基于小波阀值降噪和BP神经网络的超短期风电功率预测

针对超短期风电功率预测问题，考虑了风电场复杂的噪声背景和风电功率的波动性，提出了一种基于小波阀值降噪-BP神经网络的超短期风电功率预测方法。该方法采用近似对称光滑的紧支撑双正交小波db4（Daubechies函数）作为小波基，通过多分辨分析的Mallat算法对历史时序风电功率数据进行3尺度分解。根据Donoho阀值法对各层小波系数进行软阀值降噪处理，再通过小波逆变换重构历史时序风电功率，由BP神经网络对其进行训练，预测目的风电功率序列。仿真算例将该方法与普通BP神经网络方法进行了对比，比较结果证明其预测精度优于后者，具有很好鲁棒性和降噪性能，适用噪声复杂的风电场超短期风电功率在赣预测．     

Power grid operation optimization and forecasting using a combined forecasting system

Reliable photovoltaic and wind power generation forecasts are essential for efficient power systems operations. A combined forecasting system is developed, which integrates a data preprocessing method, a sub-predictor selection rule, and a multi-objective optimization to integrate various forecasting models. The proposed system effectively aggregates the advantages of all algorithms involved, facilitating greater prediction precision and stability. Experiments indicated that the proposed system can achieve higher quality point and interval forecasting performance relative to the comparative approaches.     

基于混沌-RBF神经网络的风电场风速预测

在利用风速时间序列具有混沌特性的前提下，将相空间重构和RBF神经网络结合的混合算法用于风电场风速预测。通过实例仿真计算对比表明，该混沌-RBF神经网络的混合算法可以进一步提高预测准确度。     

Adaptive modelling and forecasting of offshore wind power fluctuations with Markov‐switching autoregressive models

Wind power production data at temporal resolutions of a few minutes exhibit successive periods with fluctuations of various dynamic nature and magnitude, which cannot be explained (so far) by the evolution of some explanatory variable. Our proposal is to capture this regime‐switching behaviour with an approach relying on Markov‐switching autoregressive (MSAR) models. An appropriate parameterization of the model coefficients is introduced, along with an adaptive estimation method allowing accommodation of long‐term variations in the process characteristics. The objective criterion to be recursively optimized is based on penalized maximum likelihood, with exponential forgetting of past observations. MSAR models are then employed for one‐step‐ahead point forecasting of 10 min resolution time series of wind power at two large offshore wind farms. They are favourably compared against persistence and autoregressive models. It is finally shown that the main interest of MSAR models lies in their ability to generate interval/density forecasts of significantly higher skill. Copyright © 2010 John Wiley & Sons, Ltd.     

Prediction of α‐stable GARCH and ARMA‐GARCH‐M models

The best prediction of generalized autoregressive conditional heteroskedasticity (GARCH) models with α‐stable innovations, α‐stable power‐GARCH models and autoregressive moving average (ARMA) models with GARCH in mean effects (ARMA‐GARCH‐M) are proposed. We present a sufficient condition for stationarity of α‐stable GARCH models. The prediction methods are easy to implement in practice. The proposed prediction methods are applied for predicting future values of the daily SP500 stock market and wind speed data.     

Probabilistic Forecasts of Wind Power Generation by Stochastic Differential Equation Models

The increasing penetration of wind power has resulted in larger shares of volatile sources of supply in power systems worldwide. In order to operate such systems efficiently, methods for reliable probabilistic forecasts of future wind power production are essential. It is well known that the conditional density of wind power production is highly dependent on the level of predicted wind power and prediction horizon. This paper describes a new approach for wind power forecasting based on logistic‐type stochastic differential equations (SDEs). The SDE formulation allows us to calculate both state‐dependent conditional uncertainties as well as correlation structures. Model estimation is performed by maximizing the likelihood of a multidimensional random vector while accounting for the correlation structure defined by the SDE formulation. We use non‐parametric modelling to explore conditional correlation structures, and skewness of the predictive distributions as a function of explanatory variables. Copyright © 2015 John Wiley & Sons, Ltd.     

Short term load forecasting in electric power systems: A comparison of ARMA models and extended wiener filtering

On-line prediction of electric load in the buses of the EHV grid of a power generation and transmission system is basic information required by on-line procedures for centralized advanced dispatching of power generation. This paper presents two alternative approaches to on-line short term forecasting of the residual component of the load obtained after the removal of the base load from a time series of total load. The first approach involves the use of stochastic ARMA models with time-varying coefficients. The second consists in the use of an extension of Wiener filtering due to Zadeh and Ragazzini. Real data representing a load process measured in an area of Northern Italy and simulated data reproducing a non-stationary process with known characteristics constitute the basis of a numerical comparison allowing one to determine under which conditions each method is more appropriate.     

