Automated prediction system of household energy consumption in cities using web crawler and optimized artificial intelligence

The supply of electrical energy is critical to convenient and comfortable living. However, people consume a large amount of energy, contributing to an energy crisis and global warming, and damaging some ecological cycles. Residential electricity consumption has greater elasticity than industrial and business consumption; it therefore has high energy-saving potential. This work establishes an automated platform, which provides information about residential electricity consumption in each city in Taiwan. Machine learning was used to forecast future residential electricity demand. A nature-inspired optimization method was applied to enhance the accuracy of the best machine learner, yielding an even better hybrid ensemble model. Performance measures indicate that the resulting model is accurate and provides effective information for reference. An automatic web-based system based on the model was combined with a web crawler and scheduled to run automatically to provide information on monthly residential electricity consumption in each county and city. By providing energy consumption information across the country, power providers and government can discuss policy and set different goals for energy use. The results of this study can facilitate the early implementation of energy-saving and carbon emission-reducing in cities and aid utility companies in establishing energy conservation guidelines.     

Enhanced structural,electromagnetic and absorption features of CoSm ferrite-metamaterial absorbers through synergistic effects

Currently, materials with outstanding absorption abilities, such as thin size, better absorbing power, and light weight are the need of industry to resolve the electromagnetic issues. However, the research on optimizing the composition of the material, microstructure and the structure of the absorber are also the important factors for enhancing the absorption features. A metamaterial microwave absorber (MMA) based on nano ferrites with desirable absorption peaks is proposed and simulated. Sol-gel auto combustion route is used to prepare the nanosized Sm doped Co ferrite with Co_1+xSm_xFe_2-2xO₄ at x = 0.00, 0.03, 0.06, 0.09, respectively. XRD, VSM, FESEM, and VNA were employed to evaluate the structural, magnetic, morphological, and dielectric features. Rietveld refinement of the XRD patterns of samples was evaluated. Refined parameters show the spinel phase's emergence and the Fe₂O₃ phase. Grain size and crystallite size were increased with Sm doping in Co ferrite. Electromagnetic studies depicted that the highest dielectric constant value was found at x = 0.09 and the minimum value at x = 0.03, respectively. Sm doped Co ferrite at x = 0.09 depicted high Q values at higher frequencies. The coercivity values first decreased and then increased. All samples exhibit variations in coercivity and magneto-crystalline anisotropy constant. This variation was attributed to the super-exchange interactions and strong LS coupling of the cations. The multiple absorption peaks are attained for TE-polarization, and the absorptivity is considerably improved for x = 0.09. The proposed absorber simulated from CST depicted the absorption peaks of the S-band and C-band of the microwave regime. The synergistic effects among the metamaterial and ferrite layers may enhance the absorption feature and would be useful for satellite communication applications.     

Early-warning performance monitoring system (EPMS) using the business information of a project

An early-warning performance monitoring system (EPMS) is proposed to objectively measure and monitor the performance of a project for early detection of inherent poor performance problems. The EPMS is built based on project progress data and consists of a database of business information, an optimized theoretical model used as a performance measurement baseline, and an index for monitoring and forecasting the performance. By monitoring the performance through an application of the EPMS to the Korean construction project, the quarterly variation of index was found to differ by project type. These results could explain the environmental changes in the project execution. Therefore, the EPMS is expected to be an alternative for objective performance monitoring and forecasting while applying the existing methods is difficult because of the limited available data on performance indicators. The development procedures may also be useful to researchers interested in approaches to quantitatively analyze trends in various industries.     

基于负荷预测的0.4 kV配网低电压风险评估方法

我国现阶段的配电网网架结构依然十分薄弱,智能化水平低,缺乏先进的检测技术和高效的运维模式。在现阶段低压电气信息不开源的情况下,为解决0.4 kV配网无差别运维效率低的问题,文章分析了适用于低压配网状态评估的评价维度,并基于负荷预测结果提出了低压台区低电压风险评估方法,引入了微增容量所引起的压降比,实现对低电压风险的定期管控。该文对于优化低压配电网运维资源配置、指导低压配网差异化运维模式的建立,以及提高低压配电网运维整治效率具有一定的参考价值。     

