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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Bibliographic Details
Published in:Electric power components and systems Vol. 48; no. 6-7; pp. 603 - 614
Main Authors: Li, Yuancheng, Yang, Rongyan, Guo, Panpan
Format: Journal Article
Language:English
Published: Philadelphia Taylor & Francis 06.08.2020
Taylor & Francis Ltd
Subjects:
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
Online Access:Get full text
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Summary: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 article. By analyzing the characteristics of the Spark framework and the MapReduce framework, the Spark big data processing framework is determined as the basic framework for processing massive user load data, and a parallel K-means load clustering model based on Spark is designed. The on-line sequential learning machine OS-ELM makes the hidden layer data of computing each incremental training dataset mutually independent, therefore, a Spark-based parallel OS-ELM (SBPOS-ELM) algorithm is put forward. The proposed model is applied under the smart electricity big data environment and the training samples are selected using the incremental training dataset to make a short-term prediction of the millions of users' smart meter electricity load, which verifies the feasibility and effectiveness of the proposed model. At last, comparing with other commonly used short-term load forecasting algorithms, the experimental results show that SBPOS-ELM algorithm has higher accuracy and operation efficiency.
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ISSN:1532-5008
1532-5016
DOI:10.1080/15325008.2020.1793832