The effect of behavior changes caused by the covid-19 pandemic on electricity consumptions and feeder loads: a case study on an electricity distribution feeder

Hüseyin Öztürk; Kıvanç Başaran

Makale detayları

The effect of behavior changes caused by the covid-19 pandemic on electricity consumptions and feeder loads: a case study on an electricity distribution feeder

Yazarlar:

Hüseyin Öztürk | Kıvanç Başaran

Yayıncı:

Pamukkale University

Ana Başlıklar:

Elektrik Mühendisliği (Diğer)

Dergi:

Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi Vol.29 Issue 5, pp.451-457

Belge Tipi:

Makale

Yayın Tarihi:

10/2023

Belge Dili:

Türkçe

Document ID (Org No.):

2001303392

pISSN:

2147-5881

Anahtar kelimeler:

Özet:

 The COVID-19 Sars CoV-2 virus, which emerged in Wuhan city of Hubei province of China in December 2019, affected the whole world in a short time and was declared a global epidemic by the World Health Organization WHO as of March 11, 2020. After this date, closure measures have been implemented all over the world to prevent the spread of the virus. Due to the provisionstaken, there have been changes in electrical energy consumption compared to previous years. In March, April and May 2020, when the restrictions affected human life the most, dynamic changes occurred in energy demand all over the world. This has affected international energy markets, energy production and grid load planning. Although the total electricity consumption in Turkey increased compared to the previous year, there was a decrease in the consumption in the commercial tariff. In this study, the effects of the COVID-19 pandemic on electricity consumption were analyzed by analyzing the electricity consumption of Turkey and İzmir, depending on the tariffs, based on time. A case study was conducted on an electricity distribution feeder to see the impact of COVID-19 on electricity distribution networks. For the case study, an electricity distribution feeder with 99 of the subscriber density in the residential and commercial tariff group was selected. For the feeder, load forecasting was made using artificial neural networks machine learning method according to 2018, 2019 and 2020 data. In the load forecasting study, 75 of the data was selected for learning and 25 for testing. As a result of the study, the actual and forecasted load data of 2020 were compared. The effects of the COVID-19 pandemic on the lad of an electricity distribution feeder were investigated. In the study, the best performance values of load forecasting were found mse as 0.0024 and R2 as 0.83.

 The COVID-19 Sars CoV-2 virus, which emerged in Wuhan city of Hubei province of China in December 2019, affected the whole world in a short time and was declared a global epidemic by the World Health Organization WHO as of March 11, 2020. After this date, closure measures have been implemented all over the world to prevent the spread of the virus. Due to the provisionstaken, there have been changes in electrical energy consumption compared to previous years. In March, April and May 2020, when the restrictions affected human life the most, dynamic changes occurred in energy demand all over the world. This has affected international energy markets, energy production and grid load planning. Although the total electricity consumption in Turkey increased compared to the previous year, there was a decrease in the consumption in the commercial tariff. In this study, the effects of the COVID-19 pandemic on electricity consumption were analyzed by analyzing the electricity consumption of Turkey and İzmir, depending on the tariffs, based on time. A case study was conducted on an electricity distribution feeder to see the impact of COVID-19 on electricity distribution networks. For the case study, an electricity distribution feeder with 99 of the subscriber density in the residential and commercial tariff group was selected. For the feeder, load forecasting was made using artificial neural networks machine learning method according to 2018, 2019 and 2020 data. In the load forecasting study, 75 of the data was selected for learning and 25 for testing. As a result of the study, the actual and forecasted load data of 2020 were compared. The effects of the COVID-19 pandemic on the lad of an electricity distribution feeder were investigated. In the study, the best performance values of load forecasting were found mse as 0.0024 and R2 as 0.83.

 The COVID-19 Sars CoV-2 virus, which emerged in Wuhan city of Hubei province of China in December 2019, affected the whole world in a short time and was declared a global epidemic by the World Health Organization WHO as of March 11, 2020. After this date, closure measures have been implemented all over the world to prevent the spread of the virus. Due to the provisionstaken, there have been changes in electrical energy consumption compared to previous years. In March, April and May 2020, when the restrictions affected human life the most, dynamic changes occurred in energy demand all over the world. This has affected international energy markets, energy production and grid load planning. Although the total electricity consumption in Turkey increased compared to the previous year, there was a decrease in the consumption in the commercial tariff. In this study, the effects of the COVID-19 pandemic on electricity consumption were analyzed by analyzing the electricity consumption of Turkey and İzmir, depending on the tariffs, based on time. A case study was conducted on an electricity distribution feeder to see the impact of COVID-19 on electricity distribution networks. For the case study, an electricity distribution feeder with 99 of the subscriber density in the residential and commercial tariff group was selected. For the feeder, load forecasting was made using artificial neural networks machine learning method according to 2018, 2019 and 2020 data. In the load forecasting study, 75 of the data was selected for learning and 25 for testing. As a result of the study, the actual and forecasted load data of 2020 were compared. The effects of the COVID-19 pandemic on the lad of an electricity distribution feeder were investigated. In the study, the best performance values of load forecasting were found mse as 0.0024 and R2 as 0.83.

