Univariate Time Series Models For Forecasting Energy-Water Efficiency at Water Treatment Plant
Abstract
Water treatment is improving water quality by passing it through various processes in water treatment plants. One of the vital management in water treatment plants requires planning and strategy of water production volume, energy consumption, and energy-water efficiency. Besides, accurate energy-water efficiency forecasting results are an essential monitoring strategy for reducing electricity consumption per meter cubic of water production. However, without accurate and fast forecast results, decision-making and planning in water treatment plant management will be difficult. The univariate time series model is one option for management to cater to the time-consuming and low accuracy of forecasting problems. Therefore, this study investigates energy-water efficiency using six univariate forecasting models. The models were Naïve, Recursive Simple Average (RSA), 3-point Moving Average, 4-point Moving Average, 5-point Moving Average and Simple Exponential Smoothing models. The result shows that the RSA forecasting model is more accurate than the other five, with 0.24% mean absolute percentage error (MAPE), 0.00014 mean squared error (MSE) and 0.00897 mean absolute deviation (MAD). These three measurement errors are a measure of the forecasting accuracy of a forecasting method in statistics. Moreover, the RSA forecasting model shows the simple calculation and cheapest indicator to measure the energy-water efficiency of water treatment plants.
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About this article
Publication Date
29 November 2023
Article Doi
eBook ISBN
978-1-80296-131-7
Publisher
European Publisher
Volume
132
Print ISBN (optional)
-
Edition Number
1st Edition
Pages
1-816
Subjects
Accounting and finance, business and management, communication, law and governance
Cite this article as:
Mansor, R., Zaini, B. J., & Haris, H. A. (2023). Univariate Time Series Models For Forecasting Energy-Water Efficiency at Water Treatment Plant. In N. M. Suki, A. R. Mazlan, R. Azmi, N. A. Abdul Rahman, Z. Adnan, N. Hanafi, & R. Truell (Eds.), Strengthening Governance, Enhancing Integrity and Navigating Communication for Future Resilient Growth, vol 132. European Proceedings of Social and Behavioural Sciences (pp. 193-202). European Publisher. https://doi.org/10.15405/epsbs.2023.11.02.15