Electricity Consumption Forecasting Using Adaptive Neuro-Fuzzy Inference System (ANFIS)

Tay, K. G. and Muwafaq, Hassan and Tiong, W. K. and Choy, Y. Y. (2019) Electricity Consumption Forecasting Using Adaptive Neuro-Fuzzy Inference System (ANFIS). Universal Journal of Electrical and Electronic Engineering, 6 (5B). pp. 37-48.

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Abstract

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%).

Item Type: Article
Uncontrolled Keywords: ANN, FIS, ANFIS, Electricity Consumption, Forecasting, UTHM, unimas, university, universiti, Borneo, Malaysia, Sarawak, Kuching, Samarahan, ipta, education, research, Universiti Malaysia Sarawak
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Academic Faculties, Institutes and Centres > Faculty of Computer Science and Information Technology
Depositing User: Tuah
Date Deposited: 20 Oct 2020 02:50
Last Modified: 20 Oct 2020 02:50
URI: http://ir.unimas.my/id/eprint/32232

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