基于最小二乘法及信息熵的短期水文预报组合模型的比较

为探讨组合预报中平均加权法的实用性,以三峡流域宜昌站为例,建立了基于最小二乘法和信息熵理论的两种径流组合预报模型,并将组合预报结果与原单一模型的预报结果做了对比,选取具有代表性的预报精度评定指标检验预报精度。结果表明,两种组合预报模型均显著改善了预报精度评定指标,提高了短期水文预报精度,突破了传统单一水文预报模型的局限性,实际应用时可根据预报精度评定的侧重点选择合适的组合预报模型。     

Short‐term forecasting of categorical changes in wind power with Markov chain models

As penetrations of renewable wind energy increase, accurate short‐term predictions of wind power become crucial to utilities that must balance the load and supply of electricity. As storage of wind energy is not yet feasible on a large scale, the utility must integrate wind energy as soon as it is generated and decide at each balancing time‐step whether a change in conventional energy output is required. With high penetrations of wind energy, utilities must also plan for operating reserves to maintain stability of the electricity system when forecasts for renewable energy are inaccurate. Thus, a simple forecast of whether the wind power will increase, decrease or not change in the next time‐step will give utility operators an easy tool for assessing whether changes need to be made to the current generation mix. In this work, Markov chain models based on the change in power output at up to three locations or lags in time are presented that not only produce such an hourly forecast but also include a measure of the uncertainty of the forecast. Forecasts are greatly improved when knowledge of whether the maximum or minimum wind power is currently being produced and the intrahour trend in wind power are incorporated. These models are trained, tested and evaluated with a uniquely long set of 2 years of 10 min measurements at four meteorological stations in the Pacific Northwest and perform better than a benchmark state‐of‐the‐art wind speed forecasting model.Copyright © 2013 John Wiley & Sons, Ltd.     

降水数值预报和水文预报耦合效果定量评价方法

基于信息熵的基本原理,改造了信息熵函数,针对系统耦合中的诸多不确定性因素,提出了一种耦合效果的定量评估方法,利用自信息量评估各节点完成预报工作的能力,选取降水数值预报信息的获取能力、降水数值预报信息的处理能力、预报系统的调用能力和系统耦合预报成果的存储与输出能力描述工作能力,并采用数理统计方法确定各节点内不确定性因素的隶属度。实例应用结果表明,该方法能定量估算由于预见期的延长对水文预报耦合成果不确定性的影响,为耦合成果的精度评定和应用提供了客观的评价。     

Characteristics of day‐ahead wind power forecast errors in Nordic countries and benefits of aggregation

The growing proportion of wind power in the Nordic power system increases day‐ahead forecasting errors, which have a link to the rising need for balancing power. However, having a large interconnected synchronous power system has its benefits, because it enables to aggregate imbalances from large geographical areas. In this paper, day‐ahead forecast errors from four Nordic countries and the impacts of wind power plant dispersion on forecast errors in areas of different sizes are studied. The forecast accuracy in different regions depends on the amount of the total wind power capacity in the region, how dispersed the capacity is and the forecast model applied. Further, there is a saturation effect involved, after which the reduction in the relative forecast error is not very large anymore. The correlations of day‐ahead forecast errors between areas decline rapidly when the distance increases. All error statistics show a strong decreasing trend up to the area sizes of 50,000 km². The average mean absolute error (MAE) in different regions is 5.7% of installed capacity. However, MAE of a smaller area can be over 8% of the capacity, but when all the Nordic regions are aggregated together, the capacity‐normalized MAE decreases to 2.5%. The average of the largest errors for different regions is 39.8% and when looking at the largest forecast errors for smaller areas, the largest errors can exceed 80% of the installed capacity, whereas at the Nordic level, the maximum forecast error is only 13.5% of the installed capacity. Copyright © 2016 John Wiley & Sons, Ltd.     

