Yearly residential electricity forecasting model based on fuzzy regression time series in Indonesia

Riswan Efendi

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Type :	article
Subject :	Q Science (General)
ISSN :	2673-4591
Main Author :	Riswan Efendi
Title :	Yearly residential electricity forecasting model based on fuzzy regression time series in Indonesia

Place of Production :	Tanjung Malim
Publisher :	Fakulti Sains dan Matematik
Year of Publication :	2023
Notes :	Engineering Proceedings
Corporate Name :	Universiti Pendidikan Sultan Idris
HTTP Link :	Click to view web link

Abstract : Universiti Pendidikan Sultan Idris

Triangular fuzzy numbers (TFNs) are used to express the weights of criteria and alternatives to account for the ambiguity and uncertainty inherent to subjective evaluations. However, the proposed method can easily be extended to other fuzzy settings depending on the uncertainty facing managers and decision-makers. Triangular fuzzy number (TFN) is a critical component in building fuzzy models such as fuzzy regression and fuzzy autoregressive. Many symmetrical triangular fuzzy numbers have been proposed to improve the scales linguistic accuracy. Additionally, Sturges rule is a well-known approach to determining criteria or intervals of grouped data. However, some existing TFN methods are challenging despite being considered in building fuzzy regression models. The increase in electricity distribution is caused by the number of customers and the amount of installed capacity factors in Indonesia. The identified factors are uncertainty, inexactness, and random nature. This paper investigates the residential electricity distribution model using fuzzy regression time series. In the beginning step, the integration between conventional TFN and Sturges rule was proposed to determine the criteria or scale of linguistic terms. The secondary data was collected from BPS Indonesia from 2000 to 2021. The dependent variable was denoted as electric power distribution (Formula presented.). On the other hand, the number of customers and the amount of installed capacity were grouped as independent variables ((Formula presented.) and (Formula presented.)). The results showed that the best forecasting model is an FLR right upper limit without constant. This proposed model also has higher MAPE accuracy at 1.44% compared to classical models. Additionally, the proposed triangular fuzzy number could improve the accuracy of the proposed model significantly. Interestingly, both dependent and independent factors were initially forecasted using a basic time series model, namely exponential smoothing. 2023 by the authors.

References

Franzco, R.J.C.D.P.; Farmer, L.D.M. Review understanding and checking the assumptions of linear regression: A primer for medical researchers. Clin. Exp. Ophthalmol. 2014, 42, 590–596.

Zhang, L.; Wen, J.; Li, Y.; Chen, J.; Ye, Y.; Fu, Y.; Livingood, W. A Review of Machine Learning in Building Load Prediction. Appl. Energy 2021, 285, 116452. [CrossRef]

Ismail, Z.; Efendi, R.; Deris, M.M. Interquartile Range Approach to Length-Interval Adjustment of Enrolment Data in Fuzzy Time Series Forecasting. Int. J. Comp. Intell. Appl. 2012, 12, 1350016. [CrossRef]

Efendi, R.; Arbaiy, N.; Deris, M.M. A New Procedure in Stock Market Forecasting Based on Fuzzy Random Auto-Regression Time Series Model. Inf. Sci. 2018, 441, 113–132. [CrossRef]

Efendi, R.; Deris, M.M. Non-probabilistic inverse fuzzy model in time series forecasting. Int. J. Uncertain. Fuzziness Knowl.-Based Syst. 2018, 26, 855–873. [CrossRef]

Sturges, H. The Choice of A Class-Interval. J. Am. Stat. Assoc. 1926, 21, 65–66. [CrossRef]

Tanaka, H. Fuzzy Data Analysis by Possibilistic Linear Models. Fuzzy Sets Syst. 1987, 24, 363–375. [CrossRef]

Tanaka, H.;Watada, J. Possibilistic linear systems and their application to the linear regression model. Fuzzy Sets Syst. 1988, 27, 275–289. [CrossRef]

Wang, H.M.; Lee, M.J. Fuzzy regression model with fuzzy input and output data. Inf. Sci. 2007, 177, 2049–2067.

