The COVID-19 pandemic associated with the lockdown measures caused an extraordinary impact on the economies of all countries in the world. Under lockdown, dramatic reductions in industry and services resulted in electricity demand dropping to Sunday levels, though higher domestic use yielded a relatively small partial offset. In this study, we analyzed overall electricity consumption in Turkey starting from pre-COVID days until now to illustrate the pandemic's effects on consumption. For this purpose, we built an ensemble machine learning model for the analysis. Findings revealed that the proposed boosting (AdaBoost) ensemble algorithm (RMSE: 41848.7, MAE: 18574.3, R2 :0.89) is a significant contributory factor in the analysis of data related to electricity consumption. Results also show that boosting (AdaBoost) ensemble learning algorithm is more preferable in the use of energy-related data than the bagging (random forest) and single-based algorithms (deep neural networks).
Ensemble Learning Algorithms Adaboost Electricity Consumption Covid-19 Pandemic
Birincil Dil | İngilizce |
---|---|
Konular | Mühendislik |
Bölüm | Research Articles |
Yazarlar | |
Erken Görünüm Tarihi | 30 Haziran 2022 |
Yayımlanma Tarihi | 30 Haziran 2022 |
Gönderilme Tarihi | 16 Mart 2022 |
Yayımlandığı Sayı | Yıl 2022 Cilt: 1 Sayı: 1 |
Dergimiz 2024 Yılı Ocak ayı itibariyle artık İngilizce ve Türkçe yayınları kabul etmeye başlamıştır. Türkçe yayınlar İngilizce Özet içerecek şekilde kabul edilecektir. Yazım Kuralları menüsünden Tam Metin yazım şablonunu indirebilirsiniz.