Data-Driven Load Data Cleaning and Its Impacts on Forecasting Performance

Cai, Xiao; Wang, Yi; Zhang, Jialun; Shi, Jing; Li, Bingjie; Chen, Kang

doi:10.1109/ispec48194.2019.8975047

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Cited by 2 publications

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A Method of Anomaly Data Identification and Correction for Industrial and Commercial Consumers’ Electricity Loads

Ding,

Yang,

Wei

et al. 2023

2023 IEEE Sustainable Power and Energy Conference (iSPEC)

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A Method of Anomaly Data Identification and Correction for Industrial and Commercial Consumers’ Electricity Loads

Ding,

Yang,

Wei

et al. 2023

2023 IEEE Sustainable Power and Energy Conference (iSPEC)

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A systematic review of data pre-processing methods and unsupervised mining methods used in profiling smart meter data

Dahunsi¹,

Olawumi²,

Ale³

et al. 2021

ELECTRENG

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<abstract> <p>The evolution of smart meters has led to the generation of high-resolution time-series data - a stream of data capable of unveiling valuable knowledge from consumption behaviours for different applications. The ability to extract hidden knowledge from such massive amounts of data requires that it be analysed intelligently. Hence, for a clear representation of the various consumption behaviours of consumers, a good number of data mining technologies are usually employed. This paper presents a systematic review of the various data mining techniques and methodologies employed while profiling energy data streams. The review identifies the strengths and shortcomings of existing data mining methods as applied in research, focusing more on data processing techniques and load clustering. Also discussed are data mining methods used to profile consumption data, their pros and cons. It was inferred during the research that the choice of data mining technique employed is highly dependent on the application it is intended for and the intrinsic nature of the dataset.</p> </abstract>

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Data-Driven Load Data Cleaning and Its Impacts on Forecasting Performance

Cited by 2 publications

References 15 publications

A Method of Anomaly Data Identification and Correction for Industrial and Commercial Consumers’ Electricity Loads

A Method of Anomaly Data Identification and Correction for Industrial and Commercial Consumers’ Electricity Loads

A systematic review of data pre-processing methods and unsupervised mining methods used in profiling smart meter data

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