A generalized regression model based on hybrid empirical mode decomposition and support vector regression with back-propagation neural network for mid-short-term load forecasting

Since load forecasting plays a decisive role in the safe and stable operation of power systems, it is particularly important to explore forecasting methods accurately. In this article, the hybrid empirical mode decomposition (EMD) and support vector regression (SVR) with back-propagation neural network (BPNN), namely the EMDHR-SVR-BPNN model, is proposed. Information theory is mainly used to solve the data tendency problem, and the EMD method is used to solve the data volatility problem. There is no interaction between these two methods; thus these two models can complement each other through generalized regression of orthogonal decomposition. Taking the load data from the New South Wales (NSW, Australia) market as an example, the obtained simulation results are compared with other models. It is concluded that the proposed EMDHR-SVR-BPNN model not only improves the forecasting accuracy but also has good fitting ability. It can reflect the changing tendency of data in a timely manner, providing a strong basis for the electricity generation of the power sector in the future, thus reducing electricity waste. The proposed EMDHR-SVR-BPNN model has potential for employment in mid-short term load forecasting.     

A robust neural network filter for electricity demand prediction

This paper is concerned with one-day-ahead hourly predictions of electricity demand for Puget Power, a local electricity utility for the Seattle area. Standard modelling techniques, including neural networks, will fail when the assumptions of the model are violated. It is demonstrated that typical modelling assumptions such as no outliers or level shifts are incorrect for electric power demand time series. A filter which removes or lessens the significance of outliers and level shifts is demonstrated. This filter produces ‘clean data’ which is used as the basis for future robust predictions. The robust predictions are shown to be better than non-robust counterparts on electricity load data. The outliers identified by the filter are shown to correspond with suspicious data. Finally, the estimated level shifts are in agreement with the belief that load growth is taking place year to year.     

基于GSM的施工升降机超载保护远程监测系统设计

设计了一种施工升降机中超载保护的远程监测系统。该系统由重量传感器采集重量信号，经过加速度传感器修正采集值后输入单片机处理，以实现自动在线无线监测、报警和断电保护。并利用GSM模块进行远程监测报警。通过现场试验，得出该超载保护系统能有效避免动载荷误报警的问题，具有稳定性好、管理方便等特点。     

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.     

牵引网宽频域谐波与振荡抑制技术应用研究

针对大功率、高密度、多种类的机车群在小容量牵引供电网中带来的宽频域谐波和谐振问题,从交流机车的拓扑和调制出发对宽频域谐波产生的机理进行了分析,并结合对两种典型车型,对不同类型机车混跑线路的谐波分布多样性进行了分析.为此提出了基于H桥级联的有源电力滤波器（HCAPF）和高通滤波器（HPF）相结合的谐波抑制方案,利用HCAPF对无功功率进行控制并对低次谐波进行抑制,利用高通滤波器对高次进行吸收.结合示范工程,对参数设计、HCAPF性能测试进行了详细介绍,最后对整个补偿系统投入前、后24 h的电压畸变率等参数进行对比测试,结果表明补偿后电压总畸变率平均值从4.324%下降到2.934%,满足国标,其中各次谐波电压都有较大下降.同时电网电压的最大波动率从14.5%下降到10.7%.     

Linking series generated at different frequencies

This is a report on our studies of the systematical use of mixed‐frequency datasets. We suggest that the use of high‐frequency data in forecasting economic aggregates can increase the accuracy of forecasts. The best way of using this information is to build a single model that relates the data of all frequencies, for example, an ARMA model with missing observations. As an application of linking series generated at different frequencies, we show that the use of a monthly industrial production index improves the predictability of the quarterly GNP. Copyright © 2008 John Wiley & Sons, Ltd.     

Forecasting market potential and market penetration of residential water heater load control programs

