Forecasting electricity consumption of OECD countries: A global machine learning modeling approach
Küçük Resim Yok
Tarih
2021
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier Sci Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Electricity is a critical utility for social growth. Accurate estimation of its consumption plays a vital role in economic development. A database that included past electricity consumption data from all OECD countries was prepared. Since national trends may be transferable from one country to another, the entire database was modeled and simulated via machine learning techniques to forecast the energy consumption of each country. Understanding similarities among the profiles of different countries could increase predictive accuracy and improve associated public policies.
Açıklama
Anahtar Kelimeler
Machine Learning, Artificial Neural Network, Support Vector Machine, Natural-Gas Consumption, Energy-Consumption, Time-Series, Socioeconomic Indicators, Neural-Network, Demand, Combination, Regression, Arima
Kaynak
Utilities Policy
WoS Q Değeri
Q3
Scopus Q Değeri
Q1
Cilt
70
