Hourly water demand forecasting for micro water grids

Lee, Juneseok; Chae, Soo-Kwon

doi:10.2166/aqua.2015.144

Cited by 4 publications

(4 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An ARIMA model is denoted as ARIMA(p,d,q) and is expressed using the mathematical formulations given in Equations (4.1)-(4.4) (Lee & Chae, 2016):…”

Section: Arima Modelmentioning

confidence: 99%

“…where ∅ = autoregressive or damping parameter; θ = moving average parameter; µ = mean value of the process; ε t = forecast error at time t, in which ε t is assumed to follow a normal (0, σ) distribution, σ = standard deviation of the process (Lee & Chae, 2016). Equation (4.1) defines an autoregressive process of order p, AR(p), 'which predicts values from previous values'; Equation (4.2) defines a moving average process of order q, MA(q), 'which accounts for previous random trends'; Equation (4.3) defines an autoregressive moving average process of order (p,q), ARMA(p,q); and Equation (4.4) defines an autoregressive integrated moving average process of order (p,q) differenced by order d, ARIMA(p,d,q) (Lee & Chae, 2016).…”

Section: Arima Modelmentioning

confidence: 99%

“…The results showed that the ARIMA models were more accurate for analyzing the water treatment operations using a weekly timescale compared to a daily timescale 'due to significant daily variations in the control parameters of water quality in the plant' (Redondo et al, 2018). Lee and Chae (2016) developed seasonal ARIMA models to make hourly water demand forecasting for micro water grids (Lee & Chae,…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Embracing Analytics in the Drinking Water Industry

2022

View full text Add to dashboard Cite

Analytics can support numerous aspects of water industry planning, management, and operations. Given this wide range of touchpoints and applications, it is becoming increasingly imperative that the championship and capability of broad-based analytics needs to be developed and practically integrated to address the current and transitional challenges facing the drinking water industry. Analytics will contribute substantially to future efforts to provide innovative solutions that make the water industry more sustainable and resilient. The purpose of this book is to introduce analytics to practicing water engineers so they can deploy the covered subjects, approaches, and detailed techniques in their daily operations, management, and decision-making processes. Also, undergraduate students as well as early graduate students who are in the water concentrations will be exposed to established analytical techniques, along with many methods that are currently considered to be new or emerging/maturing. This book covers a broad spectrum of water industry analytics topics in an easy-to-follow manner. The overall background and contexts are motivated by (and directly drawn from) actual water utility projects that the authors have worked on numerous recent years. The authors strongly believe that the water industry should embrace and integrate data-driven fundamentals and methods into their daily operations and decision-making process(es) to replace established “rule-of-thumb” and weak heuristic approaches – and an analytics viewpoint, approach, and culture is key to this industry transformation. ISBN: 9781789062373 (paperback) ISBN: 9781789062380 (eBook) ISBN: 9781789062397 (ePub)

show abstract

“…An ARIMA model is denoted as ARIMA(p,d,q) and is expressed using the mathematical formulations given in Equations (4.1)-(4.4) (Lee & Chae, 2016):…”

Section: Arima Modelmentioning

confidence: 99%

Section: Arima Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Embracing Analytics in the Drinking Water Industry

2022

View full text Add to dashboard Cite

show abstract

“…For example, after converting to a smart metering system, the City of Santa Maria in California reduced their water losses from 6 to 2% (Godwin 2011). Mathematical models have been developed and applied to the smart water systems in a building scale (Lee & Chae 2015).…”

Section: Analytical Developmentsmentioning

confidence: 99%

Decadal review of residential water demand analysis from a practical perspective

Tanverakul

Lee

2016

Water Practice and Technology

Self Cite

View full text Add to dashboard Cite

Residential water demand has been extensively studied over more than four decades, but as yet there is no consensus on the best or most appropriate model from a practical perspective. Conservation and sustainability programs with new metering incentives have increased the necessity for an easy to use forecasting model for water resource management based on a better understanding of the factors driving residential water demand. Analytical techniques have increased in complexity, advancing with new tools for computing and data collection such as GIS and remote sensing. This paper presents a decadal review of how the residential water demand analysis have evolved over time with changes in price policies, conservation attitudes, technological improvements, and other factors explaining water demand. This paper will help provide the knowledge base for the future studies and recommendations are made for addressing/bridging the gap between drinking water industry and research community.

show abstract