Modelling the impact of temperature on electricity consumption in the Eastern Province of Saudi Arabia
ABSTRACT An econometric model is developed to forecast electricity consumption and study the impact of ambient temperature, expressed in terms of degree days (DDs), on consumption in the Eastern Province of Saudi Arabia. It is apparent that temperature plays an important role in the demand for electricity. The relationship between the behaviour of electricity consumption and temperature expressed in DDs is explored.
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ABSTRACT: This paper presents a comparative study of the electricity consumption (EC) in an urban low-voltage substation before and during the economic crisis (2008–2013). This low-voltage substation supplies electric power to near 400 users. The EC was measured for an 11-year period (2002–2012) with a sampling time of 1 minute. The study described in the paper consists of detecting the changes produced in the load curves of this substation along the time due to changes in the behaviour of consumers.The EC was compared using representative curves per time period (precrisis and crisis).These representative curves were obtained after a computational process, which was based on a search for days with similar curves to the curve of a determined (base) date. This similitude was assessed by the proximity on the calendar, day of the week, daylight time, and outdoor temperature. The last selection parameter was the error between the nearest neighbour curves and the base date curve. The obtained representative curves were linearized to determine changes in their structure (maximum and minimum consumption values, duration of the daily time slot, etc.).The results primarily indicate an increase in the EC in the night slot during the summer months in the crisis period.The Scientific World Journal 04/2014; 2014:14. DOI:10.1155/2014/948361 · 1.73 Impact Factor
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ABSTRACT: This study aims to review the related literature on operations research (OR)/management science (MS) in the Arab world published during the last three decades. Owing to the nature of this study, an extensive survey of the related literature was conducted and inferences were drawn. The inferences drawn from the literature survey on OR/MS in the Arab world were first, there is a paucity of published real-world applications of OR/MS. Second, there is a lack of published survey-type studies in most Arab countries, except in Jordan, Lebanon, Palestine, Syria, and the United Arab Emirates. Third, the majority of published works on OR/MS were of a conceptual nature. A few papers concerned with OR/MS in the Arab world were published during the 1980s, with a special emphasis on conceptual issues rather than on applied or survey-type studies. The 1990s witnessed an increased number of publications on both survey-type and conceptual studies. Since 2000, the number of publications has increased substantially, mainly through conceptual studies. This study has a number of implications for both practitioners and researchers. Practitioners will be made aware of the applications of OR/MS in the Arab world and the type of problems that have been addressed. This, in turn, might motivate the decision makers and the managers to adopt OR/MS approaches in solving their organizations' problems. As a result, this might increase the usage of OR/MS in this part of the world. Researchers will be able to identify the OR/MS research areas that need more attention in the Arab world. The study mainly covers the studies that are written in English and indexed in non-Arabic databases. Although the Arabic works were not surveyed exhaustively, the author reviewed and included some available OR/MS works written in Arabic. This study is considered as the first work of its type in surveying the scholarly publications pertaining to OR/MS in the Arab world since the 1980s.International Transactions in Operational Research 10/2010; 18(1):53 - 69. DOI:10.1111/j.1475-3995.2010.00789.x · 0.48 Impact Factor
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ABSTRACT: Daily maximum temperature can be used a good indicator of peak energy consumption, since it can be used to predict the massive use of heating or air conditioning systems. Thus, the prediction of daily maximum temperature is an important problem with interesting applications in the energy field, since it has been proven that electricity demand depends much on weather conditions. This paper presents a novel methodology for daily maximum temperature prediction, based on a Support Vector Regression approach. The paper is focused on different measuring stations in Europe, from which different meteorological variables have been obtained, including temperature, precipitation, relative humidity and air pressure. Two more variables are also included, specifically synoptic situation of the day and monthly cycle. Using this pool of prediction variables, it is shown that the SVMr algorithm is able to give an accurate prediction of the maximum temperature 24 h later. In the paper SVMr technique applied is fully described, including some bounds on the machine hyper-parameters in order to speed up the SVMr training process. The performance of the SVMr has been compared to that of different neural networks in the literature: a Multi-layer perceptron and an Extreme Learning Machine.Renewable Energy 11/2011; 36(11):3054-3060. DOI:10.1016/j.renene.2011.03.030 · 3.36 Impact Factor