Energy Usage Prediction for Smart Home with Regression Based Ensemble Model

Mohammad Shamsul Hoque; Norziana Jamil; Sharul Azim Saharudin; Nowshad Amin

doi:10.1109/ICIMU49871.2020.9243578

Energy Usage Prediction for Smart Home with Regression Based Ensemble Model

Mohammad Shamsul Hoque
, Norziana Jamil
, Sharul Azim Saharudin
, Nowshad Amin

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

5 Citations (Scopus)

Abstract

Residential sectors using energy mainly though lighting and HV AC (Heating, Ventilation and Air-Conditioning) have become a significant consumer of world energy and it is expected to grow especially with the trend of increasing smart homes. To provide an optimum, accurate and reliable electricity distribution, load prediction is a prerequisite policy and operational implementation. Smart homes with the use of various sensors create big data that gives a favorable opportunity for developing data-driven energy usage prediction models. In this paper, a novel regression-based ensemble prediction model with inbuilt automated optimization for parameters is proposed to predict the demand of electricity. The model explains the 0.998 correlation between the features and their label, and achieved root mean squared error (RMSE) and Normalized Absolute Error as low as 5.508 and 0.0508 respectively. We have also proposed a novel data-driven classification of the energy usage by unsupervised learning through clustering.

Original language	English
Title of host publication	2020 8th International Conference on Information Technology and Multimedia, ICIMU 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	378-383
Number of pages	6
ISBN (Electronic)	9781728173108
DOIs	https://doi.org/10.1109/ICIMU49871.2020.9243578
Publication status	Published - Aug 24 2020
Externally published	Yes
Event	8th International Conference on Information Technology and Multimedia, ICIMU 2020 - Selangor, Malaysia Duration: Aug 24 2020 → Aug 25 2020

Publication series

Name	2020 8th International Conference on Information Technology and Multimedia, ICIMU 2020

Conference

Conference	8th International Conference on Information Technology and Multimedia, ICIMU 2020
Country/Territory	Malaysia
City	Selangor
Period	8/24/20 → 8/25/20

UN SDGs

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

SDG 7 Affordable and Clean Energy

Keywords

Deep Neural Learning
Ensemble model
K-means Clustering
Machine Learning
Model Optimization
Random Forest
Rapidminer
Unsupervised Learning

ASJC Scopus subject areas

Information Systems
Software
Computer Networks and Communications
Computer Science Applications

Access to Document

10.1109/ICIMU49871.2020.9243578

Cite this

Hoque, M. S., Jamil, N., Saharudin, S. A., & Amin, N. (2020). Energy Usage Prediction for Smart Home with Regression Based Ensemble Model. In 2020 8th International Conference on Information Technology and Multimedia, ICIMU 2020 (pp. 378-383). Article 9243578 (2020 8th International Conference on Information Technology and Multimedia, ICIMU 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICIMU49871.2020.9243578

Energy Usage Prediction for Smart Home with Regression Based Ensemble Model. / Hoque, Mohammad Shamsul; Jamil, Norziana; Saharudin, Sharul Azim et al.
2020 8th International Conference on Information Technology and Multimedia, ICIMU 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 378-383 9243578 (2020 8th International Conference on Information Technology and Multimedia, ICIMU 2020).

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

Hoque, MS, Jamil, N, Saharudin, SA & Amin, N 2020, Energy Usage Prediction for Smart Home with Regression Based Ensemble Model. in 2020 8th International Conference on Information Technology and Multimedia, ICIMU 2020., 9243578, 2020 8th International Conference on Information Technology and Multimedia, ICIMU 2020, Institute of Electrical and Electronics Engineers Inc., pp. 378-383, 8th International Conference on Information Technology and Multimedia, ICIMU 2020, Selangor, Malaysia, 8/24/20. https://doi.org/10.1109/ICIMU49871.2020.9243578

Hoque MS, Jamil N, Saharudin SA, Amin N. Energy Usage Prediction for Smart Home with Regression Based Ensemble Model. In 2020 8th International Conference on Information Technology and Multimedia, ICIMU 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 378-383. 9243578. (2020 8th International Conference on Information Technology and Multimedia, ICIMU 2020). doi: 10.1109/ICIMU49871.2020.9243578

Hoque, Mohammad Shamsul ; Jamil, Norziana ; Saharudin, Sharul Azim et al. / Energy Usage Prediction for Smart Home with Regression Based Ensemble Model. 2020 8th International Conference on Information Technology and Multimedia, ICIMU 2020. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 378-383 (2020 8th International Conference on Information Technology and Multimedia, ICIMU 2020).

