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