International Journal of Economics and Business Administration
Articles Information
International Journal of Economics and Business Administration, Vol.5, No.2, Jun. 2019, Pub. Date: May 10, 2019
Grouping Analysis for the Energy Consumption of Domestic Loads in the Distribution Network at North Cairo Zone
Pages: 85-97 Views: 366 Downloads: 130
[01] Sara Nada, Department of Economics, Faculty of Economics & Political Sciences, Cairo University, Cairo, Egypt.
[02] Mohamed Hamed, Department of Electrical Engineering, Faculty of Engineering, Port Said University, Port Said, Egypt.
This paper presents a statistical analysis for an ideal official model (15 customers) for electrical energy consumption in the domestic sector of Cairo, the capital of Egypt (mega city) during the last 26 years (Jan 1992-Jan 2018). The statistical dispersion parameters (Mean and Maximum Values, and standard deviation) for the populations of energy consumption are determined and analyzed. The original data of customers are grouped in diverse scale into 12 groups according to either mean value or standard deviation after the purification of original data (within two scales as multi-month reading and the occupied houses conditions). This is based on two title (mean value and standard deviation) where each of them is tailored into closed and wide range data. This creates 6 groups for each while the groups G10 and G8 are appeared for both classifications as the same. The effect of simultaneous, static and maximum values may be processed for the energy growth within the period of 26 years. Additionally, two issued groups for the same model have been inserted with the study and the relative factors have been analyzed. The created groups are investigated statistically for mean value and standard deviation so that the accurate prediction for the future electric energy consumption growth can be realized as the target of article. The given investigation determines the automatic random characteristics in the domestic demand loads of customers and then, important parameters for the studied model (grouped sampling) are deduced statistically. The growth rate of energy consumption is calculated within the period for all groups in details. The results, as a micro-scale base, approved the necessity of statistical parameters for planning problems in general. The prediction for not only energy needed but also for future power demand, which is a vigorous factor for the demand requirements of power stations in the united network, is simply extracted. The maximum value should be tested for the forecasting process as a vital item because it points to the future power demand for the power generation. The prediction for future annual loads is extracted mathematically. The proposed simple linear prediction can reach to the same results with an appropriate accuracy since the complex methods of prediction may consume both computational time and effort. The concept is easy for applications in different fields where the maximum prediction gives the value of power demand required for the united electric network. The grouping system for a lot of populations may be recommended for all similar problems because it facilitates the processing populations. The proposed grouping system can be considered for medical, industrial products, marketing products, weather, stocks, etc. to be a fundamental tool for the prediction in each field.
Domestic, Dispersion Factors, Simultaneous Energy, Growth Rate, Prediction Performance, Statistical Grouping
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