2010
DOI: 10.1016/j.jaubas.2010.12.004
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Long-term load forecasting for the Kingdom of Bahrain using Monte Carlo method

Abstract: The present study applies Monte Carlo method to electric power network of the Kingdom of Bahrain over a period of five years taking into consideration the maximum electrical loads. The basic variables of the Monte Carlo method are presented and discussed from the standard deviation and reserve points of view. The maximum loads and simulation results were compared on a weekly and yearly basis. A comparison of the minimum mean square error has been calculated and plotted. The results show similarity between the … Show more

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Cited by 6 publications
(3 citation statements)
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“…The technique consists in generating random values for each probability distribution within a model, in order to simulate a relatively large number of scenarios. In MCS, the average value of the number of simulations (š‘ ) is associated with the statistical error estimate (šœ–), given by (20) [40]:…”
Section: Determining the Power Of Dgmentioning
confidence: 99%
“…The technique consists in generating random values for each probability distribution within a model, in order to simulate a relatively large number of scenarios. In MCS, the average value of the number of simulations (š‘ ) is associated with the statistical error estimate (šœ–), given by (20) [40]:…”
Section: Determining the Power Of Dgmentioning
confidence: 99%
“…These authors have found that most of the developed methodologies are implemented to short- or medium-term load forecasting and to a lesser extent to long-term forecasting. For some of the existing methods, the reader is referred to Mi et al (2018), Gaillard et al (2016), Feng (2016), Goude et al (2014), Halvorsen (1976), Fildes and Lusk (1984), Faruqui et al (1990) and Qader and Qamber (2010).…”
Section: Introductionmentioning
confidence: 99%
“…53 Then, Qader and Qamber et al proposed a Monte Carlo method to long-term load forecasting for the Kingdom of Bahrain. 41 Afterwards, AlRashidi and EL-Naggar designed a particle swarm optimization (PSO) algorithm to long term electric load forecasting. 4 In 2011, Filik et al proposed a novel mathematical method for modeling and forecasting electric energy demand.…”
Section: Introductionmentioning
confidence: 99%