@inproceedings{b793b9454b69460ba311ec9928075e16,

title = "Energy Usage Prediction for Smart Home with Regression Based Ensemble Model",

abstract = "Residential sectors using energy mainly though lighting and HV AC (Heating, Ventilation and Air-Conditioning) have become a significant consumer of world energy and it is expected to grow especially with the trend of increasing smart homes. To provide an optimum, accurate and reliable electricity distribution, load prediction is a prerequisite policy and operational implementation. Smart homes with the use of various sensors create big data that gives a favorable opportunity for developing data-driven energy usage prediction models. In this paper, a novel regression-based ensemble prediction model with inbuilt automated optimization for parameters is proposed to predict the demand of electricity. The model explains the 0.998 correlation between the features and their label, and achieved root mean squared error (RMSE) and Normalized Absolute Error as low as 5.508 and 0.0508 respectively. We have also proposed a novel data-driven classification of the energy usage by unsupervised learning through clustering.",

keywords = "Deep Neural Learning, Ensemble model, K-means Clustering, Machine Learning, Model Optimization, Random Forest, Rapidminer, Unsupervised Learning",

author = "Hoque, \{Mohammad Shamsul\} and Norziana Jamil and Saharudin, \{Sharul Azim\} and Nowshad Amin",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 8th International Conference on Information Technology and Multimedia, ICIMU 2020 ; Conference date: 24-08-2020 Through 25-08-2020",

year = "2020",

month = aug,

day = "24",

doi = "10.1109/ICIMU49871.2020.9243578",

language = "English",

series = "2020 8th International Conference on Information Technology and Multimedia, ICIMU 2020",

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

pages = "378--383",

booktitle = "2020 8th International Conference on Information Technology and Multimedia, ICIMU 2020",

}

TY - GEN

T1 - Energy Usage Prediction for Smart Home with Regression Based Ensemble Model

AU - Hoque, Mohammad Shamsul

AU - Jamil, Norziana

AU - Saharudin, Sharul Azim

AU - Amin, Nowshad

PY - 2020/8/24

Y1 - 2020/8/24

N2 - Residential sectors using energy mainly though lighting and HV AC (Heating, Ventilation and Air-Conditioning) have become a significant consumer of world energy and it is expected to grow especially with the trend of increasing smart homes. To provide an optimum, accurate and reliable electricity distribution, load prediction is a prerequisite policy and operational implementation. Smart homes with the use of various sensors create big data that gives a favorable opportunity for developing data-driven energy usage prediction models. In this paper, a novel regression-based ensemble prediction model with inbuilt automated optimization for parameters is proposed to predict the demand of electricity. The model explains the 0.998 correlation between the features and their label, and achieved root mean squared error (RMSE) and Normalized Absolute Error as low as 5.508 and 0.0508 respectively. We have also proposed a novel data-driven classification of the energy usage by unsupervised learning through clustering.

AB - Residential sectors using energy mainly though lighting and HV AC (Heating, Ventilation and Air-Conditioning) have become a significant consumer of world energy and it is expected to grow especially with the trend of increasing smart homes. To provide an optimum, accurate and reliable electricity distribution, load prediction is a prerequisite policy and operational implementation. Smart homes with the use of various sensors create big data that gives a favorable opportunity for developing data-driven energy usage prediction models. In this paper, a novel regression-based ensemble prediction model with inbuilt automated optimization for parameters is proposed to predict the demand of electricity. The model explains the 0.998 correlation between the features and their label, and achieved root mean squared error (RMSE) and Normalized Absolute Error as low as 5.508 and 0.0508 respectively. We have also proposed a novel data-driven classification of the energy usage by unsupervised learning through clustering.

KW - Deep Neural Learning

KW - Ensemble model

KW - K-means Clustering

KW - Machine Learning

KW - Model Optimization

KW - Random Forest

KW - Rapidminer

KW - Unsupervised Learning

UR - https://www.scopus.com/pages/publications/85097650660

UR - https://www.scopus.com/pages/publications/85097650660#tab=citedBy

U2 - 10.1109/ICIMU49871.2020.9243578

DO - 10.1109/ICIMU49871.2020.9243578

M3 - Conference contribution

AN - SCOPUS:85097650660

T3 - 2020 8th International Conference on Information Technology and Multimedia, ICIMU 2020

SP - 378

EP - 383

BT - 2020 8th International Conference on Information Technology and Multimedia, ICIMU 2020

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 8th International Conference on Information Technology and Multimedia, ICIMU 2020

Y2 - 24 August 2020 through 25 August 2020

ER -

Energy Usage Prediction for Smart Home with Regression Based Ensemble Model

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this