Genelleştirilmiş regresyon modelleri kullanılarak enerji talebi ve tüketimi için bir modelleme yaklaşımı

Year 2023, Volume: 11 Issue: 3, 715 - 729, 01.09.2023

Fuad Alhaj Omar

https://doi.org/10.36306/konjes.1217013

Abstract

Enerji yönetimi, mevcut enerji kaynaklarını korumak ve evlerin temel enerji ihtiyaçlarını karşılamak için önemli bir süreçtir. Pek çok çalışma, yük talebini yönetmek ve enerji maliyetlerini en aza indirmek için hanehalkı enerji tüketim modellerini optimize etmeyi amaçlamaktadır. Çatışmalardan etkilenen ülkelerde bu tür optimizasyonlarını benimsenmesi, sınırlı enerji kaynakları nedeniyle daha faydalıdır. Bu çalışma, Suriye'nin kuzeyindeki haneler için en uygun enerji tüketim modelini belirlemektedir. Amaç, enerji fiyatlarını, ortalama aylık hane gelirini, ana elektrik kaynağını, batarya depolama kapasitesini ve alan ısıtma, su ısıtma ve pişirme için kullanılan enerjiyi dikkate alarak en uygun maliyetli enerji kaynaklarını belirlemektir. Yüz otuz altı (136) standartlaştırılmış mesken hane anketi toplandı ve bir test vakası olarak kullanıldı. Verilerin istatistiksel analizi, R-Studio yazılımı kullanılarak yapıldı; Poisson regresyon ve negatif binom regresyon kullanıldı. Bulgular, kullanılan Negatif Binom (NB) modelinin açıklama gücünün yüksek olduğunu ortaya koymuştur. Ayrıca alan ısıtma ve su ısıtma için kullanılan enerji kaynakları da aylık harcamalara doğrudan etki etmektedir. Üretilen model, en uygun maliyetli enerji kaynaklarının alan ısıtma için kömür ve su ısıtma için doğal gaz ve gazyağı olduğunu göstermiştir.

Keywords

Enerji Verimliliği, Enerji Yönetimi, Enerjiyi Optimizasyonu, Poisson regresyonu, Negatif binom regresyonu.

MODELLING APPROACH FOR ENERGY DEMAND AND CONSUMPTION USING GENERALIZED LINEAR MODELS

Abstract

Energy management is an important process for maintaining available energy resources and meeting basic household energy needs. Many studies seek to optimize the household energy consumption patterns to manage the load demand and minimize energy costs. Adopting such optimizations in conflict-affected countries is more beneficial due to limited energy sources. This study identifies the optimal energy consumption model for households in northern Syria. The objective is to identify the most cost-efficient energy sources while considering the prices, average monthly household income, the main source of electricity, battery storage capacity, and energy demands for space heating, water heating and cooking. One hundred and thirty-six (136) standardized surveys of residential households are collected and used as a test case. Statistical analysis of the data was carried out using the R-Studio software, where Poisson regression and negative binomial regression were employed. Findings revealed that the Negative Binomial (NB) model used has high explanatory power. In addition, the energy sources used for space heating and water heating have a direct impact on monthly expenditures. The produced model showed that the most cost-effective energy sources are coal for space heating and natural gas and kerosene for water heating.

Keywords

Energy Efficient, Energy Management, Optimizing Energy, Poisson Regression, Negative Binomial Regression

