Data quality of electricity consumption data in a smart grid environment

Chen, Wen; Zhou, Kaile; Yang, Shanlin; Wei, Cheng

doi:10.1016/j.rser.2016.10.054

Cited by 80 publications

(38 citation statements)

References 21 publications

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“…We will assume that ignition of combustible material on an electrical network because of damage of electrical wiring happens in case of coincidence in space and time of the following three processes: there was a damage of an electrical network which is energized; the system of protection refused actuating; there was a combustible material near the place of damage of an electrical network [6][7][8].…”

Section: Problem Definitionmentioning

confidence: 99%

“…Having got to this state, the system can't leave it. The being able e 8 (1,1,1) occurs ignition of combustible material.…”

Section: Assessment Of Safe Operationmentioning

confidence: 99%

“…Using a matrix (8) and system of the algebraic equations (9), we will write down system of the equations for finding of τ 1 in a look: 1 0 0 0 1 1 2 3 1 1 0 0 0 1 2 3 2 2 1 0 0 0 1 3 2 3 3 1 0 0 0 1 4 3 4 1 2 1 0 0 0 0 5 3 1 5 1 0 0 0 0 6 3 2 6 1 0 0 0 0 7 2 1 …”

Section: Pij(δt)=p[ae(t+δt)=ej/ae(t)=ej]=p{ej→ej}=1-λijδt+0(δt) (3)mentioning

confidence: 99%

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Probability estimation of ignitions formation in the electrical network

2017

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Abstract. Their fire safety is one of the major requirements, which is imposed to electric networks. However extensiveness of methods of her assessment and a different of approaches to definition of factors of danger leads to complication of the choice of a technique and process of an assessment. The work purpose is development of a uniform method of mathematical modelling and an assessment of safe operation of electric networks.

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Section: Problem Definitionmentioning

confidence: 99%

“…Having got to this state, the system can't leave it. The being able e 8 (1,1,1) occurs ignition of combustible material.…”

Section: Assessment Of Safe Operationmentioning

confidence: 99%

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Probability estimation of ignitions formation in the electrical network

2017

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“…Notably, to guarantee reliable and useful information from the collected data, the quality of raw data should be considered prior to refinement of raw data. Here, it is noted that the quality of raw data is measured by the amount of missing values in raw data [3,4]. However, while storing the incoming measurement data in a database, missing values easily can occur due to harsh working conditions or uncontrollable factors, such as malfunctions of devices and imperfect communication signals.…”

Section: Introductionmentioning

confidence: 99%

Learning-Based Adaptive Imputation Methodwith kNN Algorithm for Missing Power Data

et al. 2017

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This paper proposes a learning-based adaptive imputation method (LAI) for imputing missing power data in an energy system. This method estimates the missing power data by using the pattern that appears in the collected data. Here, in order to capture the patterns from past power data, we newly model a feature vector by using past data and its variations. The proposed LAI then learns the optimal length of the feature vector and the optimal historical length, which are significant hyper parameters of the proposed method, by utilizing intentional missing data. Based on a weighted distance between feature vectors representing a missing situation and past situation, missing power data are estimated by referring to the k most similar past situations in the optimal historical length. We further extend the proposed LAI to alleviate the effect of unexpected variation in power data and refer to this new approach as the extended LAI method (eLAI). The eLAI selects a method between linear interpolation (LI) and the proposed LAI to improve accuracy under unexpected variations. Finally, from a simulation under various energy consumption profiles, we verify that the proposed eLAI achieves about a 74% reduction of the average imputation error in an energy system, compared to the existing imputation methods.

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“…, (6) ve(7)'de görüldüğü gibi bir model, bir olayın gerçekleşme olasılığının gerçekleşmeme olasılığına bölünmesinin ardından doğal logaritmasının alınması ile oluşmaktadır[12].Logit (p) = log [p/(1-p)] = ln [p/(1-p)(5)Logit [p(x)] = log [p(x)/1-p(x)] = a + b1x1 + b2x2 + … + bixi (6) P = exp ( a + b1x1 + b2x2 + … + bixi ) / 1+exp ( a + b1x1 + b2x2 + … + bixi)…”

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Veri Madenciliği Algoritmaları Kullanarak Türkiye’deki Elektrik Tüketicileri İçin En Uygun Tarife Seçim Önerisi

Balta¹,

Bayılmış²

2019

acperpro

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ÖzetTeknolojinin gelişmesiyle birlikte elektriğe bağımlılık gün geçtikçe artmaktadır. Elektrik tüketimi arttıkça maliyette artmaktadır. Bu nedenle bireyler, optimum fayda-maliyet ilişkisini arayarak tasarruf etmeyi amaçlamaktadırlar. Bu çalışmada ilk olarak 1500 satırlık, gündüz, puant ve gece değerlerine sahip olan veri seti oluşturuldu ve bu veri seti kullanılarak en uygun algoritmayı seçmek için Roc analizi yapıldı. Sonra meskenlerdeki toplam elektrik tüketimi, ROC analizi sonucu seçilen lojistik regresyon algoritması ile RapidMiner yazılımında analiz edildi. Abonelere en uygun tarife seçimi yapıldı. Sonuç olarak,% 100 doğru tahmin elde edildi. AbstractWith the development of technology, dependence on electricity is increasing day by day. Cost increases as electricity consumption increases. For this reason, individuals aim to save by searching for the optimum benefit-cost relationship. In this study, firstly 1500 rows of data created with day, peak, night values and Roc analysis was used to select the most appropriate algorithm for the data set. Later total electricity consumption in households was analyzed by selecting logistic regression which is data mining algorithm in RapidMiner. The most appropriate tariff selection was provided to the subscribers. As a result, 100% accurate estimation was obtained.

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Data quality of electricity consumption data in a smart grid environment

Cited by 80 publications

References 21 publications

Probability estimation of ignitions formation in the electrical network

Probability estimation of ignitions formation in the electrical network

Learning-Based Adaptive Imputation Methodwith kNN Algorithm for Missing Power Data

Veri Madenciliği Algoritmaları Kullanarak Türkiye’deki Elektrik Tüketicileri İçin En Uygun Tarife Seçim Önerisi

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