Seasonal Short-Term Electricity Demand Forecasting under Tropical Condition using Fuzzy Approach Model

Authors

  • Yusri Syam Akil Dept. of Electrical Engineering, Hasanuddin University, Makassar 90245, Indonesia.
  • Yasunori Mitani Dept. of Electrical and Electronic Engineering, Kyushu Institute of Technology, Kitakyushu 804-8550, Japan.

Keywords:

Short Term Load Forecasting, Tropical Condition, Fuzzy Approach, Dry Season, Rainy Season,

Abstract

Concern of this work is analysis and short-term electricity demand forecasting under tropical condition using fuzzy approach. Two different demand models are proposed for dry season and rainy season to forecast a total load demand in Makassar, Indonesia for 24 hours ahead in each season. Based on the typical characteristic of seasonal demand, three inputs (time of load, temperature, and type of day) are used for load model in dry season, and four inputs (time of load, temperature, type of day, and rainfall) for load model in rainy season. Meanwhile, output is estimated load in related seasons. Some forecasting error analyses are applied to models. Under tested cases, both seasonal models have good forecasting results with MAPE values smaller than 2.95%. Estimated demand values when holidays and non-holidays in each season which are relatively close to actual load have confirmed effectiveness of the fuzzy based models.

References

Çevik, H.H., and Çunkaş, M.,“Short-tem load forecasting using fuzzy logic and ANFIS”,Neural Computing & Applications, No. 26,

pp.1355-1367, 2015.

Al-Kandari, A.M., Soliman, S.A., and El-Hawary, M.E.,“Fuzzy shortterm electric load forecasting”Electrical Power and Energy Systems, No. 26, pp.111-122, 2004.

Almeshaiei, E., and Soltan, H.,“A methodology for electric power load forecasting”, Alexandria Engineering Journal, No. 50, pp.137-144, 2011.

Amjady, N., and Keynia, F.,“Short-term load forecasting of power systems by combination of wavelet transform and neuro-evolutionary algorithm”,Energy, No. 34, pp.46-57, 2009.

Hamzacebi, C., and Es, H.A., “Forecasting the annual electricity consumption of Turkey using an optimized grey model”, Energy, No. 70, pp.165-171, 2014.

Kavousi-Fard, A., Samet, H., and Marzbani, F., “A new hybrid modifed firefly algorithm and support vector regression model for accurate short term load forecasting”, Expert Systems with Applications, No. 41, pp.6047-6056, 2014.

Mamlook, R., Badran, O., and Abdulhadi, E.,“A fuzzy inference model for short-term load forecasting”,Energy Policy, No. 37, pp.1239-1248, 2009.

Pandian, S.C., Duraiswamy, K., Rajan, C.C.A., and Kanagaraj, N.,“Fuzzy approach for short term load forecasting”,Electric Power Systems Research, No. 76, pp.541-548, 2006.

Vu, D.H., Muttaqi, K.M., and Agalgaonkar, A.P., “A Variance

inflation factor and backward elimination based robust regression

model for forecasting monthly electricity demand using climatic

variables”,Applied Energy, No. 140, pp.385-394, 2015.

BPS – Statistics Sulawesi Selatan Province, Sulawesi Selatan in Figure (2012).

Akil, Y.S., Syafaruddin, Waris, T., and Lateko, A.A.H.,“The

influence of meteorological parameters under tropical condition on electricity demand characteristic: Indonesia case study”The 1st International Conference on Information Technology, Computer,

and Electrical Engineering (ICITACEE), pp.381-385, 2014.

O’Neill, B.C., and Desai, M.,“Accuracy of past projections of US energy consumption”,Energy Policy, No. 33, pp.979-993, 2005.

Winebrake, J.J., and Sakva, D.,“An evaluation of errors in US energy forecasts: 1982-2003”,Energy Policy, No. 34, pp.3475-3483, 2006.

Hahn, H., Meyer-Nieberg, S., and Pickl, S.,“Electric load forecasting methods: Tools for decision making”, European Journal of Operational Research, No. 199, pp.902-907, 2009.

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Published

2017-03-15

How to Cite

Syam Akil, Y., & Mitani, Y. (2017). Seasonal Short-Term Electricity Demand Forecasting under Tropical Condition using Fuzzy Approach Model. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(1-3), 77–82. Retrieved from https://jtec.utem.edu.my/jtec/article/view/1748

Issue

Vol. 9 No. 1-3: Smart City Technology in Communication and Computer Engineering I

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Articles

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