<span style="font-family:Verdana;font-size:12px;">The Federal Office for Economic Affairs and Export Control (BAFA) of</span><span style="font-family:Verdana;font-size:12px;"> Ger...<span style="font-family:Verdana;font-size:12px;">The Federal Office for Economic Affairs and Export Control (BAFA) of</span><span style="font-family:Verdana;font-size:12px;"> Germany promotes digital concepts for increasing energy efficiency as part of the “Pilotprogramm Einsparz<span style="white-space:nowrap;">ä</span>hler”. Within this program, Limón GmbH is developing software solutions in cooperation with the University of Kassel to identify efficiency potentials in load profiles by means of automated anomaly detection. Therefore, in this study two strategies for anomaly detection in load profiles are evaluated. To estimate the monthly load profile, strategy 1 uses the artificial neural network LSTM (Long Short-Term Memory), with a data period of one month (1</span><span style="font-family:'';font-size:10pt;"> </span><span style="font-family:Verdana;font-size:12px;">M) or three months (3</span><span style="font-family:'';font-size:10pt;"> </span><span style="font-family:'';font-size:10pt;"><span style="font-size:12px;font-family:Verdana;">M), and strategy 2 uses the smoothing method PEWMA (Probalistic Exponential Weighted Moving Average). By comparing with original load profile data, residuals or summed residuals of the sequence lengths of two, four, six and eight hours are identified as an anomaly by exceeding a predefined threshold. The thresholds are defined by the Z-Score test, </span><i><span style="font-size:12px;font-family:Verdana;">i</span></i><span style="font-size:12px;font-family:Verdana;">.</span><i><span style="font-size:12px;font-family:Verdana;">e</span></i><span style="font-size:12px;font-family:Verdana;">., residuals greater than 2, 2.5 or 3 standard deviations are considered anomalous. Furthermore, the ESD (Extreme Studentized Deviate) test is used to set thresholds by means of three significance level values of 0.05, 0.10 and 0.15, with a maximum of </span><i><span style="font-size:12px;font-family:Verdana;">k</span></i><span style="font-size:12px;font-family:Verdana;"> = 40 it展开更多
As the energy transition is upon us,the replacement of combustion engines by electrical ones will imply a greater stress on the electrical grid of different countries.Therefore,it is of paramount importance to simulat...As the energy transition is upon us,the replacement of combustion engines by electrical ones will imply a greater stress on the electrical grid of different countries.Therefore,it is of paramount importance to simulate a great number of hypothetical multi-variant scenarios to correctly plan the roll-out of new grids.In this paper,we deploy Generative Adversarial Networks(GANs)to swiftly reproduce the non-Gaussian and multimodal distribution of real energy-related samples,making GANs a valuable tool for data generation in the field.In particular,we propose an original dataset deriving from the aggregation of two European providers including hourly electric inland generation from several European countries.This dataset also comes along with the corresponding season,day of the week,hour of the day and macro-economic variables aiming at unequivocally describing the country’s energetic profile.Finally,we evaluate the performance of our model via dedicated metrics capable of grasping the non-Gaussian nature of the data and compare it with the state-of-the-art model for tabular data generation.展开更多
In the evaluation of road roughness and its effects on vehicles response in terms of ride quality, loads induced on pavement, drivers＇ comfort, etc., it is very common to generate road profles based on the equation p...In the evaluation of road roughness and its effects on vehicles response in terms of ride quality, loads induced on pavement, drivers＇ comfort, etc., it is very common to generate road profles based on the equation provided by ISO 8608 standard, according to which it is possible to group road surface profiles into eight different classes. However, real profiles are significantly different from the artificial ones because of the non-stationary fea- ture of the first ones and the not full capability of the ISO 8608 equation to correctly describe the frequency content of real road profiles. In this paper, the international roughness index, the frequency-weighted vertical acceleration awz according to ISO 2631, and the dynamic load index are applied both on artificial and real profiles, highlighting the different results obtained. The analysis carried out in this work has highlighted some limitation of the ISO 8608 approach in the description of performance and conditions of real pavement profiles. Furthermore, the different sensitivity of the various indices to the fitted power spectral density parameters is shown, which should be taken into account when performing analysis using artificial profiles.展开更多
