Year 2020, Volume 7 , Issue 3, Pages 95 - 100 2020-10-05

Power transformer demand forecast with Box Jenkins ARIMA model

Özlem KUVAT [1] , Ege ADALI [2]


Demand forecasting is based on the principle of trying to forecast the demand for the outputs of enterprises in the field of manufacturing or service for the next periods. It requires the estimation of various future scenarios, if necessary, taking measures and taking steps, and during the application phase, the technique that is most suitable for the characteristics of the examined data set is selected and used. As a result of a healthy analysis carried out in this way, detailed plans and strict measures can be taken for the unknown, negative scenarios of the future. This study analyzes the characteristics of a series of power transformers of a company operating in the electromechanical industry in the past years, and as a result of this analysis, the Box Jenkins Autoregressive Integrated Moving Average method (ARIMA), which best fits the results, is expected to occur for power transformers in the future. It was made to estimate the amount of demand. Within the scope of this study, firstly, the most suitable model was tried to be determined by taking into consideration the past 132 months data of PTS. It was decided that the best choice among the alternative models was the ARMA (4,4) x (0,1) 12 model. The model was found to be stable and it was decided that the root mean square error (RMSE), mean absolute percentage error (MAPE) and Theil inequality coefficient values determined in the performance measurements were appropriate.
ARIMA,, Box Jenkins,, Transformer demand forecast
  • Büyükşahin, Ü. and Ertekin, Ş. 2020. A feature-based hybrid ARIMA-ANN model for univariate time series forecasting. Journal of the Faculty of Engineering & Architecture of Gazi University, 35(1), 468-478.
  • Karakaş E. 2019. Forecasting Automotive Export Revenue of Turkey using ARIMA Model. Journal of Yasar University, 14/55, 318-328.
  • Kaya L., Doğan, Z. and Binici, T. 2015. A comparative investigation of alternative estimation methods in non-stationary time series: analysis of cotton price. Atatürk University Journal of Graduate School of Social Sciences 19(2), 401-421.
  • Contreras, J., Espinola, R., Nogales, F. J. and Conejo, A. J. 2003. ARIMA models to predict next-day electricity prices. IEEE Transactions On Power Systems, 18(3), 1014-1020.
  • Ordóñez, C., Lasheras, F. S., Roca-Pardiñas, J. and de Cos Juez, F. J. 2019. A hybrid ARIMA–SVM model for the study of the remaining useful life of aircraft engines. Journal of Computational and Applied Mathematics, 346, 184-191.
  • Lai, Y. and Dzombak, D. A. 2020. Use of the autoregressive ıntegrated moving average (ARIMA) model to forecast near-term regional temperature and precipitation. Weather and Forecasting, 35(3), 959-976.
  • Wang, H., Huang, J., Zhou, H., Zhao, L. and Yuan, Y. 2019. An integrated variational mode decomposition and ARIMA model to forecast air temperature. Sustainability, 11(15), 4018.
  • Guha, B. and Bandyopadhyay, G. (2016). Gold price forecasting using ARIMA model. Journal of Advanced Management Science, 4(2), 117-121.
  • Fattah, J., Ezzine, L., Aman, Z., El Moussami, H. and Lachhab, A. (2018). Forecasting of demand using ARIMA model. International Journal of Engineering Business Management, 10, 1847979018808673.
  • Dickey, D. A. and Fuller, W. A. 1979. Distribution of the estimators for autoregressive time series with a unit root. Journal of The American Statistical Association, 74(366a), 427-431.
  • Phillips, P. C. and Perron, P. 1988. Testing for a unit root in time series regression. Biometrika, 75(2), 335-346.
  • Kwiatkowski, D., Phillips, P. C., Schmidt, P. and Shin, Y. 1992. Testing the null hypothesis of stationarity against the alternative of a unit root. Journal of Econometrics, 54(1-3), 159-178.
  • Nyoni, T. 2019. Modeling and forecasting inflation in Tanzania using ARIMA models. MPRA Paper No. 92458, posted 03 Mar 2019 19:07 UTC, 12p.
  • Şahan M. and Okur Y. 2016. Estimation of the solar radiation using some meteorological data for the mediterranean region with the artificial neural network. SDU Journal of Science (E-Journal), 11(1), 61-71
  • Bozkurt, H. Y. Zaman Serileri Analizi, 2. Baskı, Ekin Kitapevi, Bursa, Türkiye, 2013.
Primary Language en
Subjects Engineering, Multidisciplinary
Journal Section Research Article
Authors

