Multi-Step Clustering of Smart Meters Time Series:Application to Demand Flexibility Characterization of SME Customers

Customer segmentation according to load-shape profiles using smart meter data is an increasingly important application to vital the planning and operation of energy systems and to enable citizens’participation in the energy transition.This study proposes an innovative multi-step clustering procedure to segment customers based on load-shape patterns at the daily and intra-daily time horizons.Smart meter data is split between daily and hourly normalized time series to assess monthly,weekly,daily,and hourly seasonality patterns separately.The dimensionality reduction implicit in the splitting allows a direct approach to clustering raw daily energy time series data.The intraday clustering procedure sequentially identifies representative hourly day-unit profiles for each customer and the entire population.For the first time,a step function approach is applied to reduce time series dimensionality.Customer attributes embedded in surveys are employed to build external clustering validation metrics using Cramer’s V correlation factors and to identify statistically significant determinants of load-shape in energy usage.In addition,a time series features engineering approach is used to extract 16 relevant demand flexibility indicators that characterize customers and corresponding clusters along four different axes:available Energy(E),Temporal patterns(T),Consistency(C),and Variability(V).The methodology is implemented on a real-world electricity consumption dataset of 325 Small and Medium-sized Enterprise(SME)customers,identifying 4 daily and 6 hourly easy-to-interpret,well-defined clusters.The application of the methodology includes selecting key parameters via grid search and a thorough comparison of clustering distances and methods to ensure the robustness of the results.Further research can test the scalability of the methodology to larger datasets from various customer segments(households and large commercial)and locations with different weather and socioeconomic conditions.

Intelligent Energy Utilization Analysis Using IUA-SMD Model Based Optimization Technique for Smart Metering Data

Smart metering has gained considerable attention as a research focus due to its reliability and energy-efficient nature compared to traditional electromechanical metering systems. Existing methods primarily focus on data management,rather than emphasizing efficiency. Accurate prediction of electricity consumption is crucial for enabling intelligent grid operations,including resource planning and demandsupply balancing. Smart metering solutions offer users the benefits of effectively interpreting their energy utilization and optimizing costs. Motivated by this,this paper presents an Intelligent Energy Utilization Analysis using Smart Metering Data(IUA-SMD)model to determine energy consumption patterns. The proposed IUA-SMD model comprises three major processes:data Pre-processing,feature extraction,and classification,with parameter optimization. We employ the extreme learning machine(ELM)based classification approach within the IUA-SMD model to derive optimal energy utilization labels. Additionally,we apply the shell game optimization(SGO)algorithm to enhance the classification efficiency of the ELM by optimizing its parameters. The effectiveness of the IUA-SMD model is evaluated using an extensive dataset of smart metering data,and the results are analyzed in terms of accuracy and mean square error(MSE). The proposed model demonstrates superior performance,achieving a maximum accuracy of65.917% and a minimum MSE of0.096. These results highlight the potential of the IUA-SMD model for enabling efficient energy utilization through intelligent analysis of smart metering data.

Functional Confirmation Using a Medical X-Ray System of a Semiconductor Survey Meter

In recent years, semiconductor survey meters have been developed and are in increasing demand worldwide. This study determined if it is possible to use the X-ray system installed in each medical facility to calculate the time constant of a semiconductor survey meter and confirm the meter’s function. An additional filter was attached to the medical X-ray system to satisfy the standards of N-60 to N-120, more copper plates were added as needed, and the first and second half-value layers were calculated to enable comparisons of the facility’s X-ray system quality with the N-60 to N-120 quality values. Next, we used a medical X-ray system to measure the leakage dose and calculate the time constant of the survey meter. The functionality of the meter was then checked and compared with the energy characteristics of the meter. The experimental results showed that it was possible to use a medical X-ray system to reproduce the N-60 to N-120 radiation quality values and to calculate the time constant from the measured results, assuming actual leakage dosimetry for that radiation quality. We also found that the calibration factor was equivalent to that of the energy characteristics of the survey meter.

Detection of Water Meter Digits Based on Improved Faster R-CNN

In order to more accurately detect the accuracy of word-wheel water meter digits, 2000 water meter pictures were produced, and an improved Faster-RCNN algorithm for detecting water meter digits was proposed. The improved Faster-RCNN algorithm uses ResNet50 combined with FPN (Feature Pyramid Network) structure instead of the original ResNet50 as the feature extraction network, which can enhance the accuracy of the model for small-sized digit recognition;the use of ROI Align instead of ROI Pooling can eliminate the error caused by the quantization process of the ROI Pooling twice, so that the candidate region is more accurately mapped to the feature map, and the accuracy of the model is further enhanced. The experiment proves that the improved Faster-RCNN algorithm can reach 91.8% recognition accuracy on the test set of homemade dataset, which meets the accuracy requirements of automatic meter reading technology for water meter digital recognition, which is of great significance for solving the problem of automatic meter reading of mechanical water meters and promoting the intelligent development of water meters.