References

• S. Ri, A. H. Blair, C. J. Kim, and R. J. Haar, "Attacks on healthcare facilities as an indicator of violence against civilians in Syria: an exploratory analysis of open-source data," PLoS One, vol. 14, no. 6, p. e0217905, 2019.
• L. Tichý, "Energy infrastructure as a target of terrorist attacks from the Islamic state in Iraq and Syria," International Journal of Critical Infrastructure Protection, vol. 25, pp. 1-13, 2019.
• S. Jabbour et al., "10 years of the Syrian conflict: a time to act and not merely to remember," The Lancet, vol. 397, no. 10281, pp. 1245-1248, 2021.
• F. A. Omar, I. Mahmoud, A. Hussian, L. Mohr, H. O. Abdullah, and A. Farzat, "The effect of the Syrian crisis on electricity supply and the household life in North-West Syria: a university-based study," Education and Conflict Review, vol. 3, pp. 77-86, 2020.
• F. Alhaj Omar, I. Mahmoud, and K. G. Cedano, "Energy poverty in the face of armed conflict: The challenge of appropriate assessment in wartime Syria," Energy Research & Social Science, vol. 95, p. 102910, 2023/01/01/ 2023, doi: https://doi.org/10.1016/j.erss.2022.102910.
• J. Messner, "Fragile States Index 2017: Factionalization and Group Grievance Fuel Rise in Instability. The Fund for Peace," ed, 2017.
• C. International Committee of the Red, Urban services during protracted armed conflict: a call for a better approach to assisting affected people. International Committee of the Red Cross, 2015.
• S. Gates, H. Hegre, H. M. Nygard, and H. Strand, "Consequences of armed conflict in the Middle East and north Africa region," mimeo, 2010.
• I. P. Conflict and H. Action, "some recent ICRC experiences," Geneva: ICRC, 2016.
• C. Bennett, M. Foley, and S. Pantuliano, "Time to let go: Remaking humanitarian action for the modern era," ODI, London, 2016.
• P. Collier, "Fragile States and International Support," Development, vol. 175, 2016.
• E. Spyrou, B. F. Hobbs, M. D. Bazilian, and D. Chattopadhyay, "Planning power systems in fragile and conflict-affected states," Nature energy, vol. 4, no. 4, pp. 300-310, 2019.
• P. B. Anand, Getting infrastructure priorities right in post-conflict reconstruction. WIDER Research Paper, 2005.
• J. Goodhand, "Violent conflict, poverty and chronic poverty," Chronic Poverty Research Centre Working Paper, no. 6, 2001.
• K. Lyra, S. Mirasgedis, and C. Tourkolias, "From measuring fuel poverty to identification of fuel poor households: a case study in Greece," Energy Efficiency, vol. 15, no. 1, p. 6, 2022/01/07 2022, doi: 10.1007/s12053-021-10017-6.
• A. Acakpovi, G. Botwe-Ohenewaa, and D. M. Sackey, "Impact of energy efficiency and conservation programs on the national grid in some selected households in Ghana," Energy Efficiency, vol. 15, no. 1, p. 5, 2022/01/06 2022, doi: 10.1007/s12053-021-09998-1.
• S. Oguah and D. Chattopadhyay, "Planning in fragile and conflict states:: Case study for West Bank and Gaza," in 2018 IEEE Power & Energy Society General Meeting (PESGM), 2018: IEEE, pp. 1-5.
• E. L. Roach and M. Al-Saidi, "Rethinking infrastructure rehabilitation: Conflict resilience of urban water and energy supply in the Middle East and South Sudan," Energy Research & Social Science, vol. 76, p. 102052, 2021.
• M. Labordena, A. Patt, M. Bazilian, M. Howells, and J. Lilliestam, "Impact of political and economic barriers for concentrating solar power in Sub-Saharan Africa," Energy Policy, vol. 102, pp. 52-72, 2017.
• T. Pettersson and P. Wallensteen, "Armed conflicts, 1946–2014," Journal of peace research, vol. 52, no. 4, pp. 536-550, 2015.
• H. Zerriffi, H. Dowlatabadi, and N. Strachan, "Electricity and conflict: Advantages of a distributed system," The Electricity Journal, vol. 15, no. 1, pp. 55-65, 2002.
