Machine Leaning Algorithms and Time Series Feature Extraction Library for Electricity Consumption Fraud Detection in Smart Grids

2021 ◽  
Author(s):  
Simona-Vasilica Oprea ◽  
Adela Bara ◽  
Gabriela Dobrita Ene
Keyword(s):  
Time Series ◽  
Feature Extraction ◽  
Smart Grids ◽  
Electricity Consumption ◽  
Fraud Detection ◽  
Machine Leaning

scholarly journals Whole Time Series Data Streams Clustering: Dynamic Profiling of the Electricity Consumption

Entropy ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. 1414
Author(s):  
Krzysztof Gajowniczek ◽  
Marcin Bator ◽  
Tomasz Ząbkowski
Keyword(s):  
Time Series ◽  
Data Streams ◽  
Smart Grids ◽  
Time Series Data ◽  
Electricity Consumption ◽  
Large Data ◽  
Individual Characteristics ◽  
Series Data ◽  
Underlying Distribution ◽  
Sliding Windows

Data from smart grids are challenging to analyze due to their very large size, high dimensionality, skewness, sparsity, and number of seasonal fluctuations, including daily and weekly effects. With the data arriving in a sequential form the underlying distribution is subject to changes over the time intervals. Time series data streams have their own specifics in terms of the data processing and data analysis because, usually, it is not possible to process the whole data in memory as the large data volumes are generated fast so the processing and the analysis should be done incrementally using sliding windows. Despite the proposal of many clustering techniques applicable for grouping the observations of a single data stream, only a few of them are focused on splitting the whole data streams into the clusters. In this article we aim to explore individual characteristics of electricity usage and recommend the most suitable tariff to the customer so they can benefit from lower prices. This work investigates various algorithms (and their improvements) what allows us to formulate the clusters, in real time, based on smart meter data.

Time series forecasting based on wavelet decomposition and feature extraction

2016 ◽  
Vol 28 (S1) ◽  
pp. 183-195 ◽  
Author(s):  
Tianhong Liu ◽  
Haikun Wei ◽  
Chi Zhang ◽  
Kanjian Zhang
Keyword(s):  
Time Series ◽  
Feature Extraction ◽  
Wavelet Decomposition ◽  
Time Series Forecasting

Multi-Dimensional Encoding Image Based Time Series Feature Extraction for Improved Colorimetric Gas Concentration Estimation

2021 ◽  
Vol MA2021-02 (57) ◽  
pp. 1939-1939
Author(s):  
Changhyun KIM ◽  
Junyeop Lee ◽  
Junkyu Park ◽  
Daewoong Jung ◽  
Chang-Woo Nam ◽  
...  
Keyword(s):  
Time Series ◽  
Feature Extraction ◽  
Gas Concentration ◽  
Concentration Estimation

scholarly journals Extracting Features from Time Series

2018 ◽  
pp. 85-100 ◽  
Author(s):  
Christian Herff ◽  
Dean J. Krusienski
Keyword(s):  
Time Series ◽  
Feature Extraction ◽  
Clinical Data ◽  
Noise Filtering ◽  
Time Intervals ◽  
Time Points ◽  
Biomedical Systems ◽  
Patient Weight ◽  
Short Time

AbstractClinical data is often collected and processed as time series: a sequence of data indexed by successive time points. Such time series can be from sources that are sampled over short time intervals to represent continuous biophysical wave-(one word waveforms) forms such as the voltage measurements representing the electrocardiogram, to measurements that are sampled daily, weekly, yearly, etc. such as patient weight, blood triglyceride levels, etc. When analyzing clinical data or designing biomedical systems for measurements, interventions, or diagnostic aids, it is important to represent the information contained within such time series in a more compact or meaningful form (e.g., noise filtering), amenable to interpretation by a human or computer. This process is known as feature extraction. This chapter will discuss some fundamental techniques for extracting features from time series representing general forms of clinical data.

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