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Electricity consumption forecasting using Adaptive Neuro-Fuzzy Inference System (ANFIS)

Universiti Tun Hussein Onn Malaysia (UTHM) is a developing Malaysian Technical University. There is a great development of UTHM since its formation in 1993. Therefore, it is crucial to have accurate future electricity consumption forecasting for its future energy management and saving. Even though t...

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Main Authors: K.G., Tay, Muwafaq, Hassan, W. K., Tiong, Y. Y., Choy
Format: Article
Language:English
Published: HRPub 2019
Subjects:
TK3001-3521 Distribution or transmission of electric power
Online Access:http://eprints.uthm.edu.my/646/1/DNJ9640_6beae058197faf0ff594019ec031eb44.pdf
http://eprints.uthm.edu.my/646/
https://doi.org/10.13189/ujeee.2019.061606
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spelling my.uthm.eprints.6462021-08-17T06:26:25Z http://eprints.uthm.edu.my/646/ Electricity consumption forecasting using Adaptive Neuro-Fuzzy Inference System (ANFIS) K.G., Tay Muwafaq, Hassan W. K., Tiong Y. Y., Choy TK3001-3521 Distribution or transmission of electric power Universiti Tun Hussein Onn Malaysia (UTHM) is a developing Malaysian Technical University. There is a great development of UTHM since its formation in 1993. Therefore, it is crucial to have accurate future electricity consumption forecasting for its future energy management and saving. Even though there are previous works of electricity consumption forecasting using Adaptive Neuro-Fuzzy Inference System (ANFIS), but most of their data are multivariate data. In this study, we have only univariate data of UTHM electricity consumption from January 2009 to December 2018 and wish to forecast 2019 consumption. The univariate data was converted to multivariate and ANFIS was chosen as it carries both advantages of Artificial Neural Network (ANN) and Fuzzy Inference System (FIS). ANFIS yields the MAPE between actual and predicted electricity consumption of 0.4002% which is relatively low if compared to previous works of UTHM electricity forecasting using time series model (11.14%), and first-order fuzzy time series (5.74%), and multiple linear regression (10.62%). HRPub 2019 Article PeerReviewed text en http://eprints.uthm.edu.my/646/1/DNJ9640_6beae058197faf0ff594019ec031eb44.pdf K.G., Tay and Muwafaq, Hassan and W. K., Tiong and Y. Y., Choy (2019) Electricity consumption forecasting using Adaptive Neuro-Fuzzy Inference System (ANFIS). Universal Journal of Electrical and Electronic Engineering, 6 (5B). pp. 37-48. (Unpublished) https://doi.org/10.13189/ujeee.2019.061606
institution Universiti Tun Hussein Onn Malaysia
building UTHM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tun Hussein Onn Malaysia
content_source UTHM Institutional Repository
url_provider http://eprints.uthm.edu.my/
language English
topic TK3001-3521 Distribution or transmission of electric power
spellingShingle TK3001-3521 Distribution or transmission of electric power
K.G., Tay
Muwafaq, Hassan
W. K., Tiong
Y. Y., Choy
Electricity consumption forecasting using Adaptive Neuro-Fuzzy Inference System (ANFIS)
description Universiti Tun Hussein Onn Malaysia (UTHM) is a developing Malaysian Technical University. There is a great development of UTHM since its formation in 1993. Therefore, it is crucial to have accurate future electricity consumption forecasting for its future energy management and saving. Even though there are previous works of electricity consumption forecasting using Adaptive Neuro-Fuzzy Inference System (ANFIS), but most of their data are multivariate data. In this study, we have only univariate data of UTHM electricity consumption from January 2009 to December 2018 and wish to forecast 2019 consumption. The univariate data was converted to multivariate and ANFIS was chosen as it carries both advantages of Artificial Neural Network (ANN) and Fuzzy Inference System (FIS). ANFIS yields the MAPE between actual and predicted electricity consumption of 0.4002% which is relatively low if compared to previous works of UTHM electricity forecasting using time series model (11.14%), and first-order fuzzy time series (5.74%), and multiple linear regression (10.62%).
format Article
author K.G., Tay
Muwafaq, Hassan
W. K., Tiong
Y. Y., Choy
author_facet K.G., Tay
Muwafaq, Hassan
W. K., Tiong
Y. Y., Choy
author_sort K.G., Tay
title Electricity consumption forecasting using Adaptive Neuro-Fuzzy Inference System (ANFIS)
title_short Electricity consumption forecasting using Adaptive Neuro-Fuzzy Inference System (ANFIS)
title_full Electricity consumption forecasting using Adaptive Neuro-Fuzzy Inference System (ANFIS)
title_fullStr Electricity consumption forecasting using Adaptive Neuro-Fuzzy Inference System (ANFIS)
title_full_unstemmed Electricity consumption forecasting using Adaptive Neuro-Fuzzy Inference System (ANFIS)
title_sort electricity consumption forecasting using adaptive neuro-fuzzy inference system (anfis)
publisher HRPub
publishDate 2019
url http://eprints.uthm.edu.my/646/1/DNJ9640_6beae058197faf0ff594019ec031eb44.pdf
http://eprints.uthm.edu.my/646/
https://doi.org/10.13189/ujeee.2019.061606
_version_ 1738580763856601088
score 13.244745

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