Controlling Congestion in Al-Seeb Highway Using Ramp Metering (Case Study)

This paper represents a case study of traffic congestion within a section on Al Seeb Street highway due to the on-ramp merging of vehicles that causes a bottleneck in the mainline road. It studies the efficiency of installing ramp metering within a ramp within the selected study zone. This is done by simulating the collected data using Vissim software by drawing three one-hour-long scenarios;the first scenario reflects the data collected for 30 minutes duration and is used as a base scenario to draw the other two scenarios, which are reflected as factored-up scenarios to create a situation observed in the early morning in the study zone at 6:00-7:00 in which slowing down of speeds exist, and breakdown is raised in working days. The two factoring-up scenarios were as follows: one without ramp metering and the other without ramp metering. Each scenario was calibrated and run five times with random seeds, and then the average was considered. The simulation examines the ability of RM to smooth traffic in mainline and reduce queuing on on-ramp roads within the selected study zone by comparing the performance of the network for the scenarios and comparing them in terms of the overall delays, number of stops and the average speeds for the vehicles within the mainline. The results showed a good performance reflected by the scenario with ramp metering with a reduction of the overall delay, a decrease in stops number and an increase of the average speed were achieved. For the base scenario, a visualization (video extracted from Vissim software) was extracted, showing no need to install RM with an associated table showing a number of stops equal to zero with an average speed of 102.74 km/h and a total delay of 6045 seconds. For the second scenario with no RM, a visualization was extracted showing a slowing down of speeds for vehicles within the mainline while vehicles merging from the on-ramp and need to be controlled with a table showing a number of stops equal to 16 and an average speed equal to 58 km/h and a total delay of 916,874 seconds. For the third scenario with RM, a visualization was extracted showing good control of the second scenario with a table showing the number of stops equal to 6, an average speed equal to 61 km/h and a total delay equal to 484,466 seconds. Ten literatures in regard to this study have been reviewed. The data collected are quantitative, which are collected using an indirect manual counting method and then the data is used to feed the software for simulation.

光伏新能源电能信号游程域模态分解理论与特征提取

高比例光伏新能源接入下的电能信号呈现出强随机、快时变、大波动特性,常导致电能表电能计量严重超差,影响电能交易的公正合理性。针对新能源电能信号敏感特征不清楚、对电能表电能计量影响不明确的问题,建立了游程域映射理论,解决了电能信号在游程域的表征问题;提出了游程域模态分解(R-EMD)方法,基于游程完成电流幅度在长持续时间下局部区间的自适应模态分解,得出光伏新能源电能信号的2个全局重要特征;构建了游程域特征参量与特征函数,并提取得到光伏新能源电能信号的4个游程域敏感特征;通过实验验证了游程域敏感特征对电能计量的影响。

基于数据驱动与相关性的电能误差分析方法研究

针对传统方法中有限负载点(load point)检定方式导致的电能表负载点检测数量过于弱化、部分负载点误差无法全面代表全量程计量特性的问题,提出将运行误差作为负载点误差可靠来源的补充策略。在电力系统运行误差持续积累的基础上生成电能表全量程误差数据,将处理后的全量程误差时间序列和时效性等关键特征进行划分,通过比较多个采集周期的皮尔逊相关系数矩阵来识别变点,建立不依赖于具体用电环境、仅依靠电能表误差本征分量的数据驱动型误差相关性预测模型。实证分析表明所提方法可以动态分析电能表误差在安全范围内的波动状态、挖掘电能表误差阶段性特征中的潜在高风险信息,以实现台区内电能表误差的长期趋势预测,对于具备时变特征的电能表误差评估及电能表轮换分析都具有非常重要的意义。

Cardy Meter速测仪快速测定土壤和植株NO_3^-和K^+含量的研究

采用Cardy Meter便携式养分快速诊断仪对土壤以及植物叶片中的NO3-和K+的含量进行了速测,并与目前实验室常规的测定方法进行了分析比较。结果表明:采用Cardy Meter仪测定的NO3-和K+含量与采用连续流动分析仪测定的NO3-以及火焰光度计测定的K+均呈现高度的相关性,说明Cardy Meter系列可以作为田间氮、钾的快速营养诊断仪器在实际生产中应用。

