[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

[01] Mohamed Hamed and Sara Nada: Analysis of Electric Loads, Lambert Academic Publishing (LAP), Germany, (Omni Scriptum GmbH & Co. KG.), July 2011, No. 26286, ISBN-13: 978-3845430225 ISBN-10: 3845430222. https://www.amazon.com/Analysis-Electric-Loads-Load-Curves/dp/3845430222. [02] Sara Nada & Mohamed Hamed: Energy Cost Analysis for Domestic Economics in Egypt Based on the Pound Floating, International Journal of Economics & Business Administration, Paper No. 70220067, Public Science Framework, Vol. 4, No. 4, Dec. 2018, Pub. Date: Jan. 19, 2019, (123-138), ISSN: 2381-7356 (Print); ISSN: 2381-7364 (Online), http://www.aiscience.org/journal/paperInfo/ijeba?paperId=4300. [03] Kadir Amasyali Nora & M. El-Gohary: A review of data-driven building energy consumption prediction studies, Renewable and Sustainable Energy Review, Volume 81, Jan 2018, Part 1, (1192-1205), https://doi.org/10.1016/j.rser.2017.04.095 https://www.sciencedirect.com/science/article/pii/S1364032117306093. [04] Hal S. Jnowles, Mark E. Hostetler, Larry S. Liebovitch: Describing the dynamics, distributions, and multiscale relationships in the time evolution of residential building energy consumption, Journal of Energy & Buildings, Vol. 158, 1 January 2018, (310-325). https://doi.org/10.1016/j.enbuild.2017.09.071. https://www.sciencedirect.com/science/article/pii/S0378778817307065. [05] Hyunju Jang and Jian Kang: An energy model of high-rise apartment buildings integrating variation in energy consumption between individual units, Journal of Energy & Buildings, Vol. 158, 1 January 2018, (656-667), https://doi.org/10.1016/j.enbuild.2017.10.047 https://www.sciencedirect.com/science/article/pii/S0378778817334266. [06] Power consumption accelerated again in 2017 2.6%, https://data.worldbank.org/indicator/eg.use.elec.kh.pc https://yearbook.enerdata.net/electricity/electricity-domestic-consumption-data.html. [07] Tallal Javied, Tobias Rackow, Roland Stankalla, Christian Sterk and Jorg Franke: A Study on Electric Energy Consumption of Manufacturing Companies in the German Industry with the Focus on Electric Drives, Procedia CIRP, Vol. 41, 2016, (318-322), open access, https://doi.org/10.1016/j.procir.2015.10.006. https://www.sciencedirect.com/science/article/pii/S2212827115010586. [08] Simona D’ Oca, Tianzhen Hong and Jared Langevin: The human dimensions of energy use in buildings: A review, Renewable & Sustainable Energy Reviews, Volume 81, Part 1, Jan 2018, (731-742), https://doi.org/10.1016/j.rser.2017.08.019 https://www.sciencedirect.com/science/article/pii/S1364032117311632. [09] Lucheng Hong, Wantao Shu and Angela C. Chao: Recurrence Interval Analysis on Electricity Consumption of an Office Building in China, Journal of Sustainability 2018, 10 (2), 306; doi: 10.3390/su10020306 http://www.mdpi.com/2071-1050/10/2/306/html. [10] M. Hansen & B. Hauge: Scripting, control, and privacy in domestic smart grid technologies: Insights from a Danish pilot study, Energy Research & Social Science, Vol. 25, March 2017, Pages 112-123, https://doi.org/10.1016/j.erss.2017.01.005. https://www.sciencedirect.com/science/article/pii/S2214629617300051. [11] Timothy M. Hansen, Edwin K. Chong, Siddharth Suryanarayanan, Anthony A. Maciejewski and Howard Jay Siegel: A Partially Observable Markov Decision Process Approach to Residential Home Energy Management, IEEE Transaction o Smart Grid, Vol. 9, Issue 2, March 2018, (1271–1281), INSPEC Accession Number: 17595777, DOI: 10.1109/TSG.2016.2582701 https://ieeexplore.ieee.org/abstract/document/7496865/. [12] Juliano Camargo, Fred Spiessens and Chris Hermans: A Network Flow Model for Price-Responsive Control of Deferrable Load Profiles, Journal Energies 2018, 11 (3), 613, https://doi.org/10.3390/en11030613. http://www.mdpi.com/1996-1073/11/3/613. [13] Vaibhav Jain, Naveen Jain and R R Joshi: Priority index based scheduling of residential load using smart home load manager, 2018 2^nd International Conference on Inventive Systems and Control (ICISC), Coimbatore, India, 19-20 Jan. 2018, Published in IEEE Xplore, 28 June 2018, Electronic ISBN: 978-1-5386-0807-4, DVD ISBN: 978-1-5386-0806-7, Print on Demand (PoD) ISBN: 978-1-5386-0808-1, INSPEC Accession Number: 17894421, DOI: 10.1109/ICISC.2018.8398870 https://ieeexplore.ieee.org/abstract/document/8398870/. [14] A. C. Menezes, A. Cripps, R. A. Buswell, J. Wright and D. Bouchlaghem: Estimating the energy consumption and power demand of small power equipment in office buildings, Energy & Buildings, Vol. 75, June 2014, Pages 199-209, https://doi.org/10.1016/j.enbuild.2014.02.011. https://www.sciencedirect.com/science/article/pii/S0378778814001224. [15] Philip Sedgwick: Standard deviation versus standard error, BMJ 2011; 343: d8010, Endgames Statistical Question, (Published 13 December 2011), https://www.bmj.com/content/343/bmj.d8010. doi: https://doi.org/10.1136/bmj.d8010. [16] Sara Nada & M. Hamed: Energy pricing in developing countries, Paper 1100869, Pub. Date: September 23, 2014, On Access Library Journal (OALib), Scientific Research Publishing, Vol. 1, No. 6. (1–18). http://www.oalib.com/journal/ DOI: 10.4236/oalib.1100869. [17] Sara Nada & Mohamed Hamed: Economic Service of Electric Energy in Developing Countries: An Egyptian Sample, International Journal of Economics and Business Administration, Vol. 4, No. 2, Jun. 2018, Pub. Date: May 28, 2018, Pages: 21-51, ISSN: 2381-7356 (Print); ISSN: 2381-7364 (Online), Paper No. 70220060_20180320, http://www.aiscience.org/journal/ijeba. [18] K. Muralitharan, R. Sakthivel & R. Vishnuvarthan: Neural network based optimization approach for energy demand prediction in smart grid, Neurocoputing, Vol. 273, 17 January 2018, Pages 199-208, https://doi.org/10.1016/j.neucom.2017.08.017 https://www.sciencedirect.com/science/article/pii/S0925231217313681. [19] J. Torriti: Understanding the timing of energy demand through time use data: Time of the day dependence of social practices, Energy Research & Social Science, Vol. 25, March 2017, Pages 37-47. https://www.sciencedirect.com/science/article/pii/S2214629616302985.