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Kısa Vadeli Elektrik Tüketiminin Sıcaklığa Bağlı Yapay Sinir Ağları ile Tahmini

Year 2018, Issue: 14, 393 - 398, 31.12.2018
https://doi.org/10.31590/ejosat.407229

Abstract



Günümüzde ülkelerin gelişmişlik seviyelerinin en önemli göstergelerinden biri enerji tüketimleridir. Sosyal ve ekonomik kalkınmanın en önemli girdisi olan enerji, yaşam standardının artması ve sürdürülebilir kalkınma sağlanabilmesi için zorunlu ihtiyaçtır. Elektrik enerjisi kolay kullanımı, kolay taşınması ve temiz bir enerji olması nedeniyle en çok tercih edilen ve tüketilen enerji türlerinden biri olmuştur. Elektrik tüketimi nüfus, ekonomik büyüme ve gayri safi yurtiçi hâsıla gibi çeşitli sosyal ve ekonomik değişkenlerin yanı sıra sıcaklık, yağış ve nem gibi iklimsel değişkenlere de bağlı değişiklik göstermektedir. Isınma ve soğuma ihtiyacı için kullanılan elektriğin, elektrik tüketimindeki etkisi büyüktür. Hava şartları elektrik tüketiminde artış ve azalışa neden olurken, etkisi en yüksek meteorolojik değişken sıcaklıktır. Konfor sıcaklığı kabul edilen sıcaklık aralığından uzaklaşıldıkça elektrik tüketimi de artmaktadır. Çalışmada Türkiye elektrik tüketiminin sıcaklık ile ilişkisi incelenmiştir. Aylık ve mevsimlik zaman periyodlarında tüketimin sıcaklık nedeniyle ne yönde ve ne kadar değiştiği araştırılmış, tüketim tahmin modeline sıcaklık girdi olarak eklenerek daha tutarlı tüketim tahmini yapabilmek amaçlanmıştır. Çalışma kapsamında Yapay Sinir Ağı yöntemi ve Levenberg-Marquardt geriye yayılma algoritması ile modellenen veri grupları kullanılarak kısa dönemli elektrik tüketim tahmini yapılmıştır. Çalışma aralığı olarak Ocak 2012-Kasım 2016 yılları belirlenmiştir. Bu yıllara ait Türkiye toplam tüketim verisi ve sıcaklık verisi kullanılmıştır. Sıcaklık verisi, tüketim miktarına göre ağırlıklandırılmış ortalama ile üretilmiştir. Türkiye tüketiminde en çok paya sahip 12 ilin sıcaklıkları, tüketim oranlarına göre ağırlıklandırılmıştır. 

References

  • A. M. Ashraful, H. H. Masjuki, M. A. Kalam, I. M. Rizwanul Fattah, S. Imtenan, S. A. Shahir and H. M. Mobarak, "Production and comparison of fuel properties, engine performance, and emission characteristics of biodiesel from various non-edible vegetable oils: A review," Energy Conversion and Management, vol. 80, no. 202-228, 2014.
  • A. S. Khwaja, X. Zhang,, A. Anpalagan and B. Venkatesh, “Boosted neural networks for improved short-term electric load forecasting,” Electric Power Systems Research, p. 431–437, 2017.
  • A. Azadeh, S. F. Ghaderi and S. Sohrabkhani, "Annual Electricity Consumption Forecasting By Neural Network İn High Energy Consuming İndustrial Sectors of Iran," Energy Conversion and Management, no. 49, p. 2272–2278, 2008.
  • EPDK, "Elektrik Piyasası 2015 Yılı Piyasa Gelişim Raporu," T.C. Enerji Piyasası Düzenleme Kurumu (EPDK), Ankara, Türkiye. 2016
  • E. Almeshaiei and H. Soltan, "A methodology for Electric Power Load Forecasting," Alexandria Engineering Journal, no. 50, p. 137–144, 2011. EPDK, "Elektrik Piyasası 2015 Yılı Piyasa Gelişim Raporu," T.C. Enerji Piyasası Düzenleme Kurumu (EPDK), Ankara, Türkiye. 2016.
  • G. Aneiros, J. Vilar and P. Raña, "Short-term forecast of daily curves of electricity demand and price," Electrical Power and Energy Systems, no. 80, pp. 96-108, 2016.
  • H. Toros, R. Ayaz, A. Ajder, A. Durusu and H. Yıldırım, "Online Load Shifting Of Electricity Production-Consumption For Reducing Environmental Hazard," European Journal of Science and Technology (EJOSAT), vol. 1, no. 2, pp. 39-42, 2013.
  • J. Boylan, “Toward a More Precise Definition of Forecastabilitiy," Business Forecasting, SAS Institute Inc., Cary, North Carolina, USA.
  • J. C. Lam, "Climatic and economic influences on residential electricity consumption," Energy Conversion and Management, vol. 39, no. 7, pp. 623-629, 1998.
  • J. Moral-Carcedo and J. PerezüGarcia, "Temperature effects on firms’ electricity demand: An analysis of sectorial differences in Spain," Applied Energy, no. 142, p. 407–425, 2015.
  • L. Friedrich and A. Afshari, "Short-term forecasting of the Abu Dhabi electricity load using," Energy Procedia, no. 75, pp. 3014-3026, 2015.
  • S. Yıldız, "Türkiye Elektrik Piyasası Kısa Dönemli Referans Fiyat Tahmini,” İTÜ, Enerji Enstitüsü, İstanbul, 2015.

