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• W2023281112 abstract "Effective modeling and forecasting requires the efficient use of the information contained in the available data so that essential data properties can be extracted and projected into the future. As far as electricity demand load forecasting is concerned time series analysis has the advantage of being statistically adaptive to data characteristics compared to econometric methods which quite often are subject to errors and uncertainties in model specification and knowledge of causal variables. This paper presents a new method for electricity demand load forecasting using the multi-model partitioning theory and compares its performance 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 Criterion (BIC). The suitability of the proposed method is illustrated through an application to actual electricity demand load of the Hellenic power system, proving the reliability and the effectiveness of the method and making clear its usefulness in the studies that concern electricity consumption and electricity prices forecasts." @default.
• W2023281112 created "2016-06-24" @default.
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• W2023281112 date "2010-03-01" @default.
• W2023281112 modified "2023-10-16" @default.
• W2023281112 title "Electricity demand load forecasting of the Hellenic power system using an ARMA model" @default.
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• W2023281112 doi "https://doi.org/10.1016/j.epsr.2009.09.006" @default.
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