基于一元线性回归由风速传感器测量值推导巷道平均风速

通过试验数据建立了风速传感器的测量值与平均风速之间的一元线性回归方程，并对拟合效果进行了评价。对参数的判断表明该方程是有效的，可以近似地把风速传感器的测量值转换成平均风速值，并在相同的巷道长度、相同的风流速度、不同断面下用Comsol模拟了风速流场分布情况。     

集聚效应对中国风电产业发展差异影响的实证研究

风电产业的发展是清洁能源发展的热点之一。本文从产业集聚效应的角度出发,深入研究了我国风电产业链的发展情况,利用地区集中度LQ指标对2009~2012年华北、东北、西北和江苏沿海风电产业集聚情况进行了实证研究,得出我国四大区域确实存在风电产业集聚现象。同时利用产业地区集中度cR指标测度各地的市场集中度,深入分析了我国典型地区的风电产业集聚特征,得出受集聚效应和各地经济资源禀赋不同的影响,我国风电产业发展存在差异且缺少地区间的产业联动发展,并提出相应的对策建议。     

基于改进变步长电导增量法的光伏阵列最大功率点跟踪

针对目前变步长最大功率跟踪在复杂光照条件下的振荡问题，结合二分法查找原理，提出了一种改进的变步长电导增量法。采用boost电路作为阻抗匹配装置，此电路占空比大小与系统输出功率相对应。最大功率点附近的工作点对应的占空比。是一组有序数据。对该组数据使用二分法查找，搜寻到最大功率点所对应的占空比，以实现最大功率的跟踪。仿真结果表明，提出的算法能够有效地抑制变步长跟踪过程中的振荡。与常见的变步长算法相比，改进算法更易实现，且具有更强的适应性。     

列车通过大跨度桥梁桥塔区域过程中的风-车-桥耦合动力分析

大跨度桥梁位于强横风环境时,桥塔的遮挡作用会使列车进出桥塔区域时车体受到的风力发生变化,这种风荷载的突变效应会对车辆的动力响应造成影响,此外车辆动力响应的改变还会影响桥梁的动力响应.本文以瓯江大跨公铁两用悬索桥为工程背景,以快速谱分析模拟风场,对桥梁子系统施加抖振风力,对桥梁子系统施加稳态风力,考虑桥塔宽度与车辆长度的关系,建立风-车-桥耦合系统运动平衡方程,以全过程迭代法求解该方程,分析计算了不同车速与风速下桥塔遮风效应对车辆桥梁动力响应的影响.结果表明风速和桥塔宽度越大,车辆的动力响应越大,而桥梁动力响应受桥塔遮风效应影响很小.     

风力发电冷却技术

随着风力发电机单机容量的逐步增大，发电机内各部件的散热量也将大大增加，如何有效解决发电机的温升瓶颈，已成为风力发电机进一步发展的关键问题之一。本文首先对风力发电的原理与结构进行了综述，分析了风力发电机运行过程中热量产生的部件和原因，介绍了目前风力发电机组所采用的冷却技术，并在此基础上对下一代大功率风力发电机冷却技术进行了展望。     

Timescale classification in wind forecasting: A review of the state-of-the-art

The intermittency of the wind has been reported to present significant challenges to power and grid systems, which intensifies with increasing penetration levels. Accurate wind forecasting can mitigate these challenges and help in integrating more wind power into the grid. A range of studies have presented algorithms to forecast the wind in terms of wind speeds and wind power generation across different timescales. However, the classification of timescales varies significantly across the different studies (2010–2014). The timescale is important in specifying which methodology to use when, as well in uniting future research, data requirements, etc. This study proposes a generic statement on how to classify the timescales, and further presents different applications of these forecasts across the entire wind power value chain.     

Identifying US business cycle regimes using dynamic factors and neural network models

We use dynamic factors and neural network models to identify current and past states (instead of future) of the US business cycle. In the first step, we reduce noise in data by using a moving average filter. Dynamic factors are then extracted from a large-scale data set consisted of more than 100 variables. In the last step, these dynamic factors are fed into the neural network model for predicting business cycle regimes. We show that our proposed method follows US business cycle regimes quite accurately in-sample and out-of-sample without taking account of the historical data availability. Our results also indicate that noise reduction is an important step for business cycle prediction. Furthermore, using pseudo real time and vintage data, we show that our neural network model identifies turning points quite accurately and very quickly in real time.     

