@inproceedings{b6f1f0b181da422e8a3258da522123ca,
title = "Evaluating factors responsible for energy consumption: Connection weight approach",
abstract = "Various governments and stakeholders are established across the globe to respond to various energy challenges that has led to one or more energy policy development. A proper analysis of what contributes to energy consumption will assist in the development of policies needed for the conservation of energy consumption. This study made use of the connection weight approach as an instrument of the Artificial Neural Network (ANN) to evaluate the contributions of activity, structure and intensity factors to energy consumption in the Canadian industrial sector. From the evaluation, intensity contributed 46.5 %, whereas activity and structure contributed 32.6 % and 20.9 %. This is an indication that policies and strategies should be developed more on intensity to achieve energy saving.",
keywords = "Artificial Neural Network, connection weight, energy consumption, policies",
author = "Olanrewaju, {Oludolapo Akanni} and Charles Mbohwa",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE Electrical Power and Energy Conference, EPEC 2016 ; Conference date: 12-10-2016 Through 14-10-2016",
year = "2016",
month = dec,
day = "5",
doi = "10.1109/EPEC.2016.7771713",
language = "English",
series = "2016 IEEE Electrical Power and Energy Conference, EPEC 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2016 IEEE Electrical Power and Energy Conference, EPEC 2016",
address = "United States",
}