Rosadi, M.; Syamsul, A.B. Faktor-Faktor yang Mempengaruhi Konsumsi Listrik di Indonesia. J. Kaji. Ekon. Dan Pembang. 2019, 1, 273–286. [CrossRef]

Nazarko, J.; Zalewski, W. The fuzzy regression approach to peak load estimation in power distribution systems. IEEE Trans. Power Syst. 1999, 14, 809–814. [CrossRef]

Purwareta, H.P.; Gusti, N.I.; Nuri, W. Model Peramalan Pasokan Energi Primer dengan Pendekatan Metode Fuzzy Linear Regression (FLR). J. Sains Dan Seni ITS 2012, 1, A34–A39.

Khairudin, M.; Nursusanto, U.; Ismara, K.I.; Arifin, F.; Fahrurrozi, D.B.; Yahya, A.; Prabuwono, A.S.; Mohamed, Z. Estimated Use of Electrical Load Using Regression Analysis and Adaptive Neoro Fuzzy Inference System. J. Eng. Sci. Technol. 2021, 16, 4452–4467.

Lee, G.-C. Regression-Based Methods for Daily Peak Load Forecasting in South Korea. Sustainability 2022, 14, 3984. [CrossRef]

Lah, M.S.C.; Arbaiy, N.; Efendi, R. Stock Market Forecasting Model Based on AR(1) with Adjusted Triangular Fuzzy Number Using Standard Deviation Approach for ASEAN Countries. Intell. Interact. Comput. 2019, 67, 103–114.

Efendi, R.; Imandari, A.N.; Rahmadhani, Y.; Suhartono; Samsudin, N.A.; Arbai, N.; Deris, M.M. Fuzzy autoregressive time series model based on symmetry triangular fuzzy numbers. New Math. Nat. Comput. 2021, 17, 387–401. [CrossRef]

Azadeh, A.; Khakestani, M.; Saberi, M.A. Flexible Fuzzy Regression Algorithm for Forecasting Oil Consumption Estimation. J. Energy Policy 2009, 37, 5567–5579. [CrossRef]

Mir, A.A.; Alghassab, M.; Ullah, K.; Khan, Z.A.; Lu, Y.; Imran, M. A Review of Electricity Demand Forecasting in Low and Middle Income Countries: The Demand Determinants and Horizons. Sustainability 2020, 12, 5931. [CrossRef]

Zadeh, L.A. Fuzzy sets. Inf. Control 1965, 8, 338–353. [CrossRef]

Afrasiabi, A.; Tavana, M.; Caprio, D.D. An Extended Hybrid Fuzzy Multi-Criteria Decision Model for Sustainable and Resilient Supplier Selection. Environ. Sci. Pollut. Res. 2022, 29, 37291–37314. [CrossRef]

Tanaka, H.; Uejima, S.; Asia, K. Linear Regression Analysis with Fuzzy Model. IEEE Trans. Syst. Man Cybern. 1982, 12, 903–907.

Tanaka, H.; Hayashi, I.;Watada, J. Possibilistic Linear Regression Analysis For Fuzzy Data. Eur. J. Oper. Res. 1989, 40, 389–396. [CrossRef]

Chang, Y.H.O.; Ayyub, B.M. Fuzzy Regression Methods-A Comparative Assessment. Fuzzy Sets Syst. 2001, 199, 187–203. [CrossRef]

Wooldridge, M. Introductory Econometrics A Modern Approach, 3rd ed.; South-Western: Mason, OH, USA, 2006.

Hanke, J.E.;Wichern, D.W. Business Forecasting, 9th ed.; Pearson/Prentice Hall: Hoboken, NJ, USA, 2009.

BPS. Available online: https://www.bps.go.id/subject/7/energi.html#subjekViewTab3 (accessed on 20 June 2023).

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