Energy Consumption Prediction Using Deep Learning Technique

Dublin Core

Title

Energy Consumption Prediction Using Deep Learning Technique

Subject

Energy consumption
Educational building
Deep Learning
LSTM
Prediction.

Description

In the present era, due to technological advances, the problem of energy consumption has become one of the most important problems for its environmental and economic impact. Educational buildings are one of the highest energy consuming institutions. Therefore, one has to direct the individual and society to reach the ideal usage of energy. One of the possible methods to do that is to prediction energy consumption. This study proposes an energy consumption prediction model using deep learning algorithm. To evaluate its performance, College of Computer (CoC) at Qassim University was selected to analyze the elements in the college that affect high energy consumption and data were collected from the Saudi Electricity Company of daily for 13 years. This research applied Long short term memory (LSTM) technique for medium-term prediction of energy consumption. The performance of the proposed model has been measured by evaluation metrics and achieved low Root mean square error (RMSE) which means higher accuracy of the model compared to relative studies. Consequently, this research provides a recommendation for educational organizations to reach optimal energy consumption.

Creator

Alanbar, Maha
Alfarraj, Amal
Alghieth, Manal

Source

International Journal of Interactive Mobile Technologies (iJIM); Vol. 14 No. 10 (2020); pp. 166-177
1865-7923

Publisher

International Association of Online Engineering (IAOE), Vienna, Austria

Date

2020-06-30

Rights

Copyright (c) 2020 Dina M. Ibrahim

Relation

Format

application/pdf

Language

eng

Type

info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Non-refereed Article

Identifier

Citation

Maha Alanbar, Amal Alfarraj and Manal Alghieth, Energy Consumption Prediction Using Deep Learning Technique, International Association of Online Engineering (IAOE), Vienna, Austria, 2020, accessed November 7, 2024, https://igi.indrastra.com/items/show/1718

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