随着电力体制改革的不断深入,为争夺市场份额、吸引潜在用户购电并提高自身收益,售电公司愈发重视用户的用电体验。对用户日负荷曲线的聚类分析能够有效挖掘用户的用电行为特性,进而为售电公司提供决策依据。针对FCM算法运行时间较长、...随着电力体制改革的不断深入,为争夺市场份额、吸引潜在用户购电并提高自身收益,售电公司愈发重视用户的用电体验。对用户日负荷曲线的聚类分析能够有效挖掘用户的用电行为特性,进而为售电公司提供决策依据。针对FCM算法运行时间较长、对初始数据敏感、容易陷入局部最优、需要人为给定类簇数以及聚类结果不稳定等问题,提出了一种基于奇异值分解(Singular Value Decomposition,SVD)和改进FCM的日负荷聚类方法。首先对日负荷数据进行奇异值分解降维;然后,利用KNN和DPC算法形成初始类簇中心矩阵,并在FCM算法的迭代寻优过程中通过局部密度和邻近点对隶属度进行修正;最后,以某地区工商业用户日负荷曲线进行算例分析。结果表明,与传统聚类算法相比,该方法的聚类结果更准确、更稳定,运行速度更快。展开更多
文摘<span style="font-family:Verdana;font-size:12px;">The Federal Office for Economic Affairs and Export Control (BAFA) of</span><span style="font-family:Verdana;font-size:12px;"> Germany promotes digital concepts for increasing energy efficiency as part of the “Pilotprogramm Einsparz<span style="white-space:nowrap;">ä</span>hler”. Within this program, Limón GmbH is developing software solutions in cooperation with the University of Kassel to identify efficiency potentials in load profiles by means of automated anomaly detection. Therefore, in this study two strategies for anomaly detection in load profiles are evaluated. To estimate the monthly load profile, strategy 1 uses the artificial neural network LSTM (Long Short-Term Memory), with a data period of one month (1</span><span style="font-family:'';font-size:10pt;"> </span><span style="font-family:Verdana;font-size:12px;">M) or three months (3</span><span style="font-family:'';font-size:10pt;"> </span><span style="font-family:'';font-size:10pt;"><span style="font-size:12px;font-family:Verdana;">M), and strategy 2 uses the smoothing method PEWMA (Probalistic Exponential Weighted Moving Average). By comparing with original load profile data, residuals or summed residuals of the sequence lengths of two, four, six and eight hours are identified as an anomaly by exceeding a predefined threshold. The thresholds are defined by the Z-Score test, </span><i><span style="font-size:12px;font-family:Verdana;">i</span></i><span style="font-size:12px;font-family:Verdana;">.</span><i><span style="font-size:12px;font-family:Verdana;">e</span></i><span style="font-size:12px;font-family:Verdana;">., residuals greater than 2, 2.5 or 3 standard deviations are considered anomalous. Furthermore, the ESD (Extreme Studentized Deviate) test is used to set thresholds by means of three significance level values of 0.05, 0.10 and 0.15, with a maximum of </span><i><span style="font-size:12px;font-family:Verdana;">k</span></i><span style="font-size:12px;font-family:Verdana;"> = 40 it
文摘As the energy transition is upon us,the replacement of combustion engines by electrical ones will imply a greater stress on the electrical grid of different countries.Therefore,it is of paramount importance to simulate a great number of hypothetical multi-variant scenarios to correctly plan the roll-out of new grids.In this paper,we deploy Generative Adversarial Networks(GANs)to swiftly reproduce the non-Gaussian and multimodal distribution of real energy-related samples,making GANs a valuable tool for data generation in the field.In particular,we propose an original dataset deriving from the aggregation of two European providers including hourly electric inland generation from several European countries.This dataset also comes along with the corresponding season,day of the week,hour of the day and macro-economic variables aiming at unequivocally describing the country’s energetic profile.Finally,we evaluate the performance of our model via dedicated metrics capable of grasping the non-Gaussian nature of the data and compare it with the state-of-the-art model for tabular data generation.
文摘In the evaluation of road roughness and its effects on vehicles response in terms of ride quality, loads induced on pavement, drivers＇ comfort, etc., it is very common to generate road profles based on the equation provided by ISO 8608 standard, according to which it is possible to group road surface profiles into eight different classes. However, real profiles are significantly different from the artificial ones because of the non-stationary fea- ture of the first ones and the not full capability of the ISO 8608 equation to correctly describe the frequency content of real road profiles. In this paper, the international roughness index, the frequency-weighted vertical acceleration awz according to ISO 2631, and the dynamic load index are applied both on artificial and real profiles, highlighting the different results obtained. The analysis carried out in this work has highlighted some limitation of the ISO 8608 approach in the description of performance and conditions of real pavement profiles. Furthermore, the different sensitivity of the various indices to the fitted power spectral density parameters is shown, which should be taken into account when performing analysis using artificial profiles.
文摘随着电力体制改革的不断深入,为争夺市场份额、吸引潜在用户购电并提高自身收益,售电公司愈发重视用户的用电体验。对用户日负荷曲线的聚类分析能够有效挖掘用户的用电行为特性,进而为售电公司提供决策依据。针对FCM算法运行时间较长、对初始数据敏感、容易陷入局部最优、需要人为给定类簇数以及聚类结果不稳定等问题,提出了一种基于奇异值分解(Singular Value Decomposition,SVD)和改进FCM的日负荷聚类方法。首先对日负荷数据进行奇异值分解降维;然后,利用KNN和DPC算法形成初始类簇中心矩阵,并在FCM算法的迭代寻优过程中通过局部密度和邻近点对隶属度进行修正;最后,以某地区工商业用户日负荷曲线进行算例分析。结果表明,与传统聚类算法相比,该方法的聚类结果更准确、更稳定,运行速度更快。
