Building a Fuzzy Expert System for Electric Load Forecasting Using a Hybrid Neural Network
Expert Systems with ApplicationsPublished 1 January 1995
P.K. Dash
Citations22
SJR quartileQ1
SJR score1.85
SNIP2.55
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
From the simulation results, it is observed that the fuzzy expert system using the Kalman filter-based algorithm gives faster convergence and more accurate prediction of a load time series.
Abstract
This paper presents the development of a hybrid neural network to model a fuzzy expert system for time series forecasting of electric load. The hybrid neural network is trained to develop fuzzy logic rules and find optimal input/output membership values of load and weather parameters. A hybrid learning algorithm consisting of unsupervised and supervised learning phases is used for training the fuzzified neural network. In the supervised learning phase, both back propagation and linear Kalman filter algorithms are used for the adjustment of weights and membership functions. Extensive tests have been performed on a 2-year utility data for the generation of peak and average load profiles in 24 h, 48 h, and 168 h ahead time frame during summer and winter seasons. From the simulation results, it is observed that the fuzzy expert system using the Kalman filter-based algorithm gives faster convergence and more accurate prediction of a load time series.
Keywords
Computer ScienceEngineering
Adaptive Filter Theory
12,680 Citations1986S. Haykin
This work focuses on Adaptive Filters Using Infinite-Duration Impulse Response Structures, a Stochastic Processes and Models that combines Kalman Filters as the Unifying Bases for RLS Filters with Estimation Theory and Complex Variables.
IEEE Transactions on Systems Man and CyberneticsFuzzy logic in control systems: fuzzy logic controller. I
5,581 Citations1990C.C. Lee
This paper presents a survey of the FLC; describes a general methodology for constructing an FLC and assessing its per formance; and points to problems which need further research.
IEEE Transactions on Systems Man and CyberneticsGenerating fuzzy rules by learning from examples
2,876 Citations1992L.-X. Wang, Jerry M. Mendel
IEEE Transactions on ComputersNeural-network-based fuzzy logic control and decision system
1,478 Citations1991Chin‐Teng Lin, C.S.G. Lee
A general neural-network (connectionist) model for fuzzy logic control and decision systems is proposed, in the form of feedforward multilayer net, which avoids the rule-matching time of the inference engine in the traditional fuzzy logic system.
Cognitive ScienceFeature discovery by competitive learning
890 Citations1985David E. Rumelhart, David Zipser
IEEE Transactions on Power SystemsAnalysis and evaluation of five short-term load forecasting techniques
707 Citations1989I.S. Moghram, Saifur Rahman
A comparative evaluation of five short-term load forecasting techniques is presented and the transfer function (TF) approach gave the best result, whereas for the peak winter day the TF approach resulted in the next to the worst accuracy.
IEEE Transactions on Power SystemsShort-term load forecasting using an artificial neural network
603 Citations1992K.Y. Lee, Y.T. Cha +1 more
An artificial neural network method is applied to forecast the short-term load for a large power system and the neural network, when grouped into different load patterns, gives a good load forecast.
IEEE Transactions on Power SystemsAdvancement in the application of neural networks for short-term load forecasting
381 Citations1992Tie-Mao Peng, N.F. Hubele +1 more
An improved neural network approach to produce short-term electric load forecasts is proposed, which includes a combination of linear and nonlinear terms which map past load and temperature inputs to the load forecast output.
IEEE Transactions on Power SystemsShort term load forecasting using a multilayer neural network with an adaptive learning algorithm
268 Citations1992Ku-Long Ho, Y.-Y. Hsu +1 more
A multilayer feedforward neural network is proposed for short-term load forecasting and it is found that, once trained by the proposed learning algorithm, the neural network can yield the desired hourly load forecast efficiently and accurately.
IEEE Transactions on Signal ProcessingA fast new algorithm for training feedforward neural networks
225 Citations1992Robert Steven Scalero, N. Tepedelenlioglu
A fast new algorithm is presented for training multilayer perceptrons as an alternative to the back-propagation algorithm that reduces the required training time considerably and overcomes many of the shortcomings presented by the conventional back- Propagation algorithms.
IEEE Transactions on Neural NetworksAn adaptively trained neural network
121 Citations1991Dong-Chul Park, M.A. El-Sharkawi +1 more
The resulting adaptively trained neural network (ATNN), based on nonlinear programming techniques, is shown to adapt to new training data that are in conflict with earlier training data without affecting the neural networks' response to data elsewhere.
Fuzzy Sets and SystemsOn the equivalence of neural nets and fuzzy expert systems
91 Citations1993James J. Buckley, Yoichi Hayashi +1 more
It is shown that a neural net can be approximate to any degree of accuracy using a fuzzy expert system using the assumptions described in the paper.
Comparison of the forecasting accuracy of neural networks with other established techniques
54 Citations2002M. Casey Brace, Jonathan Eichild Schmidt +1 more
The performance of the neural nets was disappointing with all but one of the other techniques outperforming them and this may be caused by a lack of forecasting experience by the neural net developers rather than limitations in the abilities of nets themselves.
Short term electric load forecasting using an adaptively trained layered perceptron
37 Citations2002M.A. El-Sharkawi, Sung‐Kwun Oh +3 more
This study addresses electric load forecasting using artificial neural network (NN) technology for Puget Sound Power and Light Company and finds the NN performed as the best and most sophisticated commercial forecasting systems.
Fuzzy expert systems versus neural networks
19 Citations2003Yoichi Hayashi, James J. Buckley +1 more
It is argued that any fuzzy expert system using one block of rules can be approximated, and the theory is generalized to networks of neural nets and fuzzy expert systems using multiple interconnected blocks of rules.
Knowledge enhanced connectionist models for short-term electric load forecasting
6 Citations2002Saifur Rahman, I. Drezga +1 more
This paper addresses short-term load forecasting using machine learning and neural network techniques to complement the neural networks for enhanced reliability of forecast and improved overall accuracy.