Residential Power Load Prediction in Smart Cities using Machine Learning Approaches

Waleed Alomoush; Tahir Abbas Khan; Mehwish Nadeem; Jamshaid Iqbal Janjua; Anwaar Saeed; Atifa Athar

doi:10.1109/ICBATS54253.2022.9759024

Residential Power Load Prediction in Smart Cities using Machine Learning Approaches

Waleed Alomoush
, Tahir Abbas Khan
, Mehwish Nadeem
, Jamshaid Iqbal Janjua
, Anwaar Saeed
, Atifa Athar

NCBAandE
University of Engineering and Technology Lahore
Virtual University of Pakistan
COMSATS University Islamabad

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

21 Scopus citations

Abstract

Accurate load prediction plays a vital role in energy planning and load management and offers a distinctive opportunity for applying advanced analytics. Stake holders of power markets gains benefits with better integration of load management, smart grid control and metering in smart cities. It helps to improve efficiency of power load consumption. The paper proposed hybrid method based on Machine learning for predicting residential power load. We positioned correlated feature extraction and applied with system model to generate predictive results. The loss function and RMSE were calculated for accuracy of the prediction results.

Original language	English
Title of host publication	2022 International Conference on Business Analytics for Technology and Security, ICBATS 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665409209
DOIs	https://doi.org/10.1109/ICBATS54253.2022.9759024
State	Published - 2022
Event	2022 International Conference on Business Analytics for Technology and Security, ICBATS 2022 - Dubai, United Arab Emirates Duration: 16 Feb 2022 → 17 Feb 2022

Publication series

Name	2022 International Conference on Business Analytics for Technology and Security, ICBATS 2022

Conference

Conference	2022 International Conference on Business Analytics for Technology and Security, ICBATS 2022
Country/Territory	United Arab Emirates
City	Dubai
Period	16/02/22 → 17/02/22

UN SDGs

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

SDG 11 Sustainable Cities and Communities

Keywords

Co-relational Trends
Feature Extraction
Gradient Boosting
Multiple Linear Regression
Smart Meters

Access to Document

10.1109/ICBATS54253.2022.9759024

Cite this

Alomoush, W., Khan, T. A., Nadeem, M., Janjua, J. I., Saeed, A., & Athar, A. (2022). Residential Power Load Prediction in Smart Cities using Machine Learning Approaches. In 2022 International Conference on Business Analytics for Technology and Security, ICBATS 2022 (2022 International Conference on Business Analytics for Technology and Security, ICBATS 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICBATS54253.2022.9759024

Alomoush, Waleed ; Khan, Tahir Abbas ; Nadeem, Mehwish et al. / Residential Power Load Prediction in Smart Cities using Machine Learning Approaches. 2022 International Conference on Business Analytics for Technology and Security, ICBATS 2022. Institute of Electrical and Electronics Engineers Inc., 2022. (2022 International Conference on Business Analytics for Technology and Security, ICBATS 2022).

@inproceedings{d8630880e17d4681bb377f5e03c3e115,

title = "Residential Power Load Prediction in Smart Cities using Machine Learning Approaches",

abstract = "Accurate load prediction plays a vital role in energy planning and load management and offers a distinctive opportunity for applying advanced analytics. Stake holders of power markets gains benefits with better integration of load management, smart grid control and metering in smart cities. It helps to improve efficiency of power load consumption. The paper proposed hybrid method based on Machine learning for predicting residential power load. We positioned correlated feature extraction and applied with system model to generate predictive results. The loss function and RMSE were calculated for accuracy of the prediction results.",

keywords = "Co-relational Trends, Feature Extraction, Gradient Boosting, Multiple Linear Regression, Smart Meters",

author = "Waleed Alomoush and Khan, \{Tahir Abbas\} and Mehwish Nadeem and Janjua, \{Jamshaid Iqbal\} and Anwaar Saeed and Atifa Athar",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 International Conference on Business Analytics for Technology and Security, ICBATS 2022 ; Conference date: 16-02-2022 Through 17-02-2022",

year = "2022",

doi = "10.1109/ICBATS54253.2022.9759024",

language = "English",

series = "2022 International Conference on Business Analytics for Technology and Security, ICBATS 2022",