Prediction of Short-Term Electricity Consumption by Artificial Neural Networks Using Temperature Variables

Year 2018, Issue: 14, 393 - 398, 31.12.2018
https://doi.org/10.31590/ejosat.407229

Abstract

Today, energy consumption is one of the most important indicators of countries' development levels. Energy, the most important input of social and economic development, is a necessary requirement for the increase of living standard and sustainable development. Electricity is one of the most preferred and consumed energy types because of easy use, easy transportation and clean energy. Electricity consumption varies depending on various social and economic variables such as population, economic growth and gross domestic product as well as on climatic variables such as temperature, precipitation and humidity. The electricity used for heating and cooling needs is bigger in electricity consumption. Weather conditions cause increase and decrease in electricity consumption. Temperature is the meteorological variable with the highest effect. As the comfort temperature gets away from the accepted temperature range, the electricity consumption also increases. The balance of electricity production and consumption is also important in terms of atmospheric disasters. In study, the relation of Turkish electricity consumption with temperature was examined between January 2012 and November 2016. In monthly and seasonal time periods, it was researched how the consumption was changed due to the temperature and how much it was changed and it was aimed to make a more consistent consumption estimation by adding as a temperature input to the consumption estimation model. In the study, short-term electricity consumption estimations were made by using Artificial Neural Network (ANN) method and data groups modeled by Levenberg-Marquardt backpropagation algorithm on MATLAB proglaming language. The temperature data is produced with a weighted average by consumption amount. The temperatures of 12 provinces with the largest share of consumption in Turkey are weighted according to their consumption rates. In the model weighting, the percentage of effect 1 day before is more than 1 week before and 1 week before is more than 1 year before. For this reason, deviations in the recent history more influence the model. Model results show that, error rates are considered to be reasonable. It is planned to establish the model on a regional basis for future work. When estimating regional electricity demand, a model can be developed by using different meteorological variables. It is predicted that rainfall data will increase the performance of estimates of the inclusion of temperature data in the Bosphorus region, which has a high population density and a high level of residential consumption.

References

  • A. M. Ashraful, H. H. Masjuki, M. A. Kalam, I. M. Rizwanul Fattah, S. Imtenan, S. A. Shahir and H. M. Mobarak, "Production and comparison of fuel properties, engine performance, and emission characteristics of biodiesel from various non-edible vegetable oils: A review," Energy Conversion and Management, vol. 80, no. 202-228, 2014.
  • A. S. Khwaja, X. Zhang,, A. Anpalagan and B. Venkatesh, “Boosted neural networks for improved short-term electric load forecasting,” Electric Power Systems Research, p. 431–437, 2017.
  • A. Azadeh, S. F. Ghaderi and S. Sohrabkhani, "Annual Electricity Consumption Forecasting By Neural Network İn High Energy Consuming İndustrial Sectors of Iran," Energy Conversion and Management, no. 49, p. 2272–2278, 2008.
  • EPDK, "Elektrik Piyasası 2015 Yılı Piyasa Gelişim Raporu," T.C. Enerji Piyasası Düzenleme Kurumu (EPDK), Ankara, Türkiye. 2016
  • E. Almeshaiei and H. Soltan, "A methodology for Electric Power Load Forecasting," Alexandria Engineering Journal, no. 50, p. 137–144, 2011. EPDK, "Elektrik Piyasası 2015 Yılı Piyasa Gelişim Raporu," T.C. Enerji Piyasası Düzenleme Kurumu (EPDK), Ankara, Türkiye. 2016.
  • G. Aneiros, J. Vilar and P. Raña, "Short-term forecast of daily curves of electricity demand and price," Electrical Power and Energy Systems, no. 80, pp. 96-108, 2016.
  • H. Toros, R. Ayaz, A. Ajder, A. Durusu and H. Yıldırım, "Online Load Shifting Of Electricity Production-Consumption For Reducing Environmental Hazard," European Journal of Science and Technology (EJOSAT), vol. 1, no. 2, pp. 39-42, 2013.
  • J. Boylan, “Toward a More Precise Definition of Forecastabilitiy," Business Forecasting, SAS Institute Inc., Cary, North Carolina, USA.
  • J. C. Lam, "Climatic and economic influences on residential electricity consumption," Energy Conversion and Management, vol. 39, no. 7, pp. 623-629, 1998.
  • J. Moral-Carcedo and J. PerezüGarcia, "Temperature effects on firms’ electricity demand: An analysis of sectorial differences in Spain," Applied Energy, no. 142, p. 407–425, 2015.
  • L. Friedrich and A. Afshari, "Short-term forecasting of the Abu Dhabi electricity load using," Energy Procedia, no. 75, pp. 3014-3026, 2015.
  • S. Yıldız, "Türkiye Elektrik Piyasası Kısa Dönemli Referans Fiyat Tahmini,” İTÜ, Enerji Enstitüsü, İstanbul, 2015.
There are 12 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Hüseyin Toros

Derya Aydın This is me

Publication Date December 31, 2018
Published in Issue Year 2018 Issue: 14

Cite

APA Toros, H., & Aydın, D. (2018). Prediction of Short-Term Electricity Consumption by Artificial Neural Networks Using Temperature Variables. Avrupa Bilim Ve Teknoloji Dergisi(14), 393-398. https://doi.org/10.31590/ejosat.407229