Short-Term Load Forecasting Based on Big Data Technologies

Xin Zhang; Ming Cheng; Yang Liu; Dong Hua Li; Rui Min Wu

doi:10.4028/www.scientific.net/AMM.687-691.1186

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 687-691Short-Term Load Forecasting Based on Big Data...

Short-Term Load Forecasting Based on Big Data Technologies

Abstract:

In recent years, wide installation of smart meters and implementation of Smart Meter Management System (SMMS) provides data foundation for short-term load forecasting. In this paper, a new load forecasting approach is proposed based on big data technologies using smart meter data. The new approach analyzes the characteristics of numerous electricity users, which helps system operators identify influencing factors. Big data architecture can handle large amount of data and computation efforts. Compared with the traditional system load forecasting methods, this new approach produces better prediction accuracy.

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Periodical:

Applied Mechanics and Materials (Volumes 687-691)

Pages:

1186-1192

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.687-691.1186

Citation:

Cite this paper

Online since:

November 2014

Authors:

Xin Zhang*, Ming Cheng, Yang Liu, Dong Hua Li, Rui Min Wu

Keywords:

Big Data Technologies, Data Mining (DM), Short-Term Load Forecasting, User Load

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* - Corresponding Author

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