基于FLUENT的NA63A系列翼型气动特性对比分析

利用计算流体力学软件FLUENT6．3．2对在风力发电领域应用较为广泛的NA63A系列翼型的外部流场进行数值模拟，采用Spalar-S．Allmaras湍流计算模型求解该系列翼型在不同攻角下的压力、速度分布。进而对NA63A系列不同翼型在相同攻角下以及网种翼型在不同攻角下的气动特性进行对比分析并总结规律，为该类翼型的性能研究提供了一定的理论依据。     

A semiparametric method for predicting bankruptcy

Bankruptcy prediction methods based on a semiparametric logit model are proposed for simple random (prospective) and case–control (choice‐based; retrospective) data. The unknown parameters and prediction probabilities in the model are estimated by the local likelihood approach, and the resulting estimators are analyzed through their asymptotic biases and variances. The semiparametric bankruptcy prediction methods using these two types of data are shown to be essentially equivalent. Thus our proposed prediction model can be directly applied to data sampled from the two important designs. One real data example and simulations confirm that our prediction method is more powerful than alternatives, in the sense of yielding smaller out‐of‐sample error rates. Copyright © 2007 John Wiley & Sons, Ltd.     

Predicting multistage financial distress: Reflections on sampling,feature and model selection criteria

This research proposes a prediction model of multistage financial distress (MSFD) after considering contextual and methodological issues regarding sampling, feature and model selection criteria. Financial distress is defined as a three‐stage process showing different nature and intensity of financial problems. It is argued that applied definition of distress is independent of legal framework and its predictability would provide more practical solutions. The final sample is selected after industry adjustments and oversampling the data. A wrapper subset data mining approach is applied to extract the most relevant features from financial statement and stock market indicators. An ensemble approach using a combination of DTNB (decision table and naïve base hybrid model), LMT (logistic model tree) and A2DE (alternative N dependence estimator) Bayesian models is used to develop the final prediction model. The performance of all the models is evaluated using a 10‐fold cross‐validation method. Results showed that the proposed model predicted MSFD with 84.06% accuracy. This accuracy increased to 89.57% when a 33.33% cut‐off value was considered. Hence the proposed model is accurate and reliable to identify the true nature and intensity of financial problems regardless of the contextual legal framework.     

多时间步长结冰数值模拟方法研究

时间步长是影响飞机结冰数值模批精度的重要参数之一。本文通过求解雷诺时均N-S方程，湍流模型采用k-ε两方程模型，获得空气流场，求解水滴运动轨迹方程获得水滴撞击特性，基于Messinger热力学模型求解能量和质量守恒方程计算冰形。并采用扇形分区法，更新机翼前缘结冰区网格，保持网格拓扑结构不变，实现多时间步长结冰数值计算。比较了采用单时间步长法争多时间步长法对翼型表面结冰增长数值模拟的计算结果，并与冰风洞试验数据及LEWICE预测数据进行对比。在此基础上，计算分析了不同时间步长对部件表面的结冰冰形的影响。结果表明，只有采用多时间步长法进行飞机结冰数值模拟方是有效的，并且存在一个合适的时间步长，既满足计算精度要求，又能提高计算效率。     

Forecasting load-duration curves

A new method is proposed for forecasting electricity load-duration curves. The approach first forecasts the load curve and then uses the resulting predictive densities to forecast the load-duration curve. A virtue of this procedure is that both load curves and load-duration curves can be predicted using the same model, and confidence intervals can be generated for both predictions. The procedure is applied to the problem of predicting New Zealand electricity consumption. A structural time-series model is used to forecast the load curve based on half-hourly data. The model is tailored to handle effects such as daylight savings, holidays and weekends, as well as trend, annual, weekly and daily cycles. Time-series methods, including Kalman filtering, smoothing and prediction, are used to fit the model and to achieve the desired forecasts of the load-duration curve.     

Forecasting stock prices using a hierarchical Bayesian approach

The Ohlson model is evaluated using quarterly data from stocks in the Dow Jones Index. A hierarchical Bayesian approach is developed to simultaneously estimate the unknown coefficients in the time series regression model for each company by pooling information across firms. Both estimation and prediction are carried out by the Markov chain Monte Carlo (MCMC) method. Our empirical results show that our forecast based on the hierarchical Bayes method is generally adequate for future prediction, and improves upon the classical method. Copyright © 2005 John Wiley & Sons, Ltd.