一种区分服务的新型meter算法

本文分析了 Diff Serv模型中提出的两种 meter算法 ,指出了令牌桶算法作为 meter算法所固有的缺陷 .在此基础上 ,提出了一种基于 Diff Serv模型的新型 meter算法 .该算法操作简单 ,与初始状态无关 ,并且很好地解决了权重因子的自适应取值问题 .因此它既能真实地反映 IP流的时间特性 ,又能容忍 IP流的短时间内的突发性 ,因而很适于作为Diff Serv模型中的 m eter算法 .

苜蓿小区育种气吸式排种器设计与试验

为提高苜蓿小区育种播种机械化水平,设计了一种苜蓿小区育种气吸式排种器。首先,阐述了排种器的结构和工作原理;接着,对排种器的主要结构参数进行理论计算,并分析排种器充种过程力学特性,找出影响排种效果的关键因素。为得到排种器的最佳性能参数,进行二次回归正交试验设计,以排种盘转速、工作压强、吸种孔数量为试验因素,以合格率、重播率、漏播率为性能指标,采用Design-Expert软件对试验数据进行回归方程分析和响应曲面分析,获得影响试验指标的各试验因素主次关系。研究结果表明:在排种器的最佳参数,排种盘转速24.2 r/min、吸种孔数量36个、工作压强3.7 kPa时,合格率92.81%,重播率3.35%,漏播率3.42%。验证试验表明:该排种器可以实现有效排种,可为后续研究提供理论基础,具有参考价值。

欧盟高级计量架构OPEN meter标准体系介绍

在远程抄表系统框架基础上介绍了OPEN meter标准体系,分析了OPEN meter标准的推出目的、系统框架和接口框架,并着重介绍了集中器和电表之间的接口MI1-CI1的模型,给出OPEN meter工程在框架基础上做出的测试报告结果。最后对未来OPEN meter的发展前景做出了展望。

Development of geo-electrical meter based on networking

Further development of earthquake equipments is closely associated with that of computer technology. Because Embedded PC104 module has the equivalent functions of PC,it has been widely used in recent years,and can provide a new and flexible hardware design environment,but its applications in observation instruments of earth-quake precursor are rare. The present paper introduces in detail the realization of a networked geo-electrical meter by applying the low price,high reliability embedded PC104 industrial computer.

基于时空网络的电动汽车充电桩电能表误差估计方法研究

智能电表的性能通常会随时间下降,现场检定需要大量人力物力,因此提出了一种基于高速卷积神经网络和双向长短期记忆网络相结合的误差估计方法。首先,针对智能电表采集到的充电设施的数据特性进行预处理,其次基于加入高速网络的卷积模块提取变量间的空间特征,保留一部分原始信息,再将提取的特征输入双向长短期记忆网络中捕捉时序特征,得到更精确的相对误差。最后在某地的新能源汽车充电站的数据集中进行验证,对比现有的先进模型,实验结果表明文中所提方法在充电桩电能表相对误差估计方面有更高的准确性,设计的三种性能评价指标至少有13.68%以上的提升。

投弃式电磁海流计测试系统设计与实现

针对我国投弃式电磁海流计自主研发亟需打破测试瓶颈的现状,设计实现陆上流速测试系统并开展室内模拟实验。首先,将投弃式探头在海水中的运动状态分解为水平方向的自旋转运动和垂直方向的自下落运动,研制探头自旋转运动辅助装置及自下落测量模拟水槽。其次,基于自研装置,分别设计实现水槽中的自旋转和自下落流速测试陆上实验系统。由测试结果可知,在24mV以上的模拟场源激励下,自旋转实验系统能够实现海流电场信号的高线性度测试,决定系数达到0.9916;在模拟5.14 cm/s的海流电场信号测量中,自下落实验系统采集电压相对误差为7%,能够满足测量精度。该实验系统能够有效弥补海洋投弃式设备室内测试方法的不足,并为海洋传感器的陆上测试提供新的思路和方案。

基于机器学习的100m高度风速短期预报订正方法

提出一种基于机器学习的先格点订正区域误差、后站点订正局地误差的100 m高度风速短期预报订正方法。首先,基于100 m高度风速格点实况资料,采用深度学习算法对数值天气预报进行格点订正;然后,基于测风塔100 m高度风速观测资料,采用随机森林集成预报订正方法对站点风速预报进行订正;最后,选用内蒙古中部某站点进行分析验证。结果表明,订正前未来84小时风速预报均方根误差(RMSE)为4.25 m/s,格点订正后RMSE降至3.16~3.79 m/s,站点集成订正后RMSE降至2.81 m/s。格点订正后预报误差明显降低,站点集成订正后误差进一步减小。

