Kısa Vadeli Elektrik Tüketiminin Sıcaklığa Bağlı Yapay Sinir Ağları ile Tahmini

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 programing 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.

Dinamik Tüketici Talep Yönetimi Yapabilen Blokzincir/Kripto Para Tabanlı Elektrik Piyasası İşletme Modeli

“…(Bianco, Manca, & Nardini, 2009;Kumar & Jain, 2010;McLoughlin, Duffy, & Conlon, 2013;Wolde-Rufael, 2006) Son yıllarda ise verilerin artması ve hesaplama güçlerinin bulut bilişim, GPU'lu özel hesaplama makinaları, yüksek performanslı hesaplama sistemlerinin sağladığı hesaplama gücü ile makine öğrenmesi ve derin öğrenme gibi yapay sinir ağları kullanarak yapılan birçok çalışma bulunmaktadır. (Berges, Goldman, Matthews, & Soibelman, 2009;Dong, Li, Rahman, & Vega, 2016;Hamzaçebi, 2007;Kaytez, Taplamacioglu, Cam, & Hardalac, 2015;Rahman, Srikumar, & Smith, 2018;Toros & Aydın, 2018) Ancak bu yapılan çalışmaların büyük çoğunluğu mevcut ve geçmiş verileri kullanarak tahmin yapma ve model oluşturma üzerine kurgulanmıştır. Fiyatlandırma politikaları ve farklı tarife modelleri ile ilgili olarak da yine benzer şekilde yapılmış birçok araştırma ve çalışma bulunmaktadır.…”