Power usage modelling and prediction using artificial neural networks
In recent years, improving energy efficiency in the building sector has been a major trend globally due to its high consumption of electricity. The purpose of this study is to implement a data-driven method of Artificial Neural Networks (ANN) to predict a building's energy consumption at one of...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Conference or Workshop Item |
| Published: |
2022
|
| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/98941/ http://dx.doi.org/10.1109/ICSSA54161.2022.9870959 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| id |
my.utm.98941 |
|---|---|
| record_format |
eprints |
| spelling |
my.utm.989412023-02-08T09:33:47Z http://eprints.utm.my/id/eprint/98941/ Power usage modelling and prediction using artificial neural networks Hamilludin, Ummi Nurhidayati Ardhiah Abdullah, Haslaile Bani, Nurul Aini Mohd. Noor, Norliza A. Jalil, Siti Zura Ismail, Siti Haida T Technology (General) In recent years, improving energy efficiency in the building sector has been a major trend globally due to its high consumption of electricity. The purpose of this study is to implement a data-driven method of Artificial Neural Networks (ANN) to predict a building's energy consumption at one of the ministry buildings in Putrajaya. The prediction models were developed based on historical data on the electricity consumption of the building and weather forecasts including temperature, relative humidity and pressure from September 2009 to April 2017. Various configurations were tested based on the 'trial and error' method to improve the accuracy of the model. The forecasting model achieved 0.8237% of Mean Absolute Percentage Error (MAPE) and 99.17% of accuracy. The findings of the study will enable the management to model and predict the power usage of the facility using ANN in WEKA. The power usage prediction will help to provide input in understanding the future energy consumption behavior of the facility and will enable the management to assess potential energy efficiency improvements and behavior modification of the building towards achieving higher energy savings. 2022 Conference or Workshop Item PeerReviewed Hamilludin, Ummi Nurhidayati Ardhiah and Abdullah, Haslaile and Bani, Nurul Aini and Mohd. Noor, Norliza and A. Jalil, Siti Zura and Ismail, Siti Haida (2022) Power usage modelling and prediction using artificial neural networks. In: 4th International Conference on Smart Sensors and Application, ICSSA 2022, 26 - 28 July 2022, Kuala Lumpur, Malaysia. http://dx.doi.org/10.1109/ICSSA54161.2022.9870959 |
| institution |
Universiti Teknologi Malaysia |
| building |
UTM Library |
| collection |
Institutional Repository |
| continent |
Asia |
| country |
Malaysia |
| content_provider |
Universiti Teknologi Malaysia |
| content_source |
UTM Institutional Repository |
| url_provider |
http://eprints.utm.my/ |
| topic |
T Technology (General) |
| spellingShingle |
T Technology (General) Hamilludin, Ummi Nurhidayati Ardhiah Abdullah, Haslaile Bani, Nurul Aini Mohd. Noor, Norliza A. Jalil, Siti Zura Ismail, Siti Haida Power usage modelling and prediction using artificial neural networks |
| description |
In recent years, improving energy efficiency in the building sector has been a major trend globally due to its high consumption of electricity. The purpose of this study is to implement a data-driven method of Artificial Neural Networks (ANN) to predict a building's energy consumption at one of the ministry buildings in Putrajaya. The prediction models were developed based on historical data on the electricity consumption of the building and weather forecasts including temperature, relative humidity and pressure from September 2009 to April 2017. Various configurations were tested based on the 'trial and error' method to improve the accuracy of the model. The forecasting model achieved 0.8237% of Mean Absolute Percentage Error (MAPE) and 99.17% of accuracy. The findings of the study will enable the management to model and predict the power usage of the facility using ANN in WEKA. The power usage prediction will help to provide input in understanding the future energy consumption behavior of the facility and will enable the management to assess potential energy efficiency improvements and behavior modification of the building towards achieving higher energy savings. |
| format |
Conference or Workshop Item |
| author |
Hamilludin, Ummi Nurhidayati Ardhiah Abdullah, Haslaile Bani, Nurul Aini Mohd. Noor, Norliza A. Jalil, Siti Zura Ismail, Siti Haida |
| author_facet |
Hamilludin, Ummi Nurhidayati Ardhiah Abdullah, Haslaile Bani, Nurul Aini Mohd. Noor, Norliza A. Jalil, Siti Zura Ismail, Siti Haida |
| author_sort |
Hamilludin, Ummi Nurhidayati Ardhiah |
| title |
Power usage modelling and prediction using artificial neural networks |
| title_short |
Power usage modelling and prediction using artificial neural networks |
| title_full |
Power usage modelling and prediction using artificial neural networks |
| title_fullStr |
Power usage modelling and prediction using artificial neural networks |
| title_full_unstemmed |
Power usage modelling and prediction using artificial neural networks |
| title_sort |
power usage modelling and prediction using artificial neural networks |
| publishDate |
2022 |
| url |
http://eprints.utm.my/id/eprint/98941/ http://dx.doi.org/10.1109/ICSSA54161.2022.9870959 |
| _version_ |
1758578040886001664 |
| score |
13.211869 |