Wyniki wyszukiwania - BazTech

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Detection of anomalous consumers based on smart meter data

Kaleta Joanna, Dubinski Jan, Wojdan Konrad, Swirski Konrad

Journal of Power Technologies

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2021

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Vol. 101, nr 4

202--212

EN

The continuous smart grid development makes the advanced metering infrastructure an essential part of electricity management systems. Smart meters not only provide consumers with more economical and sustainable electricity consumption but also enable the energy supplier to identify suspicious behaviour or meter failure. In this work, a shape-based algorithm that indicates households with abnormal electricity consumption pattern within a given consumer group was proposed. The algorithm was developed under the assumption that the reason for unusual electricity consumption may not only be a meter failure or fraud, but also consumer’s individual preferences and lifestyle. In the presented methodology, five unsupervised anomaly detection methods were used: K Nearest Neighbors, Local Outlier Factor, Principal Component Analysis, Isolation Forest and Histogram Based Outlier Score. Two time series similarity measures were applied: basic Euclidean distance and Dynamic Time Warping, which allows finding the best alignment between two time series. The algorithm’s performance was tested with multiple parameter configurations on five different consumer groups. Additionally, an analysis of the individual types of anomalies and their detectability by the algorithm was performed.

2

Long-term prediction of underground gas storage user gas flow nominations

Kurek Teresa, Wojdan Konrad, Świrski Konrad

Journal of Power Technologies

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2019

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Vol. 99, nr 4

272--280

EN

Many companies operating on the natural gas market use natural gas storage to balance production and transport capacities with major variations in gas demand. This paper presents an approach to predicting users’ gas flow nomination in underground gas storage by different users. A one-year prediction horizon is considered with weekly data resolution. Basic models show that whereas for the great majority of users we can predict nomination based only on weather data and technical parameters, for some users additional macro-economic data significantly improved prediction accuracy. Various modeling techniques such as linear regression, autoregressive exogenous model and Artificial Neural Network were used to develop prediction models. Results show that for most users an Artificial Neural Network provides optimal accuracy, indicating the non-linearity of the relationship between input and output variables. The models developed are intended to be used as support for facility operation decisions and gas storage product portfolio modifications.

3

Inteligentna sieć ciepłownicza. Prognozowanie zapotrzebowania na ciepło i optymalizacja pracy sieci ciepłowniczej

Guzek Michał, Wojdan Konrad

Energetyka Cieplna i Zawodowa

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2019

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Nr 3

47--51

PL

Miejskie systemy ciepłownicze od dziesięcioleci zaspokajają potrzeby cieplne mieszkańców miast. Powstają nowoczesne narzędzia informatyczne stworzone w celu zoptymalizowania pracy systemów ciepłowniczych, dla osiągnięcia korzyści ekonomicznych i środowiskowych. Popularyzowana jest koncepcja Inteligentnej Sieci Ciepłowniczej (ISC).