Abstract
As to the short-term electric power load forecasting, its accuracy is affected by many uncertain influencing factors. To improve the forecasting accuracy, a novel method using Similar Days based on fuzzy clustering analysis is proposed in this chapter. Firstly it categorizes the weather factors as temperature, air pressure, wind speed, overcast day, rainy day, etc., and then together with week type and day type these factors form the influence items. According to the items above, fuzzy rules are applied to establish the mapping table to get the factors quantized. Next, the cluster technology is utilized to classify the content in the mapping table, and the similar days are chosen based on the clustering level, which is to reduce the numbers of samples and accelerate the speed of selection. Secondly, aiming to eliminating non-gaussian noise contained in the similar days’ power load, lifting wavelet transform is adopted to extract the low sequence components. Finally a Least Squares Support Vector Machine (LS-SVM), which is optimized by particle swarm optimization algorithm, is designed to predict the low-frequency part while mean square weighted method is used to predict the high-frequency part. The simulation results show that this fuzzy clustering similar days method is effective.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Liu S, Yang L, Wang Z et al (2003) Combined power load forecast model based on Matlab neural network toolbox. Elec Power Autom Equip 23(3):59–61
Pan F, Cheng H, Yang J et al (2004) Power system short-term load forecasting based on support vector machines. Power Syst Technol 28(21):39–42
Xie H, Wei J, Liu H (2006) Parameter selection and optimization method of SVM model for short-term load forecasting. Proc CSEE 26(22):17–22
Niu D, Wang H, Gu Z (2005) Application of dynamic fuzzy neural network based on rough set theory and GA in power system short-term load forecast. Elec Power Autom Equip 25(12):10–14, 18
Han Y, Li H (2012) Bus load forecasting based on wavelet transform and SVM. Elec Power Autom Equip 32(4):88–91
Xie H, Chen Z, Niu D et al (2001) The research of daily load forecasting model based on wavelet decomposing and climatic influence. Proc CSEE 21(5):5–10
Ying C, Luh PB, Che G et al (2010) Short-term load forecasting: similar day-based wavelet neural networks. IEEE Trans Power Syst 25(1):322–330
Mohamed AA, Naresh SK (1982) Short-term load demand modeling and forecasting: a review. IEEE Trans Syst Man Cybern 12(3):370–382
Acknowledgment
This work is supported by the National Natural Science Foundation of China No. 60504010), the High Technology Research and Development Program of China (2008AA04Z129) and State Key Laboratory of Synthetical Automation for Process Industries.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer Science+Business Media New York
About this paper
Cite this paper
Yu, L., Zheng, Y., Wang, X., Li, L., Yao, G., Chen, H. (2014). Short-Term Load Forecasting Based on Fuzzy Clustering Analysis Similar Days. In: Xing, S., Chen, S., Wei, Z., Xia, J. (eds) Unifying Electrical Engineering and Electronics Engineering. Lecture Notes in Electrical Engineering, vol 238. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-4981-2_21
Download citation
DOI: https://doi.org/10.1007/978-1-4614-4981-2_21
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4614-4980-5
Online ISBN: 978-1-4614-4981-2
eBook Packages: EngineeringEngineering (R0)