Deep Learning Based Building Load Prediction for Residences of an Academic Institutions

Sula Ntsaluba; Nnamdi Nwulu; Kuselo Ntsaluba

doi:10.1109/ICECET52533.2021.9698812

Deep Learning Based Building Load Prediction for Residences of an Academic Institutions

Sula Ntsaluba
, Nnamdi Nwulu
, Kuselo Ntsaluba

Electrical and Electronic Engineering Science

University of Johannesburg

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

2 Citations (Scopus)

Abstract

Effective energy planning has in many instances been identified as a critical operation in order to achieve economic and sustainable building energy usage. Accurate building load prediction is a key factor that can result in energy usage and cost reduction. This paper presents an energy usage analysis for an academic institution over a period of four years. Building load prediction was then considered, where three different deep learning models were implemented in an attempt to identify the model that would perform best at predicting the load demand of a selected building over a 1 year period. The accuracy of the implemented models was evaluated through analysis of the Mean Absolute Percentage Error. The selected models produced values ranging from 0.09 to 0.22, which compare well with results highlighted in other literature studies for similar buildings.

Original language	English
Title of host publication	International Conference on Electrical, Computer, and Energy Technologies, ICECET 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665442312
DOIs	https://doi.org/10.1109/ICECET52533.2021.9698812
Publication status	Published - 2021
Event	2021 International Conference on Electrical, Computer, and Energy Technologies, ICECET 2021 - Cape Town, South Africa Duration: 9 Dec 2021 → 10 Dec 2021

Publication series

Name	International Conference on Electrical, Computer, and Energy Technologies, ICECET 2021

Conference

Conference	2021 International Conference on Electrical, Computer, and Energy Technologies, ICECET 2021
Country/Territory	South Africa
City	Cape Town
Period	9/12/21 → 10/12/21

UN SDGs

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

SDG 12 Responsible Consumption and Production

Keywords

building load prediction
energy consumption
feed forward neural network
mean absolute percentage error
recurrent neural network

ASJC Scopus subject areas

Electrical and Electronic Engineering
General Computer Science
Energy Engineering and Power Technology

Access to Document

10.1109/ICECET52533.2021.9698812

Cite this

Ntsaluba, S., Nwulu, N., & Ntsaluba, K. (2021). Deep Learning Based Building Load Prediction for Residences of an Academic Institutions. In International Conference on Electrical, Computer, and Energy Technologies, ICECET 2021 (International Conference on Electrical, Computer, and Energy Technologies, ICECET 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICECET52533.2021.9698812

Ntsaluba, Sula ; Nwulu, Nnamdi ; Ntsaluba, Kuselo. / Deep Learning Based Building Load Prediction for Residences of an Academic Institutions. International Conference on Electrical, Computer, and Energy Technologies, ICECET 2021. Institute of Electrical and Electronics Engineers Inc., 2021. (International Conference on Electrical, Computer, and Energy Technologies, ICECET 2021).

@inproceedings{73953d6478ae4013a04045e34d2c7eeb,

title = "Deep Learning Based Building Load Prediction for Residences of an Academic Institutions",

abstract = "Effective energy planning has in many instances been identified as a critical operation in order to achieve economic and sustainable building energy usage. Accurate building load prediction is a key factor that can result in energy usage and cost reduction. This paper presents an energy usage analysis for an academic institution over a period of four years. Building load prediction was then considered, where three different deep learning models were implemented in an attempt to identify the model that would perform best at predicting the load demand of a selected building over a 1 year period. The accuracy of the implemented models was evaluated through analysis of the Mean Absolute Percentage Error. The selected models produced values ranging from 0.09 to 0.22, which compare well with results highlighted in other literature studies for similar buildings.",

keywords = "building load prediction, energy consumption, feed forward neural network, mean absolute percentage error, recurrent neural network",

author = "Sula Ntsaluba and Nnamdi Nwulu and Kuselo Ntsaluba",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 International Conference on Electrical, Computer, and Energy Technologies, ICECET 2021 ; Conference date: 09-12-2021 Through 10-12-2021",