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

booktitle = "2022 International Conference on Business Analytics for Technology and Security, ICBATS 2022",

address = "United States",

}

Alomoush, W, Khan, TA, Nadeem, M, Janjua, JI, Saeed, A & Athar, A 2022, Residential Power Load Prediction in Smart Cities using Machine Learning Approaches. in 2022 International Conference on Business Analytics for Technology and Security, ICBATS 2022. 2022 International Conference on Business Analytics for Technology and Security, ICBATS 2022, Institute of Electrical and Electronics Engineers Inc., 2022 International Conference on Business Analytics for Technology and Security, ICBATS 2022, Dubai, United Arab Emirates, 16/02/22. https://doi.org/10.1109/ICBATS54253.2022.9759024

Residential Power Load Prediction in Smart Cities using Machine Learning Approaches. / Alomoush, Waleed; Khan, Tahir Abbas; Nadeem, Mehwish et al.
2022 International Conference on Business Analytics for Technology and Security, ICBATS 2022. Institute of Electrical and Electronics Engineers Inc., 2022. (2022 International Conference on Business Analytics for Technology and Security, ICBATS 2022).

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

TY - GEN

T1 - Residential Power Load Prediction in Smart Cities using Machine Learning Approaches

AU - Alomoush, Waleed

AU - Khan, Tahir Abbas

AU - Nadeem, Mehwish

AU - Janjua, Jamshaid Iqbal

AU - Saeed, Anwaar

AU - Athar, Atifa

PY - 2022

Y1 - 2022

N2 - Accurate load prediction plays a vital role in energy planning and load management and offers a distinctive opportunity for applying advanced analytics. Stake holders of power markets gains benefits with better integration of load management, smart grid control and metering in smart cities. It helps to improve efficiency of power load consumption. The paper proposed hybrid method based on Machine learning for predicting residential power load. We positioned correlated feature extraction and applied with system model to generate predictive results. The loss function and RMSE were calculated for accuracy of the prediction results.

AB - Accurate load prediction plays a vital role in energy planning and load management and offers a distinctive opportunity for applying advanced analytics. Stake holders of power markets gains benefits with better integration of load management, smart grid control and metering in smart cities. It helps to improve efficiency of power load consumption. The paper proposed hybrid method based on Machine learning for predicting residential power load. We positioned correlated feature extraction and applied with system model to generate predictive results. The loss function and RMSE were calculated for accuracy of the prediction results.

KW - Co-relational Trends

KW - Feature Extraction

KW - Gradient Boosting

KW - Multiple Linear Regression

KW - Smart Meters

UR - https://www.scopus.com/pages/publications/85129664694

U2 - 10.1109/ICBATS54253.2022.9759024

DO - 10.1109/ICBATS54253.2022.9759024

M3 - Conference contribution

AN - SCOPUS:85129664694

T3 - 2022 International Conference on Business Analytics for Technology and Security, ICBATS 2022

BT - 2022 International Conference on Business Analytics for Technology and Security, ICBATS 2022

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2022 International Conference on Business Analytics for Technology and Security, ICBATS 2022

Y2 - 16 February 2022 through 17 February 2022

ER -

Alomoush W, Khan TA, Nadeem M, Janjua JI, Saeed A, Athar A. Residential Power Load Prediction in Smart Cities using Machine Learning Approaches. In 2022 International Conference on Business Analytics for Technology and Security, ICBATS 2022. Institute of Electrical and Electronics Engineers Inc. 2022. (2022 International Conference on Business Analytics for Technology and Security, ICBATS 2022). doi: 10.1109/ICBATS54253.2022.9759024

Residential Power Load Prediction in Smart Cities using Machine Learning Approaches

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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