基于VMD和改进TCN的短期光伏发电功率预测

光伏发电功率存在波动性,且光伏出力易受各种气象特征影响,传统TCN网络容易过度强化空间特性而弱化个体特性。针对上述问题,文中提出一种基于VMD和改进TCN的短期光伏发电功率预测模型。通过VMD将原始光伏发电功率时间序列分解为若干不同频率的模态分量,将各个模态分量以及相对应的气象数据输入至改进TCN网络进行建模学习。利用中心频率法确定VMD的最优分解模态分解个数。在传统TCN预测模型的基础上,使用DropBlock正则化取代Dropout正则化以达到抑制卷积层中信息协同的效果,并引入注意力机制自主挖掘并突出关键气象输入特征的影响,量化各气象因素对光伏发电的影响,从而提高预测精度。以江苏省某光伏电站真实数据为例进行仿真实验,结果表明所提预测方法的RMSE为0.62 MW,MAPE为2.03%。     

Artificial intelligence and healthcare: Forecasting of medical bookings through multi-source time-series fusion

Nowadays, Artificial intelligence (AI), combined with the digitalization of healthcare, can lead to substantial improvements in Patient Care, Disease Management, Hospital Administration, and supply chain effectiveness. Among predictive analytics tools, time series forecasting represents a central task to support healthcare management in terms of bookings and medical services predictions. In this context, the development of flexible frameworks to provide robust and reliable predictions became a central point in this healthcare innovation process. This paper presents and discusses a multi-source time series fusion and forecasting framework relying on Deep Learning. By combining weather, air-quality and medical bookings time series through a feature compression stage which preserves temporal patterns, the prediction is provided through a flexible ensemble technique based on machine learning models and a hybrid neural network. The proposed system is able to predict the number of bookings related to a specific medical examination for a 7-days horizon period. To assess the proposed approach’s effectiveness, we rely on time series extracted from a real dataset of administrative e-health records provided by the Campania Region health department, in Italy.     

Measuring quality of government in EU regions across space and time

A wave of recent cross-national research has pointed to the positive consequences for countries with high levels of “quality of government” (QoG), broadly defined, such as corruption, impartiality, and quality of public services. Yet the question of how QoG varies at the sub-national level is still widely overlooked, in particular with measures that are available over time. To address it, we present the third round of data from the regional European Quality of Government Index (EQI) survey corruption (D73), Europe (N44) governance (H11); sub-national (R50), time series (C22), collected in 2017 and built upon the opinions of 78,000 respondents in 193 regions from 21 European countries. The data provides several contributions to the literature. First, while the majority of QoG-type indices rely on expert assessments, the EQI relies on the assessments of citizens, who are the on-the-ground consumers of public services. Second, the data begins to show trends on QoG variation over time, as well as across European regions. Consequently, this data is the most comprehensive sub-national data to date; mapping of QoG within and across EU countries over the past decade. Building on previous rounds of data collected in 2010 and 2013, the 2017 EQI, which is published free for scholarly use, builds on both perceptions and experiences of citizens in public service areas such as health care, education, and law enforcement. This paper presents the results of the latest survey, improved with respect to the previous ones, discussion of trends across space and over time, as well as interesting avenues for future research that we detect across European regions.     

按时序特征优化模型后在线选配的超短期风电预测

讨论超短期风电功率预测(USTWPP)模型的适用性。提出的USTWPP方法,从历史数据的风电功率时间序列(WPTS)中筛选特征量,选择门限值,并将短窗口内的WPTS划分为不同形态的子集,以及一个囊括所有不具有排他性分类特征的"非形态子集"。然后在离线环境下,分别按对应的训练样本优化各子集专用的预测模型及参数。在线应用时,将当前时刻前一个短窗口的WPTS与各子集的分类判据比对,以归入上述子集之一,然后调用相应的预测模型完成USTWPP。最后,以实际算例验证了该方法的有效性。     

Sensory Data Prediction Using Spatiotemporal Correlation and LSTM Recurrent Neural Network

