Spark-based Parallel OS-ELM Algorithm Application for Short-term Load Forecasting for Massive User Data

The data type and quantity of user load data show an exponential growth, so that the traditional load forecasting methods can hardly meet the load forecasting requirements of massive users. Aiming at this problem, a parallel OS-ELM short-term load forecasting model based on Spark is proposed in this...

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Vydané v:	Electric power components and systems Ročník 48; číslo 6-7; s. 603 - 614
Hlavní autori:	Li, Yuancheng, Yang, Rongyan, Guo, Panpan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Philadelphia Taylor & Francis 06.08.2020 Taylor & Francis Ltd
Predmet:	Algorithms Big Data Clustering Data processing Datasets Electrical loads Electricity consumption Electricity meters Forecasting K-means algorithm Machine learning massive user load data Mathematical models parallel OS-ELM algorithm short-term load forecast Smart grid Spark Training User requirements
ISSN:	1532-5008, 1532-5016
On-line prístup:	Získať plný text
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