative series and dynamically modulated to adjust to the individual series forecasted by the main track. The RNN architecture consists of multiple recurrent layers stacked with hierarchical dilations and equipped with recently proposed attentive dilated recurrent cells. These cells enable the model to capture short-term, long-term and seasonal dependencies across time series as well as to weight dynamically the input information. The model produces both point forecasts and predictive intervals. The experimental part of the work performed on 35 forecasting problems shows that the proposed model outperforms in terms of accuracy its predecessor as well as standard statistical models and state-of-the-art machine learning models.

太阳能的高效整合到电力组合中取决于其间歇性的可靠预期。预测由云覆盖动态产生的太阳辐照度的时间变异的有希望的方法是基于地面天空图像或卫星图像序列的分析。尽管结果令人鼓舞，但现有深度学习方法的经常性限制在于对过去观察的反应而不是积极预期未来事件的无处不在的趋势。这导致频繁的时间滞后和有限的预测突发事件的能力。为了解决这一挑战，我们介绍了Eclipse，一种时空神经网络架构，即模型从天空图像模拟云运动，不仅预测未来的辐照水平，而且还可以在本地辐照度图上提供更丰富的信息。我们表明Eclipse预期关键事件，并在产生视觉上现实期货的同时降低时间延误。