This paper presents a model which estimates market potential and forecasts market penetration for one demand-side management (DSM) programwater heater load controlin the service territory of Virginia Power Corporation, a large electric utility in the south-eastern United States. Water heater load control is a voluntary program where customers are paid a monthly incentive to allow the utility to shut off power to their electric water heaters during periods of peak demand. Reducing the level of peak demand through DSM programs is one way for utilities to avoid building new power plants. The current total energy (or demand) impact due to a load control program is the sum of the changes in energy (or demand) for all program participants. The projected energy and demand impact due to a load control program is the average change per participant multiplied by the number of participants or adopters of the program. While it is reasonably straightforward to measure the energy savings resulting from shutting off power to a water heater, the more difficult task for planning purposes is forecasting the number of customers who will actually join the program (i.e. the market penetration) for a given incentive. The customer decision process is divided into three stages: eligibility, awareness, and adoption. The responsiveness of market penetration to changes in advertising and incentive amounts is demonstrated. In addition, the impact of changing advertising and incentive amounts on the percentage of aware customers who adopt the program and on that of eligible customers who become aware of the program is estimated. This model can be used by utility planners and managers to forecast the market penetration of both new and existing load control programs. In addition, it can be employed to estimate the impact of various promotion and marketing schemes on both market potential and market penetration.     

Does Disagreement Amongst Forecasters Have Predictive Value?

In the present study we examine the predictive power of disagreement amongst forecasters. In our empirical work, we find that in some situations this variable can signal upcoming structural and temporal changes in an economic process and in the predictive power of the survey forecasts. We examine a variety of macroeconomic variables, and we use different measurements for the degree of disagreement, together with measures for location of the survey data and autoregressive components. Forecasts from simple linear models and forecasts from Markov regime‐switching models with constant and with time‐varying transition probabilities are constructed in real time and compared on forecast accuracy. Copyright © 2015 John Wiley & Sons, Ltd.     

一种基于扩展有限状态机的自动化测试用例生成方法

扩展有限状态机(EFSM)是使用最广泛的测试模型之一.由于不可行路径的存在,运用EFSM模型生成测试用例仍然是个难题.本文提出了一种基于EFSM模型的自动化测试用例生成方法 (ATGEM).为解决不可行路径问题,首先提出一种基于数据流分析的路径可行性度量方法来预测路径的可行性,以尽可能避开不可行路径,提高测试用例自动化生成的效率.然后通过建立动态可执行模型来获取运行时反馈信息作为搜索算法的适应度函数(fitness function),实现测试数据和预言信息的自动生成.该方法结合静态分析和动态分析技术生成一个较优可行路径子集和对应测试用例来达到指定的覆盖准则,能够应用于多种数据类型的测试用例生成,适用范围较广.通过实验在多个EFSM模型上验证了ATGEM方法中测试用例生成和路径可行性度量方法的有效性,实验结果表明,利用路径可行性度量方法可以大幅度提高测试用例生成效率,与现有方法相比,ATGEM中的测试用例生成方法具有更高的效率.     

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。     

Forecasting temporally aggregated vector ARMA processes

If interest centres on forecasting a temporally aggregated multiple time series and the generation process of the disaggregate series is a known vector ARMA (autoregressive moving average) process then forecasting the disaggregate series and temporally aggregating the forecasts is at least as efficient, under a mean squared error measure, as forecasting the aggregated series directly. Necessary and sufficient conditions for equality of the two forecasts are given. In practice the data generation process is usually unknown and has to be determined from the available data. Using asymptotic theory it is shown that also in this case aggregated forecasts from the disaggregate process will usually be superior to forecasts obtained from the aggregated process.     

一种基于自体集层次聚类的否定选择算法

否定选择算法是用于产生人工免疫检测器的重要算法,然而传统的否定选择过程需要将随机生成的候选检测器与全部自体数据进行匹配以排除识别了自体的无效检测器,该匹配过程导致检测器的生成效率过低,极大地限制了免疫算法的应用.为此,文中提出了一种基于自体集层次聚类的否定选择算法CB-RNSA.算法首先对自体数据进行层次聚类预处理,然后用聚类中心取代自体数据点与候选检测器进行匹配,以减少距离计算代价.在生成检测器的过程中,候选检测器被限定在非自体空间的低覆盖率区域内,以降低检测器冗余.对检测器的非自体空间覆盖率进行了概率分析,给出了中止生成检测器的条件,该条件较传统的预设检测器数量的中止条件更为合理.理论分析表明CB-RNSA的时间复杂度与自体集规模无关,从而解决了经典的否定选择算法的时间复杂度随自体数量呈指数增长这一难题,极大地提高了大自体样本空间下的检测器生成效率.对比实验结果表明:在相同的实验数据集与期望覆盖率下,CB-RNSA的检测率比经典的RNSA与V-detector算法分别提高了12.3%与7.4%,误警率分别降低了8.5%与4.9%,产生检测器的时间代价分别降低了67.6%和75.7%.     