The Wireless Sensor Networks (WSNs) are widely utilized in various industrial and environmental monitoring applications. The process of data gathering within the WSN is significant in terms of reporting the environmental data. However, it might occur that certain sensor node malfunctions due to the energy draining out or unexpected damage. Therefore, the collected data may become inaccurate or incomplete. Focusing on the spatiotemporal correlation among sensor nodes, this paper proposes a novel algorithm to predict the value of the missing or inaccurate data and predict the future data in replacement of certain nonfunctional sensor nodes. The Long-Short-Term-Memory Recurrent Neural Network (LSTM RNN) helps to more accurately derive the time-series data corresponding to the sets of past collected data, making the prediction results more reliable. It is observed from the simulation results that the proposed algorithm provides an outstanding data gathering efficiency while ensuring the data accuracy.     

An automatic tool for reconstructing monthly time-series of hydro-climatic variables at ungauged basins

Integrative information models for filling/reconstructing hydro-climatic time-series are required for a variety of practical applications. A GIS-based model for a rapid and reliable assessment of monthly time-series of several key hydro-climatic variables at the basin scale, is here developed as plug-in and applied to the entire region of Sicily (Italy). The plug-in, once the desired basin outlet section and time-window are selected, uses appropriate spatial techniques and algorithms to identify its drainage area and estimate the corresponding mean areal rainfall and temperatures time-series. A recent regional regressive rainfall-runoff model is successively applied for the assessment of the runoff time-series. Finally, a consolidated temperature-based method is applied to estimate monthly potential evapotranspiration time-series, while, actual evapotranspiration and soil moisture storage time-series are derived through a classical water balance model. The tool, supported by a preliminarily developed database, includes automatic procedures for data retrieving and processing and a user friendly interface.     

时间序列分析方法在干旱区地下水位动态预测中的应用

地下水动力学模型与时间序列模型都可用于分析、预测干旱区地下水位埋深动态变化。但比较而言,时间序列模型无须进行专门的试验来获取有关地质参数,方法易于掌握,计算量小,因而更便于应用推广。本文采用时间序列分析方法分析新疆南部平原区地下水位变化,通过HP（Hodrick-Prescott）滤波方法与ARMA（p,q）模型相结合...     

基于时序数据挖掘的航班延误预测分析

航班延误一直作为国际国内民航业的一个热点问题。通过对航班延误的相关概念进行简要介绍,建立时间序列预测模型,将数据挖掘中隐马尔可夫模型和指数平滑预测方法应用于航班延误预测分析中。通过与所采集时间点的实际航班延误数对比分析来评估预测模型,得到较为理想的预测结果。该分析为航空公司运行指挥中心提供决策支持和理论依据,对保障航班正常运行有着重要的实际意义。     

BP网络在三江平原井灌区地下水埋深预测中的应用

近些年来,由于水田面积迅速增加,导致三江平原井灌水稻区地下水位持续下降,吊泵、局部超采现象时有发生。为解决上述问题,以853农场为例,采用BP神经网络方法建立了853农场地下水埋深动态预测模型,对地下水埋深进行模拟和预测,精度检验结果表明,模型拟合和预测精度均较高。该模型揭示了研究区域地下水动态变化规律,为三江平原井灌区地下水资源的可持续利用提供了科学依据。     

时序DInSAR在重复采动地表沉陷监测中的应用

基于TerraSAR-X高分辨率SAR数据,利用时序DInSAR技术监测矿区开采沉陷,探讨其在开采沉陷监测中的定量化应用。结合矿区工作面回采进度资料,对时序DInSAR结果进行分析,提取不同开采阶段的超前影响角、边界角、起动距等参数,以认识和描述上覆岩层存在重复采动时的地表沉陷规律。利用在工作面上方角反射器位置上同步获取的GPS观测结果对DInSAR技术进行验证,结果表明X波段SAR数据可以准确监测微小形变,从而保证了开采沉陷影响范围监测及角量参数提取的可靠性。