Journal ArticleDOI
Electricity demand load forecasting of the Hellenic power system using an ARMA model
Stylianos Sp. Pappas,Lambros Ekonomou,Panagiotis Karampelas,D.C. Karamousantas,Sokratis K. Katsikas,George E. Chatzarakis,P.D. Skafidas +6 more
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TLDR
In this paper, a new method for electricity demand load forecasting using the multi-model partitioning theory is presented, and compared with three other well established time series analysis techniques namely Corrected Akaike Information Criterion (AICC), AKAike's information Criterion(AIC) and Schwarz's Bayesian Information Criteria (BIC).About:Â
This article is published in Electric Power Systems Research.The article was published on 2010-03-01. It has received 154 citations till now. The article focuses on the topics: Demand forecasting & Bayesian information criterion.read more
Citations
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Journal ArticleDOI
A hybrid annual power load forecasting model based on generalized regression neural network with fruit fly optimization algorithm
TL;DR: A hybrid annual power load forecasting model combining fruit fly optimization algorithm (FOA) and generalized regression neural network was proposed to solve this problem, where the FOA was used to automatically select the appropriate spread parameter value for the GRNN power load forecasts model.
Journal ArticleDOI
Forecasting energy consumption using ensemble ARIMA-ANFIS hybrid algorithm
TL;DR: In this article, the authors forecasted the annual energy consumption in Iran using three patterns of ARIMA-ANFIS model, including grid partitioning, sub clustering, and fuzzy c-means clustering.
Journal ArticleDOI
Forecasting methods in energy planning models
TL;DR: A systematic and critical review of forecasting methods used in 483 EPMs, finding that computational intelligence (CI) methods demonstrate better performance than that of the statistical ones, in particular for parameters with greater variability in the source data.
Journal ArticleDOI
A methodology for Electric Power Load Forecasting
Eisa Al-Meshaiei,Hassan Soltan +1 more
TL;DR: In this paper, the authors present a pragmatic methodology that can be used as a guide to construct electric power load forecasting models based on decomposition and segmentation of the load time series.
Journal ArticleDOI
A novel multi-time-scale modeling for electric power demand forecasting: From short-term to medium-term horizon
Kianoosh G. Boroojeni,M. Hadi Amini,Shahab Bahrami,S. Sitharama Iyengar,Arif I. Sarwat,Orkun Karabasoglu +5 more
TL;DR: A generalized technique for modeling historical load data in the form of time-series with different cycles of seasonality in a given power network, using the hourly-metered load data of PJM network as a real-world input dataset is presented.
References
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Journal ArticleDOI
Estimating the Dimension of a Model
TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.
Estimating the dimension of a model
TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.
Journal ArticleDOI
Paper: Modeling by shortest data description
TL;DR: The number of digits it takes to write down an observed sequence x1,...,xN of a time series depends on the model with its parameters that one assumes to have generated the observed data.
Book ChapterDOI
Fitting autoregressive models for prediction
TL;DR: This is a preliminary report on a newly developed simple and practical procedure of statistical identification of predictors by using autoregressive models in a stationary time series.
Journal ArticleDOI
Short-term load forecasting via ARMA model identification including non-Gaussian process considerations
Shyh-Jier Huang,Kuang-Rong Shih +1 more
TL;DR: The concept of cumulant and bispectrum are embedded into the ARMA model in order to facilitate Gaussian and non-Gaussian process considerations, and with embodiment of a Gaussianity verification procedure, the forecasted model is identified more appropriately.