黄河什四份子弯道冰期水流及冰塞特征研究

覆冰条件下,弯道水流固有的三维特性更为复杂,对河流弯道冰下水流特征及冰塞的研究有助于推进防凌减灾工作。依据2020年1月稳封期的原型观测数据,对黄河什四份子弯道冰下水流及冰塞特征进行了详细分析。结果表明,弯顶节点工程引起的回流是阻冰的重要条件,上游下潜的冰花因水动力不足及河势等因素影响发生堆积,冰塞在弯内形成并沿凹岸主槽逐渐向上游发展,同时,弯顶卡冰封河与下游较大的水流流速,促进了清沟的形成;入弯至弯顶区间,冰下水流流速垂线分布基本服从双对数分布规律;受凹岸冰塞及回流影响,纵向上,水流流速呈减小趋势,在弯顶附近开始恢复;径向上,河道主流被冰塞压迫并逐渐趋于凸岸,致使弯道主流易位,凸岸水流流速大于凹岸,清沟向凸岸偏移;弯道水流湍动能与雷诺应力主要在近底处存在较大的变幅,表明近底水流运动活跃,且二者大小与流速大小在径向与纵向分布上具有较好的一致性,受冰盖影响,水流湍动能沿水深近似呈"S"形分布,雷诺应力分布无明显规律。     

Accuracy of wind energy forecasts in Great Britain and prospects for improvement

The metric representing the wind energy forecast error, when reported as a percent, is calculated quite differently than the error metrics for electricity transmission, electricity load, or in other industries such as manufacturing when they are also reported as a percent. The resulting calculated metric is quite different from what would be reported if the method utilized elsewhere was employed. This paper examines the possible forecast assessment and operational challenges associated with this finding. Concerning the prospects for improvement, the errors reported in MW of energy have a systematic component. With this insight, we developed a model to improve accuracy.     

基于需求预测的PaaS平台资源分配方法

针对缺乏PaaS平台下资源需求的有效预测与优化分配的问题，提出一种资源需求预测模型和分配方法。首先，根据PaaS平台中应用对资源需求的周期性来对资源序列进行切分，并在短期预测的基础上结合应用的多周期性特征，利用多元回归算法建立综合的预测模型。然后，基于MapReduce架构设计实现了一个Master-Slave模式的PaaS平台资源分配系统。最后，结合当前任务请求和资源需求预测结果进行资源分配。实验结果表明，采用该资源需求预测模型和分配方法后，相比于自回归模型和指数平滑算法，平均绝对百分比误差分别下降8.71个百分点和2.07个百分点，均方根误差分别下降2.01个百分点和0.46个百分点。所提预测模型的预测结果不仅误差小，与真实值的拟合程度也较高，而且利用较小的时间开销就可以获得较高的准确度。此外，使用该预测模型的PaaS平台的资源请求的平均等待时间有了明显的下降。     

Matching of technological forecasting technique to a technology using fuzzy multi-attribute decision-making methods: Case study from the aerospace industry

Given the accelerating pace of technological advances and environmental changes, technology-based companies are required to predict and understand future events in their environments. However, there is a wide range of forecasting methods creating confusion on which method to use. This paper demonstrates the selection of an appropriate technique for technology forecasting in the Iran Aviation Industries Organization (IAIO). To this end, a review of the literature was first reviewed to extract the proper criteria for selecting a forecasting method. Next, the SWARA and fuzzy MUTLIMOORA methods were used to evaluate and prioritize a total of twelve forecasting methods proposed for the case study. The results suggested that the Delphi method for technology forecasting in the IAIO. Scenario writing and the relevance tree are the next proper alternatives that can be used.     

Flow regime recognition in a long pipeline-riser system based on signals at the top of the riser

To identify conveniently multiphase flow regimes in subsea pipeline-risers, we study in this paper experimentally two-phase flows in a 1657 m long pipeline with an S-shaped riser to simulate field experiment, within a wide range of gas and liquid velocities. Three flow regimes, namely severe slugging, transitional flows, and stable flows, are analyzed based on three differential pressure and one pressure signals at the top of the riser; comparatively speaking, the positions of these signals in the experimental system are similar to those of the sea level signals in industrial fields, which are easy and less expensive to obtain. The obtained signals are decomposed into six scales via a multi-scale wavelet analysis, and further four statistical parameters on each scale are extracted, including mean values, standard deviations, ranges, and mean values of absolute. We compared the effects of six SVM classifiers with different kernel functions on the recognition rate of flow regimes, and it is found the recognition rates of SVM classifier with quadratic and cubic kernel functions are the highest. Further, the principal component analysis is employed to reduce the dimension of statistical parameters and it indicates that the recognition rate tends to increase with the rising number of principal components from 1 to 6, and it remains constant if the principal component number is further increased. Moreover, The results suggest that the recognition rate obtained from the pressure difference between the top of the riser and the separator peaks, and then it comes that from the pressure signal at the top of the riser, and that for the pressure difference signal at the top of the riser is the least satisfying one. As for the optimal differential pressure signals between the top of the riser and the separator, the results show that the recognition rate increases rapidly from 70.2% to 90.4% when the sample duration rising from 2.3 s to 18.6 s, and when the sample duration exceeds 74.4 s, the recognition rate exceeds 92.9% and remains unchanged.     

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%。