[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 investigates two major titles as trend parameter and the envelope pattern where they are applied to the original population data of the energy consumption of the domestic sector in a mega city, representing the developing countries, to predict the future development of energy requirements. The occupied house consumption is accounted for the purification and modification of original input data. An official sample for the domestic customers at north Cairo (the Capital of Egypt) over 26 years has been comprised in the investigation and analysed. Linearly, a sample for the trend of energy curves is hosted and investigated. The determined performance, for the readings of the reflected trends for energy readings, is discussed and examined. Secondly, envelope characteristics for the energy data (population) are projected along the duration of the data (26 years) while the annual rise rate of domestic energy consumption is assessed and discussed. An envelope performance has been implemented mainly for the maximum values of the model studied as well as for its two parts reflected where the average value for the model and its parts are implanted. The envelope margin has been determined and clarified as the wave covering the initial data. The original values of extracted envelope are managed and modified to reach the persist of function pattern in the drawings while a comparison for the modification is fulfilled. Finally, the trend style is applied for the generated envelope to get the slope of energy growth. The long term of the model is introduced for the accuracy degree of prediction into stages where the time duration stages (26 years) are taken as 4 (1992-1996), 8 (1992-2000), 18 (1992-2010), 23 (1992-2015) and finally the original time duration of 26 years (Jan 1992-Jan 2018). Results of different durations are calibrated to the original 26 years model since it is the most accurate output results. The determined results for the accuracy of prediction are tested either in percentage of the original (26 years) or as a per the introduced system of unit value for each year, in general, sequentially. A neural network is proposed for the prediction of energy development for the domestic consumption.

ANN, Energy Consumption, Development, Domestic, Trend, Envelope, Model Duration, Statistical Analysis

[01] K Lee, Y Kim, H C Sung, J Ryu & S W Jeon (2020): Trend Analysis of Urban Heat Island Intensity According to Urban Area Change in Asian Mega Cities, Sustainability 2020, Vol. 12, No. 1, 112; https://doi.org/10.3390/su12010112, https://www.mdpi.com/2071-1050/12/1/112 [02] M S Nashwan, S Shahid & X Wang (2019): Uncertainty in Estimated Trends Using Gridded Rainfall Data: A Case Study of Bangladesh, received: 14 December 2018, Revised: 2 February 2019, Water J, Vol. 11, No. 2, 349; https://doi.org/10.3390/w11020349, https://www.mdpi.com/2073-4441/11/2/349 [03] M Calzavara, C H Glock, E H Grosse & F Sgarbossa (2019): An integrated storage assignment method for manual order picking warehouses considering cost, workload and posture, International Journal of Production Research, Vol. 57, Issue 8 (2392-2408), Received 12 May 2018, Accepted 27 Aug 2018, Published online: 27 Sep 2018. https://doi.org/10.1080/00207543.2018.1518609 https://www.tandfonline.com/doi/abs/10.1080/00207543.2018.1518609 [04] G Yanga & D L Moyerb (2020): Estimation of nonlinear water-quality trends in high-frequency monitoring data, Science of The Total Environment, Vol. 715, 1 May 2020, 136686, https://doi.org/10.1016/j.scitotenv.2020.136686 https://www.sciencedirect.com/science/article/pii/S0048969720301960 [05] K Tesfay, M Yohannes & S Bayisa (2018): Trend analysis of malaria prevalence in Raya Azebo district, Northern Ethiopia: a retrospective study, BMC Research Notes, Vol. 11, No. 1, p. 900. [06] J Yao, P Wang, G Wang, S Shresth, B Xu & W Suna (2020): Establishing a time series trend structure model to mine potential hydrological information from hydrometeorological time series data, Science of The Total Environment, Vol. 698, 1 January 2020, 134227, https://doi.org/10.1016/j.scitotenv.2019.134227 https://www.sciencedirect.com/science/article/pii/S004896971934210X [07] J Alvarez-Galvez, J A Salinas-Perez, M L Rodero-Cosano & L Salvador-Carulla (2017): Methodological barriers to studying the association between the economic crisis and suicide in Spain, BMC Public Health. 