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.     

A new method for extrapolating the load-displacement response of drilled shaft foundations for interpretation of side capacity

In this study, the side capacity of drilled shaft foundations is estimated from partially-mobilized load–displacement field data using a new method in the extrapolation of load–displacement response. A dataset of 138 bi-directional load tests is used to evaluate the degree of mobilization of unit side resistance. A total of 612 unit side-resistance curves obtained from measured strain gauge recordings are utilized in this study. The proposed extrapolation approach is based on a new technique, the Double Tangent method, characterizing the extent of mobilization for each unit side-resistance curve. Roughly, 12% of the dataset exhibits a fully-mobilized load–displacement response, with the remainder exhibiting varying degrees of a partially-mobilized response. Fully-mobilized records are further characterized using the Double Tangent method over different ranges of mobilization, resulting in four regression models based on predominant soil types. Each model is assessed statistically, and a global regression model is found suitable to predict maximum unit side resistance. The global model is further validated using two independent load test datasets, comparing measured values of unit side resistance against predicted values. The model is then used to predict maximum unit side resistance for all partially-mobilized data within the dataset, and the results are compared to two extrapolation techniques currently used in practice. The corresponding resistance-displacement response is extrapolated using a proposed asymptotic curve-fitting function for side resistance, and an example extrapolation is illustrated to showcase how the proposed method can be used in engineering practice.     

Notwendigkeit und Vermarktung von Flexibilitätsoptionen im Energiesystem der Zukunft

Demands on production, distribution and consumption of electrical energy change fundamentally with the energy revolution. Energy purchasing costs for inflexible consumers are rising and proceeds of inflexible producers are sinking. Companies are able to reduce costs by marketing operational flexibility options. This article sets out the need of flexibility in the energy system and the new marketing options. Flexibility potentials within infrastructure plants of a chemical park are analyzed, evaluated regarding marketing and activated in the presented research project FlexChemistry.     

Bearing capacity of horizontally layered geosynthetic reinforced stone columns

In very soft soils, the bearing capacity of stone columns may not improve significantly due to very low confinement of the surrounding soil. Therefore, they may be reinforced with geosynthetics by using vertical encasement or horizontal layers. Very limited studies exist on horizontally reinforced stone columns (HRSCs). In this research, some large body laboratory tests have been performed on horizontally reinforced stone columns with diameters of 60, 80, and 100?mm and groups of stone columns with 60?mm diameter. Results show that the bearing capacity of stone columns increases by using horizontally reinforcing layers. Also, they reduce lateral bulging of stone columns by their frictional and interlocking effects with stone column aggregates. Finally, numerical analyses were carried out to study main affecting parameters on the bearing capacity of HRSCs. Numerical analysis results show that the bearing capacity increases considerably with increasing the number of horizontal layers and decreasing space between layers.     

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。     

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。     

Structural load alleviation using distributed delay shaper: Application to flexible aircraft

Lightweight flexible aircraft suffers from unwanted oscillatory vibrations during aircraft manoeuvres. A recently developed distributed-delay signal (DZV) shaper is therefore proposed to be applied as a feedforward controller to alleviate the manoeuvre loads, as an alternative to traditional structural filters used routinely in this context. Structural filters are essentially linear low-pass filters with bandwidth below the significant flexible modes, applied to control signals generated either by the pilot’s direct input or by the flight control system. It has been showed that if instead a properly tuned signal shaper is used, better performance can be achieved: first, the target modes are significantly attenuated while the responsiveness of the aircraft is less compromised and secondly, the oscillatory nature of the vibrations are reduced. The high fidelity simulation results on a full scaled dynamic model of a highly flexible blended wing–body (BWB) aircraft show that in comparison to traditional structural filters, signal shapers significantly reduce the wing root loading (forces and moments) which provides potential structural benefits.