On Selecting a Power Transformation in Time-Series Analysis

The primary aim of this paper is to select an appropriate power transformation when we use ARMA models for a given time series. We propose a Bayesian procedure for estimating the power transformation as well as other parameters in time series models. The posterior distributions of interest are obtained utilizing the Gibbs sampler, a Markov Chain Monte Carlo (MCMC) method. The proposed methodology is illustrated with two real data sets. The performance of the proposed procedure is compared with other competing procedures. © 1997 John Wiley & Sons, Ltd.     

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.     

Discharge pattern analysis suggests existence of a low-threshold calcium channel in cold receptors

Summary The regular periodic activity patterns of mammalian cold receptors have been quantitatively studied. Analysis of the timing of either single impulses or impulse groups demonstrated that the periodic receptor process is maintained independently of impulse generation and continues to operate under conditions when afferent impulses are not initiated. These results imply that the underlying conductances must be operational at threshold potentials related to impulse generation. In addition to temperature, the periodic process is considerably sensitive to calcium, which affects mainly the probability of impulse generation during each cycle. Reduction of external calcium and application of calcium entry blockers with relative selectivity for low-threshold calcium channels are similarly effective in modulating cold receptor activity. The data imply the existence of a low-threshold calcium conductance at the sensory terminal.     

智能电网基本理念阐释

由于可再生能源开发利用的快速发展,世界各国面临着可再生能源如何接入及充分利用等一系列问题,而智能电网则是应对这些挑战的有效战略措施.智能电网具有强大的功能、显著的综合效益和广阔的发展前景,是世界电网发展的趋势.但是智能电网不是一个单纯的技术问题,它涉及许多重要的基本理念.厘清这些理念对于科学高效地实施智能电网、对于技术创新和产业发展至关重要.为此本文从智能电网的特点、智能电网的总体设想和技术内涵、与智能电网相关的技术、智能电网的近期与远期目标以及在实施智能电网时需要注意的事项等方面阐释了智能电网的基本理念.     

基于粗糙集与灰色动态预测模型在电力负荷短期预测的应用

针对电力系统多因素负荷预测问题的复杂性,结合粗糙集理论与GM（1,N）模型各自的优势,提出一种基于粗糙集理论的GM（1,N）预测模型.采取粗糙集理论对影响负荷预测因素进行简约,利用GM（1,N）建立简约后的因素变量和负荷之间的关系建立模型,并与GM（1,1）预测模型进行了比较,结果反映基于粗糙集理论的GM（1,N）预测模型的优越性,精准度达到94.055%.     

Discharge pattern analysis suggests existence of a low-threshold calcium channel in cold receptors.

The regular periodic activity patterns of mammalian cold receptors have been quantitatively studied. Analysis of the timing of either single impulses or impulse groups demonstrated that the periodic receptor process is maintained independently of impulse generation and continues to operate under conditions when afferent impulses are not initiated. These results imply that the underlying conductances must be operational at threshold potentials related to impulse generation. In addition to temperature, the periodic process is considerably sensitive to calcium, which affects mainly the probability of impulse generation during each cycle. Reduction of external calcium and application of calcium entry blockers with relative selectivity for low-threshold calcium channels are similarly effective in modulating cold receptor activity. The data imply the existence of a low-threshold calcium conductance at the sensory terminal.     

基于DSP和CPLD的电力数据远程通讯系统设计

本文根据电力数据的特点，提出了一种新的电力数据压缩处理方案。首先对电力数据进行预处理，然后再用自适应Huffman编码对实时采集的电力数据进行压缩。通过对比测试结果，本文给出了运用此处理方案的效果仿真图。最后提出了基于DSP和CPLD的电力数据远程通讯的设计方案，该方案简单易行，适合电力系统，为本算法的移植打下了基础。     

燃煤发电与水泥生产联产技术可行性研究

本文基于解决我国以燃煤发电为主的电力工业的粉煤灰与SO2等酸性气体排放等难题的目的，从优化能源利用和洁净煤技术的角度出发，提出了燃煤发电与水泥生产联产技术。从原料代替、水泥煅烧工艺与掺杂热效应等方面和一系列实验证明了该联产技术的可行性，并就其前景作了分析。燃煤发电和水泥生产的合二为一，将会在我国的能源节约、环境保护与社会经济效益等方面产生深远而重大的意义，有效促进我国电力工业和水泥工业可持续发展。