2017; 17: 694. Published online 2017 Sep 6. doi: 10.1186/s12889-017-4702-0, PMID: 28877695, PMCID: PMC5588603, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5588603/ [08] S Nada & M Hamed (2019): Growth analysis for the electric energy curves of domestic consumption in north Cairo zone, Public Science Framework, International Journal of Economics and Business Administration, Vol. 5, No. 1 (1-22), Pub. Date: Mar. 5, 2019, ISSN: 2381-7356 (Print); ISSN: 2381-7364 (Online), http://www.aiscience.org/journal/ijeba http://www.aiscience.org/journal/allissues/ijeba.html?issueId=70220501 [09] https://en.wikipedia.org/wiki [10] A Mosavi & A Bahmani (2019): Energy consumption prediction using machine learning; a review, (www.preprints.org) Posted: 11 March 2019 (1-61), doi: 10.20944/preprints201903.0131.v1 [11] L Zhang, Z F Fan, D W Liu, M G Zhou, Z Q Wang & M Li (2020): Trend analysis on the disease burden related to cirrhosis and other chronic liver diseases caused by hepatitis B, in China, from 1990 to 2016, Zhonghua Liu Xing Bing Xue Za Zhi, 2020 Feb 10; Vol. 41, No. 2 (173-177). doi: 10.3760/cma.j.issn.0254-6450.2020.02.007. https://www.ncbi.nlm.nih.gov/pubmed/32164125 [12] H Azamathulla, U Rathnayake, & A Shatnawi (2018): Gene expression programming and artificial neural network to estimate atmospheric temperature in Tabuk, Saudi Arabia, Applied Water Science, Vol. 8, No. 6 (184–190). [13] T L Guihua, S Chupeng, Y Kai, L Shengzhong, M L Huang & W Luo (2018): Source apportionment and source-to-sink transport of major and trace elements in coastal sediments: Combining positive matrix factorization and sediment trend analysis. https://doi.org/10.1016/j.scitotenv.2018.09.198 https://www.sciencedirect.com/science/article/pii/S004896971833643X [14] G L Baiyu, C Liping, W Shuaifang, Z Kuangyuan & Z X Lei (2019): Wave height statistical characteristic analysis, Journal of Oceanology and Limnology, First Online: 6 May 2018, March 2019, Vol. 37, Issue 2 (448–460). [15] Y S Güçlü, E Şişman & İ Dabanlı (2020): Innovative triangular trend analysis, Arabian Journal of Geosciences, Vol. 13, Article number: 27 (2020), https://link.springer.com/article/10.1007/s12517-019-5048-y [16] S Razavi & R Vogel (2018): Pre whitening of hydroclimatic time series? Implications for inferred change and variability across time scales,” Journal of Hydrology, Vol. 557 (109–115). [17] Z S En (2017): Innovative trend significance test and applications, Theoretical & Applied Climatology, Vol. 127, No. 3-4 (939–947). [18] A Oztopal & Z S En (2016): Innovative trend methodology applications to precipitation records in Turkey, Water Resources Management, Vol. 31, No. 3 (727–737). [19] K Glenk, R J. Johnston, J Meyerhoff & J Sagebiel (2020): Spatial Dimensions of Stated Preference Valuation in Environmental and Resource Economics: Methods, Trends and Challenges, Environmental and Resource Economics, Vol. 75 (215–242) https://link.springer.com/article/10.1007/s10640-018-00311-w [20] Alaa A. J. Al-Hasani (2020): Trend analysis and abrupt change detection of streamflow variations in the lower Tigris River Basin, Iraq, International Journal of River Basin Management, Received 05 May 2019, Accepted 27 Jan 2020, Accepted author version posted online: 31 Jan 2020, Published online: 16 Feb 2020, https://doi.org/10.1080/15715124.2020.1723603, https://www.tandfonline.com/doi/full/10.1080/15715124.2020.1723603 [21] M Rudolf, R Perera, D Swanston, J Burberry, K Roberts & S Jebb (2019): Observational analysis of disparities in obesity in children in the UK: Has Leeds bucked the trend? First published: 25 April 2019 https://doi.org/10.1111/ijpo.12529 https://onlinelibrary.wiley.com/doi/full/10.1111/ijpo.12529 [22] Petrophysical properties, book chapter (2018), Geo Science World, https://pubs.geoscienceworld.org/books/book/2072/chapter/114343105/Petrophysical-properties [23] E. H. Rutter; J. Mecklenburgh & K. Taylor (2017): Geomechanical and Petrophysical Properties of Mudrocks, GEOLOGICAL SOCIETY, LONDON, SPECIAL PUBLICATIONS, Vol. 454, DOI: https://doi.org/10.1144/SP454, ISBN electronic: 978-1-78620-335-9, Publication date: January 01, 2017. [24] S Nada & M Hamed (2018): Economic Service of Electric Energy in Developing Countries: An Egyptian Sample, International Journal of Economics and Business Administration, Vol. 4, No. 2 (21-51), Pub. Date: May 28, 2018, ISSN: 2381-7356 (Print); ISSN: 2381-7364 (Online), Paper No. 70220060_20180320, http://www.aiscience.org/journal/ijeba http://www.aiscience.org/journal/paperInfo/ijeba?paperId=3821 [25] T Pettinger (2019): Diagrams of Cost Curves, January 11, 2019, https://www.economicshelp.org/blog/189/economics/diagrams-of-cost-curves/ [26] C L Chan & K L Ting (2020): Curvature Theory on Contact and Transfer Characteristics of Enveloping Curves, J. of Mechanisms & Robotics, Feb 2020, Vol. 12, No. (1): 011018 (10 pages), Paper No: JMR-19-1058 https://doi.org/10.1115/1.4045247, https://asmedigitalcollection.asme.org/mechanismsrobotics/article-abstract/12/1/011018/1065792/Curvature-Theory-on-Contact-and-Transfer?redirectedFrom=fulltext [27] Jin-Young Kim & Sung-Bae Cho (2019): Electric Energy Consumption Prediction by Deep Learning with State Explainable Autoencoder, Energies 2019 (MDPI), Vol. 12, No. 739 (1-14), Received: 3 February 2019; Accepted: 19 February 2019; Published: 22 February 2019, doi: 10.3390/en12040739 www.mdpi.com/journal/energies [28] S Nada & M Hamed (2019): Grouping Analysis for the Energy Consumption of Domestic Loads in the Distribution Network at North Cairo Zone, International Journal of Economics and Business Administration,, Public Science Framework, paper No. 70220078_20190306, Vol. 5, No. 2 (85-97), ISSN: 2381-7356 (Print); ISSN: 2381-7364 (Online), http://www.aiscience.org/journal/ijeba http://www.aiscience.org/journal/paperInfo/ijeba?paperId=4454