基于循环一致对抗学习的段码液晶仪表读数识别方法

为解决自然场景中仪表图像受复杂光照、背景及其他随机干扰所造成的读数识别难题,提出一种基于循环一致对抗学习的段码液晶仪表读数识别方法。该方法使用M-CRAFT作为字符检测网络,获取段码读数区域和段码字符位置,通过后处理算法分割得到段码字符子图像,然后以循环一致对抗域自适应网络CYCADA为基础,增加感知损失和重构损失并设计分类器,构建了一种改进的字符背景分离与识别网络I-CYCADA,用于将具有复杂背景的段码字符图像转换为清晰直观的二值图像,从而使读数识别变得更为简单和准确。为了验证该方法的有效性,构建了由多种复杂场景下的液晶段码仪表图像组成的数据集。实验结果表明,I-CYCADA可与不同CNN分类网络结合使用,均能提升转换后的段码字符图像的识别准确率,本文方法在自建数据集上的字符级识别准确率达95.39%,完整读数识别准确率达86.65%,有效改善了对困难样本的识别效果,且轻量级设计可满足实时性要求。

基于运算放大器的Meter-Bus总线的主站接口电路的研究

基于运算放大器、比较器等芯片功能及电路基本理论的研究,结合Meter-Bus总线标准的相关要求,本文介绍了一种具备负载驱动能力补偿、负载过载报警等功能的Meter-Bus总线的主站接口电路。该Meter-Bus总线的主站接口电路主要基于德州仪器(TI)公司的OPA564AIDWPR运算放大器原理实现主站数据的发送功能,利用LM393比较器的特性实现了数据的接收及负载过载的报警功能,同时结合运算放大器TLE2021的功能及三极管的特性实现了Meter-BUS总线的驱动能力补偿功能。该Meter-Bus总线的主站接口电路解决了目前的Meter-BUS总线的主站电路通信误码率高,通信质量差以及电路稳定性差,功能不齐全,适用范围窄的技术问题。而且电路符合MeterBus总线的相关标准,特别适合在各种自动抄表系统中使用。

超声波流量计的压电换能器动态性能一致性研究

超声流量计的关键组成部分是压电换能器,其性能指标决定流量计的计量准确性和稳定性。针对没有关于换能器匹配性技术指标的规范与标准这一问题,提出一种基于动态时间规整的换能器一致性评估方法,并进行快速配对研究。该方法通过回波信号时间序列的规整路径距离,进行换能器动态性能一致性评估,可以高效地实现换能器动态特性分析。水流量测量实验结果表明:通过动态时间规整算法计算得出的规整路径距离越小,配对的换能器一致性越高,零流量时的顺逆流传播时间差最大可减小到0.3 ns,零流量读数小于0.01 m/s,小流量重复性优于0.2%,满足1.5级精度标准,验证了该方法的可行性,满足了实际工程应用的需要。

Mu-Meter MK6在路面抗滑性能检测中的应用研究

为分析Mu-Meter MK6路面摩擦系数测试系统在路面抗滑性能评价中的应用,首先研究了Mu-Meter MK6路面摩擦系数测试系统的结构组成和工作原理,结合工程实践给出了使用过程中的注意事项,并通过试验研究和结果分析,找出了影响摩擦系数测试结果的有关主要因素以及部分因素的影响程度。且通过试验证明,虽然Mu-Meter系列摩擦系数测试仪和横向力系数测试车(SCRIM)的测试指标均属于横向力摩擦系数,指标性质相同,但由于结构原理的差异,并不能以SFC值的温度和速度修正来直接修正mu值。

Pinguin Audio Meter在调频广播监测中的应用

Pinguin Audio Meter作为一款音频分析软件,将高分辨率数字电平表、声波频谱分析仪、相位及立体声示波表相结合,在声音、音乐制作方面得到广泛应用,其很容易应用到调频广播信号的监测中。就该软件应用于调频广播信号监测的可行性进行了阐述,搭建实验平台,构建了基于Pinguin Audio Me-ter分析软件的监测系统。实验结果表明该软件可以对调频广播信号进行适时监测,并具有结构简单、功能丰富、投资较少的特点。