Application of improved fuzzy c-means algorithm on bad-data identification and adjustment in short-term load forecasting

Bad data identification and adjustment in Short-term load forecasting should fully consider the similarity and smoothness of the daily load curve. First, completing the missing data use the Neville algorithm. Then the daily load curves are clustered by improved fuzzy c-means algorithm, and a typical...

Full description

Saved in:
Bibliographic Details
Main Authors: Sun Qian, Yao JianGang, Jiang WenQian, Yang ShengJie, Xu ZhenChao
Format: Conference Proceeding
Language:English
Subjects:
Automation
bad data
Classification algorithms
Clustering algorithms
improved FCM algorithm
Indexes
Interpolation
Load forecasting
Neville algorithm
typical curve
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Bad data identification and adjustment in Short-term load forecasting should fully consider the similarity and smoothness of the daily load curve. First, completing the missing data use the Neville algorithm. Then the daily load curves are clustered by improved fuzzy c-means algorithm, and a typical load curve is thus obtained for each cluster. Use the horizontal and vertical similarity of the daily load curve to identify the bad data. At last,the bad data are adjusted with typical curves. Test results using actual data demonstrate the validity and feasibility of the proposed method.
DOI:10.1109/APAP.2011.6180464