• M. Bazilian and D. Chattopadhyay, "Considering power system planning in fragile and conflict states," Energy for Sustainable Development, vol. 32, pp. 110-120, 2016.
• N. Patankar, A. R. de Queiroz, J. F. DeCarolis, M. D. Bazilian, and D. Chattopadhyay, "Building conflict uncertainty into electricity planning: A South Sudan case study," Energy for Sustainable Development, vol. 49, pp. 53-64, 2019.
• J. L. Sowers, E. Weinthal, and N. Zawahri, "Targeting environmental infrastructures, international law, and civilians in the new Middle Eastern wars," Security Dialogue, vol. 48, no. 5, pp. 410-430, 2017.
• P. Justino, "Violent conflict and human capital accumulation," IDS Working Papers, vol. 2011, no. 379, pp. 1-17, 2011.
• H. Sun, S. Lu, and S. Solaymani, "Impacts of oil price uncertainty on energy efficiency, economy, and environment of Malaysia: stochastic approach and CGE model," Energy Efficiency, vol. 14, no. 2, p. 21, 2021/02/04 2021, doi: 10.1007/s12053-020-09924-x.
• A. O’Sullivan, M.-E. Rey, and J. G. Mendez, "Opportunities and Challenges in the MENA Region," Arab world competitiveness report, vol. 2012, pp. 42-67, 2011.
• N. R. C. Idmc, "Global Report on Internal Displacement 2019," ed, 2019.
• T. A. A. People, "URBAN SERVICES DURING PROTRACTED ARMED CONFLICT," 2015.
• C. International Committee of the Red, "Bled Dry: How War in the Middle East is Bringing the Region’s Water Supplies to Breaking Point," 2015: ICRC Geneva.
• N. R. Draper and H. Smith, Applied regression analysis. John Wiley & Sons, 1998.
• A. O. Sykes, "An introduction to regression analysis," 1993.
• D. C. Montgomery, E. A. Peck, and G. G. Vining, Introduction to linear regression analysis. John Wiley & Sons, 2021.
• A. M. Legendre, Nouvelles méthodes pour la détermination des orbites des comètes; par AM Legendre. chez Firmin Didot, libraire pour lew mathematiques, la marine, l …, 1806.
• C. F. Gauss, Theoria combinationis observationum erroribus minimis obnoxiae. H. Dieterich, 1823.
• F. Galton, "Regression towards mediocrity in hereditary stature," The Journal of the Anthropological Institute of Great Britain and Ireland, vol. 15, pp. 246-263, 1886.
• S. Weisberg, Applied linear regression. John Wiley & Sons, 2005.
• U. Olsson, "Generalized linear models," An applied approach. Studentlitteratur, Lund, vol. 18, 2002.
• J. Fox, Applied regression analysis and generalized linear models. Sage Publications, 2015.
• Ö. Deniz, "Poisson regresyon analizi," İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi, vol. 4, no. 7, pp. 59-72, 2005.
• R. Winkelmann, Econometric analysis of count data. Springer Science & Business Media, 2008.
• P. De Jong and G. Z. Heller, "Generalized linear models for insurance data," Cambridge Books, 2008.
• A. J. Dobson and A. G. Barnett, An introduction to generalized linear models. CRC press, 2018.
• A. Agresti, Analysis of ordinal categorical data. John Wiley & Sons, 2010.
• J. M. Hilbe, Negative binomial regression. Cambridge University Press, 2011.
• A. C. Cameron and P. K. Trivedi, Regression analysis of count data. Cambridge university press, 2013.
• C. Walck, "Hand-book on statistical distributions for experimentalists," University of Stockholm, vol. 10, pp. 96-01, 2007.
• K. J. Hastings, Probability and statistics. Addison-Wesley Reading, MA, 1997.
• E. H. Payne, J. W. Hardin, L. E. Egede, V. Ramakrishnan, A. Selassie, and M. Gebregziabher, "Approaches for dealing with various sources of overdispersion in modeling count data: Scale adjustment versus modeling," Statistical methods in medical research, vol. 26, no. 4, pp. 1802-1823, 2017.