Evaluating factors responsible for energy consumption: Connection weight approach

Oludolapo Akanni Olanrewaju; Charles Mbohwa

doi:10.1109/EPEC.2016.7771713

Evaluating factors responsible for energy consumption: Connection weight approach

Oludolapo Akanni Olanrewaju
, Charles Mbohwa

Quality and Operations Management

University of Johannesburg

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Citations (Scopus)

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.

Original language	English
Title of host publication	2016 IEEE Electrical Power and Energy Conference, EPEC 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781509019199
DOIs	https://doi.org/10.1109/EPEC.2016.7771713
Publication status	Published - 5 Dec 2016
Event	2016 IEEE Electrical Power and Energy Conference, EPEC 2016 - Ottawa, Canada Duration: 12 Oct 2016 → 14 Oct 2016

Publication series

Name	2016 IEEE Electrical Power and Energy Conference, EPEC 2016

Conference

Conference	2016 IEEE Electrical Power and Energy Conference, EPEC 2016
Country/Territory	Canada
City	Ottawa
Period	12/10/16 → 14/10/16

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy
SDG 9 Industry, Innovation, and Infrastructure

Keywords

Artificial Neural Network
connection weight
energy consumption
policies

ASJC Scopus subject areas

Energy Engineering and Power Technology
Renewable Energy, Sustainability and the Environment
Control and Optimization

Access to Document

10.1109/EPEC.2016.7771713

Cite this

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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",

}

Olanrewaju, OA & Mbohwa, C 2016, Evaluating factors responsible for energy consumption: Connection weight approach. in 2016 IEEE Electrical Power and Energy Conference, EPEC 2016., 7771713, 2016 IEEE Electrical Power and Energy Conference, EPEC 2016, Institute of Electrical and Electronics Engineers Inc., 2016 IEEE Electrical Power and Energy Conference, EPEC 2016, Ottawa, Canada, 12/10/16. https://doi.org/10.1109/EPEC.2016.7771713

Evaluating factors responsible for energy consumption: Connection weight approach. / Olanrewaju, Oludolapo Akanni; Mbohwa, Charles.
2016 IEEE Electrical Power and Energy Conference, EPEC 2016. Institute of Electrical and Electronics Engineers Inc., 2016. 7771713 (2016 IEEE Electrical Power and Energy Conference, EPEC 2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Evaluating factors responsible for energy consumption

T2 - 2016 IEEE Electrical Power and Energy Conference, EPEC 2016

AU - Olanrewaju, Oludolapo Akanni

AU - Mbohwa, Charles

PY - 2016/12/5

Y1 - 2016/12/5

N2 - 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.

AB - 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.

KW - Artificial Neural Network

KW - connection weight

KW - energy consumption

KW - policies

UR - https://www.scopus.com/pages/publications/85010496228

U2 - 10.1109/EPEC.2016.7771713

DO - 10.1109/EPEC.2016.7771713

M3 - Conference contribution

AN - SCOPUS:85010496228

T3 - 2016 IEEE Electrical Power and Energy Conference, EPEC 2016

BT - 2016 IEEE Electrical Power and Energy Conference, EPEC 2016

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 12 October 2016 through 14 October 2016

ER -

Evaluating factors responsible for energy consumption: Connection weight approach

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this