MODELING THE ELECTRICITY CONSUMPTION IN THE PROVINCE OF DAVAO ORIENTAL WITH AUTO-REGRESSIVE INTEGRATED MOVING AVERAGE

被引:0
|
作者
Salimaco, Rodrigo Amper, Jr. [1 ]
机构
[1] Davao Oriental State Univ, City Mati 8200, Davao Oriental, Philippines
关键词
seasonal ARIMA model; auto-regressive integrated moving average; ENERGY-CONSUMPTION;
D O I
10.17654/0972361722025
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
The inexorable ballooning population and industry had been showing a tantamount effect to the rapid increase of energy consumption worldwide. Thus, efficient energy utilization through effective management and appropriate prediction is ultimately needed for future consumption. In this study, seasonal ARIMA model was utilized in forecasting electricity consumption in the Province of Oriental using the monthly consumption from January 2004 to December 2020. Upon diagnostic checking with the use of AIC, SBC and MAPE; the ARIMA (1, 1, 0) x (0, 1, 1)(12) was found to be the best fit model to do the forecasting. Results show that with the most likely prediction, there is an increasing rate of the monthly consumption with a seasonal higher demand every August. It is also forecasted that there would be nearly 49% increase of the electricity consumption in the Province from 2015 towards 2025.
引用
收藏
页码:23 / 37
页数:15
相关论文
共 50 条
  • [1] Monthly streamflow forecasting with auto-regressive integrated moving average
    Nasir, Najah
    Samsudin, Ruhaidah
    Shabri, Ani
    [J]. 1ST INTERNATIONAL CONFERENCE ON APPLIED & INDUSTRIAL MATHEMATICS AND STATISTICS 2017 (ICOAIMS 2017), 2017, 890
  • [2] FORECASTING A MONTHLY ELECTRICITY CONSUMPTION USING AUTO-REGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) AND ARTIFICIAL NEURAL NETWORK (ANN) MODELS
    Cuarteros, Noel G., Jr.
    [J]. ADVANCES AND APPLICATIONS IN STATISTICS, 2022, 79 : 55 - 66
  • [3] Short-term electricity prices forecasting based on support vector regression and Auto-regressive integrated moving average modeling
    Che, Jinxing
    Wang, Jianzhou
    [J]. ENERGY CONVERSION AND MANAGEMENT, 2010, 51 (10) : 1911 - 1917
  • [4] Auto-regressive integrated moving average (ARIMA) modeling of cocoa production in Nigeria: 1900-2025
    Ajetomobi, Joshua Olusegun
    Olaleye, Adesola Olutayo
    [J]. JOURNAL OF CROP IMPROVEMENT, 2019, 33 (04) : 445 - 455
  • [5] Auto-Regressive Integrated Moving Average Threshold Influence Techniques for Stock Data Analysis
    Singh, Bhupinder
    Henge, Santosh Kumar
    Mandal, Sanjeev Kumar
    Yadav, Manoj Kumar
    Yadav, Poonam Tomar
    Upadhyay, Aditya
    Iyer, Srinivasan
    Gupta, Rajkumar A.
    [J]. INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2023, 14 (06) : 446 - 455
  • [6] Continuous auto-regressive moving average random fields on n
    Brockwell, Peter J.
    Matsuda, Yasumasa
    [J]. JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY, 2017, 79 (03) : 833 - 857
  • [7] The auto-regressive integrated moving average procedures:: Implications for adapted physical activity research
    Fortes, M
    Ninot, G
    Delignières, D
    [J]. ADAPTED PHYSICAL ACTIVITY QUARTERLY, 2005, 22 (03) : 221 - 236
  • [8] A Study of Wind Statistics Through Auto-Regressive and Moving-Average (ARMA) Modeling
    尹彰
    周宗仁
    [J]. China Ocean Engineering, 2001, (01) : 61 - 72
  • [9] A spatiotemporal auto-regressive moving average model for solar radiation
    Glasbey, C. A.
    Allcroft, D. J.
    [J]. JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS, 2008, 57 : 343 - 355
  • [10] A Novel Modified Auto-regressive Moving Average Hysteresis Model
    Li, Jiedong
    Tang, Hui
    Zhan, Boyu
    Zhang, Guixin
    Wu, Zelong
    Gao, Jian
    Chen, Xin
    Yang, Zhijun
    [J]. 2018 IEEE INTERNATIONAL CONFERENCE ON MANIPULATION, MANUFACTURING AND MEASUREMENT ON THE NANOSCALE (3M-NANO) - CONFERENCE PROCEEDINGS, 2018, : 278 - 282