year = "2021",

doi = "10.1109/ICECET52533.2021.9698812",

language = "English",

series = "International Conference on Electrical, Computer, and Energy Technologies, ICECET 2021",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "International Conference on Electrical, Computer, and Energy Technologies, ICECET 2021",

address = "United States",

}

Ntsaluba, S, Nwulu, N & Ntsaluba, K 2021, Deep Learning Based Building Load Prediction for Residences of an Academic Institutions. in International Conference on Electrical, Computer, and Energy Technologies, ICECET 2021. International Conference on Electrical, Computer, and Energy Technologies, ICECET 2021, Institute of Electrical and Electronics Engineers Inc., 2021 International Conference on Electrical, Computer, and Energy Technologies, ICECET 2021, Cape Town, South Africa, 9/12/21. https://doi.org/10.1109/ICECET52533.2021.9698812

Deep Learning Based Building Load Prediction for Residences of an Academic Institutions. / Ntsaluba, Sula; Nwulu, Nnamdi; Ntsaluba, Kuselo.
International Conference on Electrical, Computer, and Energy Technologies, ICECET 2021. Institute of Electrical and Electronics Engineers Inc., 2021. (International Conference on Electrical, Computer, and Energy Technologies, ICECET 2021).

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

TY - GEN

T1 - Deep Learning Based Building Load Prediction for Residences of an Academic Institutions

AU - Ntsaluba, Sula

AU - Nwulu, Nnamdi

AU - Ntsaluba, Kuselo

PY - 2021

Y1 - 2021

N2 - Effective energy planning has in many instances been identified as a critical operation in order to achieve economic and sustainable building energy usage. Accurate building load prediction is a key factor that can result in energy usage and cost reduction. This paper presents an energy usage analysis for an academic institution over a period of four years. Building load prediction was then considered, where three different deep learning models were implemented in an attempt to identify the model that would perform best at predicting the load demand of a selected building over a 1 year period. The accuracy of the implemented models was evaluated through analysis of the Mean Absolute Percentage Error. The selected models produced values ranging from 0.09 to 0.22, which compare well with results highlighted in other literature studies for similar buildings.

AB - Effective energy planning has in many instances been identified as a critical operation in order to achieve economic and sustainable building energy usage. Accurate building load prediction is a key factor that can result in energy usage and cost reduction. This paper presents an energy usage analysis for an academic institution over a period of four years. Building load prediction was then considered, where three different deep learning models were implemented in an attempt to identify the model that would perform best at predicting the load demand of a selected building over a 1 year period. The accuracy of the implemented models was evaluated through analysis of the Mean Absolute Percentage Error. The selected models produced values ranging from 0.09 to 0.22, which compare well with results highlighted in other literature studies for similar buildings.

KW - building load prediction

KW - energy consumption

KW - feed forward neural network

KW - mean absolute percentage error

KW - recurrent neural network

UR - https://www.scopus.com/pages/publications/85127059123

U2 - 10.1109/ICECET52533.2021.9698812

DO - 10.1109/ICECET52533.2021.9698812

M3 - Conference contribution

AN - SCOPUS:85127059123

T3 - International Conference on Electrical, Computer, and Energy Technologies, ICECET 2021

BT - International Conference on Electrical, Computer, and Energy Technologies, ICECET 2021

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2021 International Conference on Electrical, Computer, and Energy Technologies, ICECET 2021

Y2 - 9 December 2021 through 10 December 2021

ER -

Ntsaluba S, Nwulu N, Ntsaluba K. Deep Learning Based Building Load Prediction for Residences of an Academic Institutions. In International Conference on Electrical, Computer, and Energy Technologies, ICECET 2021. Institute of Electrical and Electronics Engineers Inc. 2021. (International Conference on Electrical, Computer, and Energy Technologies, ICECET 2021). doi: 10.1109/ICECET52533.2021.9698812

Deep Learning Based Building Load Prediction for Residences of an Academic Institutions

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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