Proceedings ArticleDOI
Application of neural network technology to short-term system load forecasting
Alex D. Papalexopoulos,Shangyou Hao,Tie-Mao Peng +2 more
- Vol. 2, pp 796-800
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This article is published in IEEE PowerTech Conference.The article was published on 1993-09-05. It has received 15 citations till now. The article focuses on the topics: Artificial neural network & Term (time).read more
Citations
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Journal ArticleDOI
A study of the importance of occupancy to building cooling load in prediction by intelligent approach
TL;DR: In this article, a probabilistic entropy-based neural (PENN) model was proposed to predict the cooling load of a building, which is one of the key factors in the success of energy-saving measures.
Journal ArticleDOI
Weather sensitive method for short term load forecasting in Electric Power Utility of Serbia
S. Ruzic,A. Vuckovic,N. Nikolic +2 more
TL;DR: In this paper, a regression-based adaptive weather sensitive short-term load-forecasting algorithm has been developed and implemented in Electric Power Utility of Serbia, which has been used to forecast 24-h loads one to seven days ahead since 1991.
Journal ArticleDOI
The use of occupancy space electrical power demand in building cooling load prediction
TL;DR: In this paper, the authors presented an investigation into the use of occupancy space electrical power demand to mimic occupants' activities in building cooling load prediction by intelligent approach, where the artificial neural network model adopted is the Levenberg-Marquardt algorithm.
Proceedings ArticleDOI
A review of ANN-based short-term load forecasting models
Y. Rui,A.A. El-Keib +1 more
TL;DR: An extensive survey of ANN-based load forecasting models is given and the six most important factors which affect the accuracy and efficiency of the load forecasters are presented and discussed.
Journal ArticleDOI
A neural network operator oriented short-term and online load forecasting environment
M. Sforna,F. Proverbio +1 more
TL;DR: In this article, the authors investigated the application of artificial intelligence techniques and eventually checked their positive contribution in the field of short-term load forecasting, focusing on the construction problems of an integrated tool specifically designed to meet the needs of utility forecasters and power system operators.
References
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Book
Time series analysis, forecasting and control
TL;DR: In this article, a complete revision of a classic, seminal, and authoritative book that has been the model for most books on the topic written since 1970 is presented, focusing on practical techniques throughout, rather than a rigorous mathematical treatment of the subject.
Journal ArticleDOI
Multilayer feedforward networks are universal approximators
TL;DR: It is rigorously established that standard multilayer feedforward networks with as few as one hidden layer using arbitrary squashing functions are capable of approximating any Borel measurable function from one finite dimensional space to another to any desired degree of accuracy, provided sufficiently many hidden units are available.
Journal ArticleDOI
Time Series Analysis Forecasting and Control
TL;DR: This revision of a classic, seminal, and authoritative book explores the building of stochastic models for time series and their use in important areas of application forecasting, model specification, estimation, and checking, transfer function modeling of dynamic relationships, modeling the effects of intervention events, and process control.
Book
Theory and Practice of Recursive Identification
Lennart Ljung,Torsten Söderström +1 more
TL;DR: Methods of recursive identification deal with the problem of building mathematical models of signals and systems on-line, at the same time as data is being collected.
Journal ArticleDOI
The accuracy of extrapolation (time series) methods: Results of a forecasting competition
Spyros Makridakis,A. Andersen,A. Andersen,R. Carbone,Robert Fildes,Robert Fildes,Michèle Hibon,R. Lewandowski,J. Newton,E. Parzen,Robert L. Winkler +10 more
TL;DR: The results of a forecasting competition are presented to provide empirical evidence about differences found to exist among the various extrapolative (time series) methods used in the competition.