Spatio‐temporal analysis and modeling of short‐term wind power forecast errors

Forecasts of wind power production are increasingly being used in various management tasks. So far, such forecasts and related uncertainty information have usually been generated individually for a given site of interest (either a wind farm or a group of wind farms), without properly accounting for the spatio‐temporal dependencies observed in the wind generation field. However, it is intuitively expected that, owing to the inertia of meteorological forecasting systems, a forecast error made at a given point in space and time will be related to forecast errors at other points in space in the following period. The existence of such underlying correlation patterns is demonstrated and analyzed in this paper, considering the case‐study of western Denmark. The effects of prevailing wind speed and direction on autocorrelation and cross‐correlation patterns are thoroughly described. For a flat terrain region of small size like western Denmark, significant correlation between the various zones is observed for time delays up to 5 h. Wind direction is shown to play a crucial role, while the effect of wind speed is more complex. Nonlinear models permitting capture of the interdependence structure of wind power forecast errors are proposed, and their ability to mimic this structure is discussed. The best performing model is shown to explain 54% of the variations of the forecast errors observed for the individual forecasts used today. Even though focus is on 1‐h‐ahead forecast errors and on western Denmark only, the methodology proposed may be similarly tested on the cases of further look‐ahead times, larger areas, or more complex topographies. Such generalization may not be straightforward. While the results presented here comprise a first step only, the revealed error propagation principles may be seen as a basis for future related work. Copyright © 2010 John Wiley & Sons, Ltd.     

Development of a geophysic model output statistics module for improving short‐term numerical wind predictions over complex sites

Developed for short‐term (0–48 h) wind power forecasting purposes, high‐resolution meteorological forecasts for Eastern Canada are available from Environment Canada's Numerical Weather Prediction (NWP) model configured on a limited area (GEM‐LAM). This paper uses 3 years of forecast data from this model for the region of North Cape (Prince Edward Island, Canada). Although the model resolution is relatively high (2.5 km), statistical analysis and site inspection reveal that the model does not have a sufficiently refined grid to properly represent the meteorological phenomena over this complex coastal site. To cope with such representation error, a generalized Geophysic Model Output Statistics (GMOS) module is developed and applied to reduce the forecast error of the NWP forecasts. GMOS differs from other Model Output Statistics (MOS) that are widely used by meteorological centres in the following aspects: (i) GMOS takes into account the surrounding geophysical parameters such as surface roughness, terrain height, etc., along with wind direction; (ii) GMOS can be directly applied for model output correction without any training. Compared with other methods, GMOS using a multiple grid point approach improves the GEM‐LAM predictions root mean squared error by 1–5% for all time horizons and most meteorological conditions. Also, the topographic signature of the forecast error (uneven directional distribution of the forecast error related to the surface characteristics) due to misrepresentation issues is significantly reduced. The NWP forecasts combined with GMOS outperform the persistence model from a 2 h horizon, instead of 3 h using MOS. Finally, GMOS is applied and validated at two other sites located in New Brunswick, Canada. Similar improvements on the forecasts were observed, thus showing the general applicability of GMOS. Copyright © 2012 John Wiley & Sons, Ltd.     

基于深度学习的西南地区寿溪河山洪预报研究

鉴于山洪突发性强、历时短、陡涨陡落等致使在模拟预报过程中具有较大难度和不确定性问题,构建了基于深度学习的LSTM网络模型进行山洪确定性预报和概率预报,从精度和可靠度两方面研究其在西南山区的适用性.并以西南山洪易发区寿溪河流域为例进行模拟,结果显示LSTM网络模型更易发现暴雨洪水之间的深层规律,验证期平均纳什效率系数达0...     

Adaptive post‐processing of short‐term wind forecasts for energy applications

We present a new method of reducing the error in predicted wind speed, thus enabling better management of wind energy facilities. A numerical weather prediction model, COSMO, was used to produce 48 h forecast data every day in 2008 at horizontal resolutions of 10 and 3 km. A new adaptive statistical method was applied to the model output to improve the forecast skill. The method applied corrective weights to a set of forecasts generated using several post‐processing methods. The weights were calculated based on the recent skill of the different forecasts. The resulting forecast data were compared with observed data, and skill scores were calculated to allow comparison between different post‐processing methods. The total root mean square error performance of the composite forecast is superior to that of any of the individual methods. Copyright © 2010 John Wiley & Sons, Ltd.     

