Journal ArticleDOI
Comparison of very short-term load forecasting techniques
K. Liu,S. Subbarayan,R. R. Shoults,Michael T. Manry,Chiman Kwan,F.I. Lewis,Jorge Naccarino +6 more
- Vol. 11, Iss: 2, pp 877-882
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TLDR
The preliminary study shows that it is feasible to design a simple, satisfactory dynamic forecaster to predict very short-term power system load trends online and FL and NN can be good candidates for this application.Abstract:
Three practical techniques-fuzzy logic (FL), neural networks (NN), and autoregressive models-for very short-term power system load forecasting are proposed and discussed in this paper. Their performances are evaluated through a computer simulation study. The preliminary study shows that it is feasible to design a simple, satisfactory dynamic forecaster to predict very short-term power system load trends online. FL and NN can be good candidates for this application.read more
Citations
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Journal ArticleDOI
Neural networks for short-term load forecasting: a review and evaluation
TL;DR: This review examines a collection of papers (published between 1991 and 1999) that report the application of NNs to short-term load forecasting, and critically evaluating the ways in which the NNs proposed in these papers were designed and tested.
Book
Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach
TL;DR: In this paper, the authors present a case study of the electricity market in the UK and Australia, showing that electricity prices in both countries are correlated with the number of customers and the amount of electricity consumed.
Journal ArticleDOI
Probabilistic electric load forecasting: A tutorial review
TL;DR: The need to invest in additional research, such as reproducible case studies, probabilistic load forecast evaluation and valuation, and a consideration of emerging technologies and energy policies in the probabilism load forecasting process are underlined.
Journal ArticleDOI
Electric load forecasting: Literature survey and classification of methods
TL;DR: A review and categorization of electric load forecasting techniques is presented, dividing them into nine categories: multiple regression, exponential smoothing, iterative reweighted least-squares, adaptive load forecasting, stochastic time series, ARMAX models based on genetic algorithms, fuzzy logic, neural networks, and expert systems.
Journal ArticleDOI
A comparison of univariate methods for forecasting electricity demand up to a day ahead
TL;DR: In this article, the authors compared the performance of six univariate methods for short-term electricity demand forecasting for lead times up to a day ahead and concluded that simpler and more robust methods can outperform more complex alternatives.
References
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Proceedings ArticleDOI
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