NDLI: Particle swarm optimisation aided least-square support vector machine for load forecast with spikes

Content Provider	IET Digital Library
Author	Lin, Whei Min Tu, Chia Sheng Yang, Ren Fu Tsai, Ming Tang
Abstract	This study developed a load forecasting system for electric market participants. Combining the least-square support vector machine (LSSVM) and particle swarm optimisation (PSO), a LSSVM_PSO was proposed for the solving process. The loads, temperature, and relative humidity of the Taipower system were collected in the Excel Database. Data mining techniques is used to discover meaningful patterns, with the PSO applied to adjust learning rates. The forecasting error can be reduced during the training process to improve both the accuracy and reliability, where even the spikes were nicely followed. The support vector regression, LSSVM, radial basis function neural network and the proposed LSSVM_PSO were all developed and compared to check the convergence and performance. Simulation results demonstrated the effectiveness of the proposed method in a price volatile environment.
Starting Page	1145
Ending Page	1153
Page Count	9
ISSN	17518687
Volume Number	10
e-ISSN	17518695
Issue Number	Issue 5, Apr (2016)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-gtd/10/5
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-gtd.2015.0702
Journal	IET Generation, Transmission & Distribution
Publisher Date	2016-04-07
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Data Mining Electric Market Excel Database Forecasting Error Interpolation And Function Approximation Knowledge Engineering Technique Least Square Support Vector Machine Least Squares Approximation Load Forecasting Load Forecasting System LSSVM_PSO Neural Computing Technique Neural Nets Numerical Analysis Optimisation Technique Particle Swarm Optimisation Power Engineering Computing Power Market Power System Economics Power System Managemen Power System Operation Power System Planning And Layout Radial Basis Function Network Radial Basis Function Neural Network Spikes Support Vector Machine Taipower System Training Process
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Energy Engineering and Power Technology Electrical and Electronic Engineering

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Fault diagnosis method for attached lifting scaffold based on support vector machine

Mid-term electricity market clearing price forecasting using multiple least squares support vector machines

Short-term power load forecasting based on support vector machine and particle swarm optimization

Medium Term Load Forecasting for Jordan Electric Power System Using Particle Swarm Optimization Algorithm Based on Least Square Regression Methods

Short Term Load Forecasting Using Neural Network Trained with Genetic Algorithm & Particle Swarm Optimization

Short-Term Load Forecasting Using Adaptive Annealing Learning Algorithm Based Reinforcement Neural Network

Prediction of electricity demand of China based on the analysis of decoupling and driving force

State of charge prediction for UAVs based on support vector machine

Performance of different kernel functions for LS-SVM-GWO to estimate flashover voltage of polluted insulators

Particle swarm optimisation aided least-square support vector machine for load forecast with spikes

Similar Documents

Fault diagnosis method for attached lifting scaffold based on support vector machine

Mid-term electricity market clearing price forecasting using multiple least squares support vector machines

Short-term power load forecasting based on support vector machine and particle swarm optimization

Medium Term Load Forecasting for Jordan Electric Power System Using Particle Swarm Optimization Algorithm Based on Least Square Regression Methods

Short Term Load Forecasting Using Neural Network Trained with Genetic Algorithm & Particle Swarm Optimization

Short-Term Load Forecasting Using Adaptive Annealing Learning Algorithm Based Reinforcement Neural Network

Prediction of electricity demand of China based on the analysis of decoupling and driving force

State of charge prediction for UAVs based on support vector machine

Performance of different kernel functions for LS-SVM-GWO to estimate flashover voltage of polluted insulators

Particle swarm optimisation aided least-square support vector machine for load forecast with spikes