灰色GM(1,1)和神经网络组合的能源预测模型

能源消费预测是制定能源规划的重要组成部分。鉴于能源消费系统复杂性和非线性的特征，文章结合某省能源消费的历史数据，首先用灰色预测和神经网络建立了单项预测模型．并对单项预测模型的优缺点进行了分析，然后采用最优组合权重的方法进行优化组合，从而获得更为精确的预测模型和预测值。实例的预测结果表明该模型可以作为能源消费预测的有效工具。     

Regime‐switching models and multiple causal factors in forecasting wind speed

This study evaluates two types of models for wind speed forecasting. The first is models with multiple causal factors, such as offsite readings of wind speed and meteorological variables. These can be estimated using either regressions or neural networks. The second is state transition and the closely related class of regime‐switching transition models. These are attractive in that they can be used to predict outlying fluctuations or large ramp events. The regime‐switching model uses a persistence forecast during periods of high wind speed, and regressions for low and intermediate speeds. These techniques are tested on three databases. Two main criteria are used to evaluate the outcomes, the number of high and low states than can be predicted correctly and the mean absolute percent error of the forecast. Neural nets are found to predict the state transitions somewhat better than logistic regressions, although the regressions do not do badly. Three methods all achieve about the same degree of forecast accuracy: multivariate regressions, state transition and regime‐switching models. If the states could be predicted perfectly, the regime‐switching model would improve forecast accuracy by an additional 2.5 to 3 percentage points. Analysis of the density functions of wind speed and the forecasting models finds that the regime‐switching method more closely approximates the distribution of the actual data. Copyright © 2009 John Wiley & Sons, Ltd.     

数据化模型模块技术在洪水预报建模中的应用

针对洪水预报模型的传统开发模式中存在重复开发、模型应用不灵活、扩展性差等问题,依据洪水预报模型的结构与逻辑特性,将模型拆分为多个相对独立的模块,并运用数据化模型模块技术对这些模块进行封装,形成洪水预报模型组件库;利用洪水预报模型组件库搭建洪水预报模型,将其应用于嵊州流域预报中。嵊州流域的应用实践表明,采用数据化模型模块技术搭建的洪水预报模型具有代码重用率高、移植性强、可扩展性好、易于维护等优点,同时为洪水多模型集合预报提供了便利,丰富了洪水预报结果。     

A new reference for wind power forecasting

In recent years some research towards developing forecasting models for wind power or energy has been carried out. In order to evaluate the prediction ability of these models, the forecasts are usually compared with those of the persistence forecast model. As shown in this article, however, it is not reasonable to use the persistence model when the forecast length is more than a few hours. Instead, a new statistical reference for predicting wind power, which basically is a weighting between the persistence and the mean of the power, is proposed. This reference forecast model is adequate for all forecast lengths and, like the persistence model, requires only measured time series as input. Copyright © 1998 John Wiley & Sons, Ltd.     

An economic impact metric for evaluating wave height forecasters for offshore wind maintenance access

This paper demonstrates that wave height forecasters chosen on statistical quality metrics result in sub‐optimal decision support for offshore wind farm maintenance. Offshore access is constrained by wave height, but the majority of approaches to evaluating the effectiveness of a wave height forecaster utilize overall accuracy or error rates. This paper introduces a new metric more appropriate to the wind industry, which considers the economic impact of an incorrect forecast above or below critical wave height boundaries. The paper describes a process for constructing a value criterion where the implications between forecasting error and economic consequences are explicated in terms of opportunity costs and realized maintenance costs. A comparison between nine forecasting techniques for modeling and predicting wave heights based on historical data, including an ensemble aggregator, is described demonstrating that the performance ranking of forecasters is sensitive to the evaluation criteria. The results highlight the importance of appropriate metrics for wave height prediction specific to the wind industry and the limitations of current models that minimize a metric that does not support decision‐making. With improved ability to forecast weather windows, maintenance scheduling is subject to less uncertainty, hence reducing costs related to vessel dispatch, and lost energy because of downtime. Copyright © 2015 John Wiley & Sons, Ltd.     

基于模糊回归的电价预测

利用模糊隶属函数建立了复合回归模型；在复合回归模型基础上，建立了模糊非线性回归模型；给出了求非对称模糊分布的上下边界的线性规划方法；实例应用验证了方法的正确性。     

基于小波-ANFIS的水库月径流预报模型

根据径流变化特性,提出一种基于小波-ANFIS的水库月径流组合预报模型.利用Mallat算法对月径流序列进行多尺度分解,得到对应尺度下的低频信号和高频信号,分别对这两种信号建立了ANFIS模型进行预报,将各模型预报结果叠加作为原径流的预报值.该模型用于淮河支流沙河上游年月径流变化幅度较大的昭平台水库月径流预报中,结果表明所建模型能够较好地预报原始信号的趋势,预报精度比单一ANFIS 预报模型有较大改善,但仍有待提高.对导致这一现象的主要原因进行了分析,并对模型的改进提出了合理化建议.     

