Using Cluster Analysis and Least Square Support Vector Machine to Predicting Power Demand for the Next-Day

Using Cluster Analysis and Least Square Support Vector Machine to Predicting Power Demand for the Next-Day

Predicting the next-day power demand has been one of the most important research areas in the electricity industry for the past decade. A successful and more accurate prediction can help both the policy-makers and consumers to plan their bidding strategies. Self-organizing maps (SOM) and K-means are...

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Bibliographic Details
Main Authors: Jianqiang Huang, Ye Liang, Haodong Bian, Xiaoying Wang
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Short-term electric demand forecasting
cluster analysis
self-organizing maps (SOM)
K-means clustering
least square support vector machine (LSSVM)
Online Access:https://ieeexplore.ieee.org/document/8736216/

Internet

https://ieeexplore.ieee.org/document/8736216/

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