A Comparison of Statistical Prediction Models in Household Water End-Uses

Myoung, Sungmin; Lee, Doo-Jin; Kim, Hwa-Soo; Jo, Jinnam

doi:10.5351/kjas.2011.24.4.567

Cited by 4 publications

(2 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, Kim et al (2007) and Kanakoudis and Tsitsifli (2013) monitored water demand patterns and their side effects in terms of hours in the day, days in the week, season, and weather [8,9]. Myeong et al (2011) studied water demand with spatial patterns, such as the house type, number of rooms in the house, house area, and population in the house, by statistical analysis [10]. In addition, Kim et al (2012) studied leakage as one of the reasons for energy overconsumption and developed a strategic model of leakage detection by demand pattern analysis [11].…”

Section: Introductionmentioning

confidence: 99%

Energy Cost Optimization for Water Distribution Networks Using Demand Pattern and Storage Facilities

Chang¹,

Choi

Kim³

et al. 2018

Sustainability

View full text Add to dashboard Cite

Energy consumption in water supply systems is closely connected with the demand for water, since energy is mostly consumed in the process of water transport and distribution, in addition to the energy that might be needed to pump the water from its sources. Existing studies have been carried out on optimizing the pump operations to attain appropriate pressure and on controlling the water level of storage facilities to transfer the required demand and to reduce the energy cost. The idea is to reduce the amount of the water being supplied when the unit price of energy is high and to increase the supply when the unit price is low. To realize this scheme, the energy consumption of water supply systems, the amount of water transfer, the organization of energy cost structure, the utilization of water tanks, and so forth are investigated and analyzed to establish a model of optimized water demand management based on the application of water tanks in supplied areas. In this study, with the assumption that energy cost can be reduced by the redistribution of a demand pattern, a numerical analysis is conducted on transferring water demand at storage facilities from the peak energy cost hours to the lower energy cost hours. This study was applied at the Bupyeong 2 reservoir catchment, Incheon, Korea.

show abstract

Section: Introductionmentioning

confidence: 99%

Energy Cost Optimization for Water Distribution Networks Using Demand Pattern and Storage Facilities

Chang¹,

Choi

Kim³

et al. 2018

Sustainability

View full text Add to dashboard Cite

show abstract

“…The authors in [7] developed a predictive model for household water end-uses using data collected in Korea over three years. However, the measured data was not fitted to normal distribution.…”

Section: Introductionmentioning

confidence: 99%

Energy Optimization and Cost Reduction in Water Distribution Networks

Tarek

Mahmoud

Ismail

2022

2022 13th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP)

View full text Add to dashboard Cite

Since the majority of energy consumed by water supply systems is used in transporting and distributing water, in addition to the energy required to pump the water from its sources, energy consumption is significantly associated with the water demand. Several studies have been carried out to optimize pump operations to achieve appropriate pressure and reduce the energy associated with controlling water levels in storage facilities. In this paper, we develop an optimization and decision support technique for a Water Distribution Network (WDN) that considers energy efficiency by limiting the energy consumption of transport and distribution water operations. Therefore, three considerations are taken into account to reduce energy usage, which are: a) the tank demand pattern is redistributed using a genetic algorithm; b) the reservoir serving pattern is governed by game theory c) a decision-making algorithm is also proposed to select the bestsuited controlling setting of the pump and valves based on the other two considerations.

show abstract

An analysis of time series models for toilet and laundry water-uses

Myoung¹,

Kim²,

Lee³

et al. 2013

Journal of the Korean Data and Information Science Society

Self Cite

View full text Add to dashboard Cite

End-uses of household water have been influenced by a housing type, life style and housing area which are considered as internal factors. Also, there are external factors such as water rate, weather and water supply facilities.Analysis of influential factors on water consumption in households would give an explanation on the cause of changing trends and would help predicting the water demand of end-use in household. In this paper, we used real data to predict toilet and laundry water-uses and utilized the linear regression model with autoregressive errors. The results showed that the monthly autoregressive error models explained about 71% for describing the water demand of end-use in toilet and laundry water-uses.

show abstract

A Comparison of Statistical Prediction Models in Household Water End-Uses

Cited by 4 publications

References 2 publications

Energy Cost Optimization for Water Distribution Networks Using Demand Pattern and Storage Facilities

Energy Cost Optimization for Water Distribution Networks Using Demand Pattern and Storage Facilities

Energy Optimization and Cost Reduction in Water Distribution Networks

An analysis of time series models for toilet and laundry water-uses

Contact Info

Product

Resources

About