Missing data treatment of the load profiles in distribution networks

Grigoraș, Gheorghe; Cartina, G.; Bobric, Elena Crenguta; Bărbulescu, Cecilia

doi:10.1109/ptc.2009.5282021

Cited by 10 publications

(5 citation statements)

References 4 publications

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“…In this case, the possible contribution of switchable nodal capacitors located in the distribution systems should be coordinated with the real needs of reactive power support for voltage profile improvement. For the customers that have implemented smart metering devices (which can record consumption at different time intervals, memorize the values and remotely transmit the information), this consumption variation is known [47][48][49][50][51][52]. For customers that have not installed such intelligent devices, it requires a method by which the total electricity consumption over a period of time to be assigned to time slots [53][54][55][56].…”

Section: Operational Aspects Of Voltage Controlmentioning

confidence: 99%

Integration of Dispersed Power Generation

Serițan

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Cepișcă

et al. 2016

Electricity Distribution

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Current paper deals with three main issues, regarding the operation of the electric power systems in the presence of distributed generation. The first issue regards the assessment of the main continuity of supply parameters, such as frequency of interruptions, duration of interruption, power not served and energy no served. This issues are the base guidelines for the network system operators' decision of building new distributed generation capacities which might be able to mitigate the electric network loss of operation. The second issue addressed was the consume variability of the various low-voltage consumers. This particularity, in conjunction with the sheer number of consumers leads to the creation of serious unbalance for the daily curve. This means that if the network system operator cannot contain the load variability, it must buy energy from intraday energy market, with a price decisively higher than the one of the reserved capacity. This issue leads to a two-fold necessity: implementing a better know-how system in order to better contain the customers load fluctuation and the necessity of distributed generation implementation, in order to help load shedding. The third issue regards general technical aspects with respect to the electrical distribution systems operation in the presence of distributed generation.

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Section: Operational Aspects Of Voltage Controlmentioning

confidence: 99%

Integration of Dispersed Power Generation

Serițan

Porumb

Cepișcă

et al. 2016

Electricity Distribution

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“…AI is used inside the entire data process-pipeline (Figure 4) of the Bauhaus.MobilityLab platform to solve different data-driven tasks. Starting with pre-processing [23][24][25][26][27][28] and transformations [29], the data are examined for anomalies as well as missing-values and is normalized or standardized for further processes.…”

Section: Aimentioning

confidence: 99%

Bauhaus.MobilityLab: A Living Lab for the Development and Evaluation of AI-Assisted Services

et al. 2022

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With the vision “Innovation by experiment” the Bauhaus.MobilityLab started in July 2020 as a living lab in the district Brühl of the city Erfurt, Thuringia, Germany. As a unique project, it is coupling the sectors mobility, logistics and energy into a unified living lab. It allows to design, develop and evaluate innovative services to increase the quality of life in the city. Bauhaus.MobilityLab offers access to live smart city data of different domains and provides a set of powerful artificial intelligence (AI) algorithms for data processing, analytics and forecasting. In contrast to existing platforms, its uniqueness is the available and integrated living lab. It allows directly rolling out new smart city services and to evaluate the impact in the real world. This paper describes the implementation of the technical platform supporting the Bauhaus.MobilityLab, realized according to the DIN SPEC 91357 as an open urban platform. It focuses on data sharing based on the concepts of the International Data Spaces and the integration of AI algorithms. The concepts are presented based on examples in the energy domain.

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“…Therefore, the objective of data imputation is to clean the dataset by performing the imputation of missing values and by removing outliers. The imputation of values in feeders' demand can be done in various ways [33,34]. By considering the practical experience observed in the industry, the following data imputation rules are applied hierarchically: (i) assigning the records from the nearest day (same type of day), (ii) choosing a value from the previous month (same type of day) and (iii) imputing the recorded value with the closest date of the previous year (same type of day).…”

Section: Data Pre-processingmentioning

confidence: 99%