 Aralık 2019da Çinin Hubey eyaletinin Wuhan kentinde ortaya çıkan COVİD-19 Sars CoV-2 virüsü kısa sürede tüm dünyayı etkisi altına almış ve Dünya Sağlık Örgütü WHO tarafından 11 Mart 2020 tarihi itibariyle küresel salgın ilan edilmiştir. Bu tarihten sonra virüsün yayılımı önlemek için tüm dünyada kapanma tedbirleri uygulanmaya başlanmıştır. Getirilen tedbirler nedeniyle elektrik enerjisi tüketiminde önceki yıllara göre değişimler yaşanmıştır. Kısıtlamaların insan hayatını en çok etkilediği 2020 yılı Mart, Nisan ve Mayıs aylarında tüm dünyada enerji talebinde dinamik değişimler meydana gelmiştir. Bu durum uluslararası enerji piyasalarını, enerji üretimi ve şebeke yük planlamalarını etkilemiştir. Türkiyede toplam elektrik tüketimi bir önceki yıla göre artmasına rağmen ticarethane tarifesindeki tüketimlerde azalış meydana gelmiştir. Bu çalışmada, Türkiye ve İzmir ilinin elektrik tüketimlerinin tarifeler özelinde zamana bağlı olarak analizi yapılarak COVİD-19 salgınının elektrik tüketimi üzerindeki etkileri incelenmiştir. COVİD-19un elektrik dağıtım şebekeleri üzerindeki etkisini görmek için ise bir elektrik dağıtım fideri üzerinde vaka çalışması yapılmıştır. Vaka çalışması için abone yoğunluğunun 99u mesken ve ticarethane tarife grubunda yer alan bir elektrik dağıtım fideri seçilmiştir. Fider için 2018, 2019 ve 2020 yılı verilerine göre yapay sinir ağları makine öğrenmesi yöntemi kullanılarak yük tahmini yapılmıştır. Yapılan yük tahmin çalışmasında, verilerin 75i öğrenme için, 25i ise test için seçilmiştir. Çalışma sonucunda 2020 yılı gerçek ve tahmin yük verileri karşılaştırılmıştır. COVİD-19 pandemisinin bir elektrik dağıtım fiderinin yükü üzerindeki etkileri incelenmiştir. Çalışmada, yük tahmininin en iyi performans değerleri mse, 0.0024 ve R2 , 0.83 olarak bulunmuştur.

Makale detayları

The effect of behavior changes caused by the covid-19 pandemic on electricity consumptions and feeder loads: a case study on an electricity distribution feeder

Yazarlar:

Hüseyin Öztürk | Kıvanç Başaran

Yayıncı:

Pamukkale University

Ana Başlıklar:

Elektrik Mühendisliği (Diğer)

Dergi:

Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi Vol.29 Issue 5, pp.451-457

Belge Tipi:

Makale

Yayın Tarihi:

10/2023

Belge Dili:

Türkçe

Document ID (Org No.):

2001303392

pISSN:

2147-5881

COVID-19

Load forecasting

Electricity distribution grid

Artificial neural networkCOVİD-19

Yük tahmini

Elektrik dağıtım şebekesi

Yapay sinir ağları

Sayı

Makale detayları

Yazarlar: Hüseyin Öztürk | Kıvanç Başaran

Yayıncı: Pamukkale University

Ana Başlıklar: Elektrik Mühendisliği (Diğer)

Dergi: Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi Vol.29 Issue 5, pp.451-457

Belge Tipi: Makale

Yayın Tarihi: 10/2023

Belge Dili: Türkçe

Document ID (Org No.): 2001303392

pISSN: 2147-5881

Sayı

Yazarlar:

Hüseyin Öztürk | Kıvanç Başaran

Yayıncı:

Pamukkale University

Ana Başlıklar:

Elektrik Mühendisliği (Diğer)

Dergi:

Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi Vol.29 Issue 5, pp.451-457

Belge Tipi:

Makale

Yayın Tarihi:

10/2023

Belge Dili:

Türkçe

Document ID (Org No.):

2001303392

pISSN:

2147-5881