Implementation of a Model Output Statistics based on meteorological variable screening for short‐term wind power forecast

A combination of physical and statistical treatments to post‐process numerical weather predictions (NWP) outputs is needed for successful short‐term wind power forecasts. One of the most promising and effective approaches for statistical treatment is the Model Output Statistics (MOS) technique. In this study, a MOS based on multiple linear regression is proposed: the model screens the most relevant NWP forecast variables and selects the best predictors to fit a regression equation that minimizes the forecast errors, utilizing wind farm power output measurements as input. The performance of the method is evaluated in two wind farms, located in different topographical areas and with different NWP grid spacing. Because of the high seasonal variability of NWP forecasts, it was considered appropriate to implement monthly stratified MOS. In both wind farms, the first predictors were always wind speeds (at different heights) or friction velocity. When friction velocity is the first predictor, the proposed MOS forecasts resulted to be highly dependent on the friction velocity–wind speed correlation. Negligible improvements were encountered when including more than two predictors in the regression equation. The proposed MOS performed well in both wind farms, and its forecasts compare positively with an actual operative model in use at Risø DTU and other MOS types, showing minimum BIAS and improving NWP power forecast of around 15% in terms of root mean square error. Further improvements could be obtained by the implementation of a more refined MOS stratification, e.g. fitting specific equations in different synoptic situations. Copyright © 2012 John Wiley & Sons, Ltd.     

基于支持向量机的水电站中长期径流组合预报

中长期径流预报是充分利用水资源、实现水电站优化运行的重要环节。以金沙江中游龙盘电站为研究对象,分别采用自回归滑动平均模型、最近邻抽样回归模型、BP神经网络建立了该水电站月径流预报模型,在分析三种模型预报结果具有一定互补性的基础上进一步建立了支持向量机分月组合预报模型。统计结果表明,与单一预报模型相比,该组合预报模型具有更高的精度和稳定性,为寻求水电站径流预报规律和制定中长期调度计划提供了技术支持。     

From probabilistic forecasts to statistical scenarios of short‐term wind power production

Short‐term (up to 2–3 days ahead) probabilistic forecasts of wind power provide forecast users with highly valuable information on the uncertainty of expected wind generation. Whatever the type of these probabilistic forecasts, they are produced on a per horizon basis, and hence do not inform on the development of the forecast uncertainty through forecast series. However, this additional information may be paramount for a large class of time‐dependent and multistage decision‐making problems, e.g. optimal operation of combined wind‐storage systems or multiple‐market trading with different gate closures. This issue is addressed here by describing a method that permits the generation of statistical scenarios of short‐term wind generation that accounts for both the interdependence structure of prediction errors and the predictive distributions of wind power production. The method is based on the conversion of series of prediction errors to a multivariate Gaussian random variable, the interdependence structure of which can then be summarized by a unique covariance matrix. Such matrix is recursively estimated in order to accommodate long‐term variations in the prediction error characteristics. The quality and interest of the methodology are demonstrated with an application to the test case of a multi‐MW wind farm over a period of more than 2 years. Copyright © 2008 John Wiley & Sons, Ltd.     

Lifetime and economic analyses of lithium‐ion batteries for balancing wind power forecast error

Wind power plant operators are often faced with extra charges when their power production does not match the forecasted power. Because the accuracy of wind power forecasts is limited, the use of energy storage systems is an attractive alternative even when large‐scale aggregation of wind power is considered. In this paper, the economic feasibility of lithium‐ion batteries for balancing the wind power forecast error is analysed. In order to perform a reliable assessment, an ageing model of lithium‐ion battery was developed considering both cycling and calendar life. The economic analysis considers two different energy management strategies for the storage systems and it is performed for the Danish market. Analyses have shown that the price of the Li‐ion BESS needs to decrease by 6.7 times in order to obtain a positive net present value considering the present prices on the Danish energy market. Moreover, it was found that for total elimination of the wind power forecast error, it is required to have a 25‐MWh Li‐ion battery energy storage system for the considered 2 MW WT. Copyright © 2015 John Wiley & Sons, Ltd.