Orcid: 0000-0001-7017-4557
Author: Özlem KUVAT (Primary Author)
Institution: Balıkesir Üniversitesi
Country: Turkey


Orcid: 0000-0002-5739-5854
Author: Ege ADALI
Institution: BALIKESİR ÜNİVERSİTESİ
Country: Turkey


Dates

Application Date : July 20, 2020
Acceptance Date : September 29, 2020
Publication Date : October 5, 2020

Bibtex @research article { ijeat771010, journal = {International Journal of Energy Applications and Technologies}, issn = {}, eissn = {2548-060X}, address = {editor.ijeat@gmail.com}, publisher = {İlker ÖRS}, year = {2020}, volume = {7}, pages = {95 - 100}, doi = {10.31593/ijeat.771010}, title = {Power transformer demand forecast with Box Jenkins ARIMA model}, key = {cite}, author = {Kuvat, Özlem and Adalı, Ege} }
APA Kuvat, Ö , Adalı, E . (2020). Power transformer demand forecast with Box Jenkins ARIMA model . International Journal of Energy Applications and Technologies , 7 (3) , 95-100 . DOI: 10.31593/ijeat.771010
MLA Kuvat, Ö , Adalı, E . "Power transformer demand forecast with Box Jenkins ARIMA model" . International Journal of Energy Applications and Technologies 7 (2020 ): 95-100 <https://dergipark.org.tr/en/pub/ijeat/issue/57106/771010>
Chicago Kuvat, Ö , Adalı, E . "Power transformer demand forecast with Box Jenkins ARIMA model". International Journal of Energy Applications and Technologies 7 (2020 ): 95-100
RIS TY - JOUR T1 - Power transformer demand forecast with Box Jenkins ARIMA model AU - Özlem Kuvat , Ege Adalı Y1 - 2020 PY - 2020 N1 - doi: 10.31593/ijeat.771010 DO - 10.31593/ijeat.771010 T2 - International Journal of Energy Applications and Technologies JF - Journal JO - JOR SP - 95 EP - 100 VL - 7 IS - 3 SN - -2548-060X M3 - doi: 10.31593/ijeat.771010 UR - https://doi.org/10.31593/ijeat.771010 Y2 - 2020 ER -
EndNote %0 International Journal of Energy Applications and Technologies Power transformer demand forecast with Box Jenkins ARIMA model %A Özlem Kuvat , Ege Adalı %T Power transformer demand forecast with Box Jenkins ARIMA model %D 2020 %J International Journal of Energy Applications and Technologies %P -2548-060X %V 7 %N 3 %R doi: 10.31593/ijeat.771010 %U 10.31593/ijeat.771010
ISNAD Kuvat, Özlem , Adalı, Ege . "Power transformer demand forecast with Box Jenkins ARIMA model". International Journal of Energy Applications and Technologies 7 / 3 (October 2020): 95-100 . https://doi.org/10.31593/ijeat.771010
AMA Kuvat Ö , Adalı E . Power transformer demand forecast with Box Jenkins ARIMA model. IJEAT. 2020; 7(3): 95-100.
Vancouver Kuvat Ö , Adalı E . Power transformer demand forecast with Box Jenkins ARIMA model. International Journal of Energy Applications and Technologies. 2020; 7(3): 95-100.