Letters: Daily maximum load forecasting of consecutive national holidays using OSELM-based multi-agents system with weighted average strategy

Authors:
Keem Siah Yap;Hwa Jen Yap
Affiliations:
College of Graduate Studies, Universiti Tenaga Nasional, Jalan IKRAM-UNITEN, 43009 Kajang, Selangor, Malaysia;Department of Engineering Design and Manufacture, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia
Venue:
Neurocomputing
Year:
2012

Citing 13
Cited 0

Learning internal representations by error propagation

Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Neural Networks for Pattern Recognition

Neural Networks for Pattern Recognition
Letters: Convex incremental extreme learning machine

Neurocomputing
Enhanced random search based incremental extreme learning machine

Neurocomputing
A protein secondary structure prediction framework based on the Extreme Learning Machine

Neurocomputing
Letters: Ensemble of online sequential extreme learning machine

Neurocomputing
Error minimized extreme learning machine with growth of hidden nodes and incremental learning

IEEE Transactions on Neural Networks
A Neuro-Fuzzy Inference System Through Integration of Fuzzy Logic and Extreme Learning Machines

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Orthogonal least squares learning algorithm for radial basis function networks

IEEE Transactions on Neural Networks
A study on reduced support vector machines

IEEE Transactions on Neural Networks
Generalized regression neural networks in time-varying environment

IEEE Transactions on Neural Networks
Universal approximation using incremental constructive feedforward networks with random hidden nodes

IEEE Transactions on Neural Networks
A Fast and Accurate Online Sequential Learning Algorithm for Feedforward Networks

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.01

Visualization

Abstract

In the previous research, a Multi-Agent System based on Online Sequential Extreme Learning Machine (OSELM) neural network and Bayesian Formalism (MAS-OSELM-BF) has been introduced for solving pattern classification problems. However this model is incapable of handling regression tasks. In this article, a new OSELM-based multi-agent system with weighted average strategy (MAS-OSELM-WA) is introduced for solving data regression tasks. A MAS-OSELM-WA consists of several individual OSELM (individual agent) and the final decision (parent agent). The outputs of the individual agents are sent to the parent agent for a final decision whereby the coefficients of parent agent are computed by a gradient descent method. The effectiveness of the MAS-OSELM-WA is evaluated by an electrical load forecasting problem in Malaysia for a month with consequent national holidays (i.e., during the month of Hari Raya-Malay New Year of Malaysia). The results demonstrated that the MAS-OSELM-WA is able to produce good performance as compared with the other approaches.