Appropriate Models for Forecasting of Water Supply Consumption in Ubonratchathani Province
Abstract
At present, water supply consumption in Ubonratchathani province is increasing. Forecasting water consumption of people using appropriate and accurate model is very important and useful for planning the measures to support the increased use of water supply. The purpose of this research was to construct a model suitable for the time series of water consumption of people in Ubonratchathani province in 4 stations, including Ubonratchathani Station, Phipun mangsahan Station, Det Udom Station, and Khemarat Station. The data of 168 values were obtained from the website of Provincial Waterworks Authority from January 2004 to December 2017. The data were divided into two sets. Set 1 was the data from January 2004 to December 2016, of which 156 values were used for constructing univariate time series based on the method of Box – Jenkins and the hybrid model of the technique of Box – Jenkins and support vector regression. Set 2 was the data from January to December 2017, of which 12 values were used for the comparison of the accuracy of forecasting of each station by using Mean Absolute Error (MAE ) and Mean Absolute Percent Error (MAPE). The results showed that the hybrid model was more accurate in forecasting than a single model due to the lowest MAE and MAPE values. The hybrid model can be used as a tool to forecast the amount of water supply of people in Ubon Ratchathani Province appropriately.It can also be used for decision making for planning water supply management to meet the needs of people in the future. Keywords: water supply consumption, forecasting, Box – Jenkins, hybrid forecasting, Ubonratchathani ProvinceReferences
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Box, G.E.P., Jenkins, G.M., Reinsel, G.C., Ljung, G.M., (2008). Time Series Analysis: Forecasting and
Control, 4th ed. John Wiley and Sons, Inc., New Jersey.
Chen, Y., Xu, P., Chu, Y., Li, W., Wu, Y., Ni, L., Wang, K. (2017). Short-term electrical load forecasting using the
Support Vector Regression (SVR) model to calculate the demand response baseline for office buildings.
Applied Energy, 195, 659–670. https://doi.org/10.1016/j.apenergy.2017.03.034
Cheng, H., Ding, X., Zhou, W., & Ding, R. (2019). A hybrid electricity price forecasting model with Bayesian optimization for German energy exchange. International Journal of Electrical Power & Energy Systems, 110, 653–666. https://doi.org/10.1016/j.ijepes.2019.03.056
Cheng, Y., Zhang, H., Liu, Z., Chen, L., & Wang, P. (2019). Hybrid algorithm for short-term forecasting of PM2.5 in
China. Atmospheric Environment, 200, 264–279. https://doi.org/10.1016/j.atmosenv.2018.12.025
Curceac, S., Ternynck, C., Ouarda, T. B. M. J., Chebana, F., & Niang, S. D. (2019). Short-term air temperature
forecasting using Nonparametric Functional Data Analysis and SARMA models. Environmental Modelling &
Software, 111, 394–408. https://doi.org/10.1016/j.envsoft.2018.09.017
de Marcos, R. A., Bello, A., & Reneses, J. (2019). Electricity price forecasting in the short term hybridising
fundamental and econometric modelling. Electric Power Systems Research, 167, 240–251.
https://doi.org/10.1016/j.epsr.2018.10.034
de Oliveira, E. M., & Cyrino Oliveira, F. L. (2018). Forecasting mid-long term electric energy consumption through
bagging ARIMA and exponential smoothing methods. Energy, 144, 776–788.
https://doi.org/10.1016/j.energy.2017.12.049
Junsagoon, S. (2015). Time Series Forecasting : Linear Approach, Non – Linear Approach and Hybrid Models.
EAU HERITAGE JOURNAL Science and Technology, 9(2) ,50 – 63. (in Thai)
Mao, Q., Zhang, K., Yan, W., & Cheng, C. (2018). Forecasting the incidence of tuberculosis in China using the
seasonal auto-regressive integrated moving average (SARIMA) model. Journal of Infection and Public
Health, 11(5), 707–712. https://doi.org/10.1016/j.jiph.2018.04.009
Meyer, D.,Dimitriadou,E., Hornik, K., Weingessel, A., Leisch, F.,Chang, C.C., & Lin, C.C. (2015). Misc Functions of
the Department of Statistics,Probability Theory Group.
http://cran.r-project.org/web/packages/e1071/index.html,5 April 2018.
Mohammadi, K., Shamshirband, S., Tong, C. W., Arif, M., Petković, D., & Ch, S. (2015). A new hybrid support
vector machine–wavelet transform approach for estimation of horizontal global solar radiation. Energy
Conversion and Management, 92, 162–171. https://doi.org/10.1016/j.enconman.2014.12.050
Provincial Waterworks Authority. Data water consumption. 2018. Retrieved February 20, 2018,
from: https://www.pwa.co.th/province/ (in Thai)
R Core Team. (2017). R: A language and environment for statistical computing. R Foundation for Statistical
Computing, Vienna, Austria . Retrieved February 20, 2018, from https://www.R-project.org/.
R Core Team. (2019). forecast: Forecasting Functions for Time Series and Linear Models. R Foundation
for Statistical Computing, Vienna, Austria. Retrieved February 20, 2018, from
https://cran.r-project.org/web/packages/forecast/
Sujjaviriyasup, T. (2019). Hybrid Model of Linear and Nonlinear Model for Forecasting Annual Molumes of
International Rice Exports. Burapha Science Journal, 24(2) , 517 – 531. (in Thai)
Tongpitak, N., Matchima, K., Sutthiaion, T. and Thepchim, S. (2017). Forecasting of the Water Consumption of the
Resident Residing in the Urban Areas of Muang Ubon and Warinchamra Districts. JOURNAL OF
INDUSTRIAL TECHNOLOGY UBON RATCHATANI RAJABHAT UNIVERSITY, 7(1), 58 – 74. (in Thai)
Vapnik, V. (1998). The Nature of Statistical Learning Theory. Springer-Verlag.
Xu, S., Chan, H. K., & Zhang, T. (2019). Forecasting the demand of the aviation industry using hybrid time series
SARIMA-SVR approach. Transportation Research Part E: Logistics and Transportation Review, 122,
169–180. https://doi.org/10.1016/j.tre.2018.12.005
Yang, Z., & Wang, J. (2018). A hybrid forecasting approach applied in wind speed forecasting based on a data processing strategy and an optimized artificial intelligence algorithm. Energy, 160, 87–100. https://doi.org/10.1016/j.energy.2018.07.005
Yaslan, Y., & Bican, B. (2017). Empirical mode decomposition based denoising method with support vector regression for time series prediction: A case study for electricity load forecasting. Measurement, 103,
52–61. https://doi.org/10.1016/j.measurement.2017.02.007
Zhang, G. P. (2003). Time series forecasting using a hybrid ARIMA and neural network model.
Neurocomputiong. 50, 159 – 175. https://doi.org/10.1016/S0925-2312(01)00702-0
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2020-01-08
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