Evaluating the Impact of Meteorological Factors on Water Demand in the Las Vegas Valley Using Time-Series Analysis: 1990–2014

Huntra, Patcha; Keener, Tim C.

doi:10.3390/ijgi6080249

Cited by 18 publications

(4 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The technique proposed in [19] utilizes a non-homogeneous Markov chain model, allowing for an understanding of the water consumption dynamics. This model can predict daily consumption patterns based on additional inputs, such as external factors like weather conditions [20], the day of the week, etc. A different study [21] concentrates on estimating water demand on a weekly and hourly basis using an autoregressive model that is based on a time-based periodic component of time series data to refine daily demand estimates and hours.…”

Section: Related Workmentioning

confidence: 99%

Retracted: Real-Time Water and Electricity Consumption Monitoring Using Machine Learning Techniques

Bashir

2023

IEEE Access

View full text Add to dashboard Cite

This work studies the automatic classification of water consumption patterns and electrical devices, both supervised and unsupervised. This involves training machine learning algorithms to identify normal and abnormal water consumption patterns and differentiate between different types of electrical devices. We performed an unsupervised classification of consumer water patterns in direct and indirect ways. The first is to use the raw consumption patterns obtained directly from the server. The second one corresponds to the use of the sampled consumption patterns. This classification is performed using hierarchical bottom-up classification and a self-organizing map. A probabilistic analysis of daily water consumption is performed to extract the percentage of daily consumption with the most information. It enables us to identify water consumption patterns more quickly by reducing the number of data points for each daily pattern, allowing us to recognize and classify anomalous behaviors as soon as feasible. Then, the signatures of electrical devices are classified using three ML algorithms: multilayer perceptron (MLP), k nearest neighbor (KNN), and decision tree (DT). Furthermore, assembly approaches are also studied. These are based on the OAA (One Against All) principle, which presents one class against all other classes, and the ECOC (Error-Correcting Output Codes) philosophy, which allows the classification error to be corrected. According to the bias/variance trade-off, both techniques enhanced classification by ensuring accuracy and generalization. A more in-depth analysis of the properties of the electrical devices is handled with an esemplastic approach based on gradient-boosting decision trees. The features of electrical devices can be extracted using this analysis based on the nature of their harmonic signatures.

show abstract

Section: Related Workmentioning

confidence: 99%

Retracted: Real-Time Water and Electricity Consumption Monitoring Using Machine Learning Techniques

Bashir

2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Quevedo et al [32] assessed the effects of seasonal ARIMA (SARIMA) and exponential smoothing models that took calendar effects into account and showed that they were superior in forecasting water demand when temporal and daily periods were considered. Furthermore, Patcha et al [33] demonstrated that the ARIMAX model with dew point depression and average temperature input plays an important role in forecasting long-term water consumption rates in Las Vegas.…”

Section: Literature Reviewmentioning

confidence: 99%

Short-Term Bathwater Demand Forecasting for Shared Shower Rooms in Smart Campuses Using Machine Learning Methods

Zhang

Yang

et al. 2022

Water

View full text Add to dashboard Cite

Water scarcity is a growing threat to humankind. At university campuses, there is a need for shared shower room managers to forecast the demand for bath water accurately. Accurate bath water demand forecasts can decrease the costs of water heating and pumping, reduce overall energy consumption, and improve student satisfaction (due to stability of bath water supply and bathwater temperature). We present a case study conducted at Capital Normal University (Beijing, China), which provides shared shower rooms separately for female and male students. Bath water consumption data are collected in real-time through shower tap controllers to forecast short-term bath water consumption in the shower buildings. We forecasted and compared daily and hourly bath water demand using the autoregressive integrated moving average, random forests, long short-term memory, and neural basis expansion analysis time series-forecasting models, and assessed the models’ performance using the mean absolute error, mean absolute percentage error, root-mean-square error, and coefficient of determination equations. Subsequently, covariates such as weather information, student behavior, and calendars were used to improve the models’ performance. These models achieved highly accurate forecasting for all the shower room areas. The results imply that machine learning methods outperform statistical methods (particularly for larger datasets) and can be employed to make accurate bath water demand forecasts.

show abstract

“…In fact, the approach proposed in [17] is based on a model of nonhomogeneous Markov chains allowing knowledge of the dynamics of water consumption. This model can predict behaviors of daily consumption based on other parameters such as exogenous factors represented by the climate [18], the day type, etc. Another study [19] deals with the water demand forecasting on weekly and hourly scales with an autoregressive model based on a periodic component on time series data to refine daily demand values and hours.…”

Section: Forecasting With Machine-learning Algorithmsmentioning

confidence: 99%

A Real-Time Data Analysis Platform for Short-Term Water Consumption Forecasting with Machine Learning

Boudhaouia

Wira

2021

Forecasting

View full text Add to dashboard Cite

This article presents a real-time data analysis platform to forecast water consumption with Machine-Learning (ML) techniques. The strategy fully relies on a web-oriented architecture to ensure better management and optimized monitoring of water consumption. This monitoring is carried out through a communicating system for collecting data in the form of unevenly spaced time series. The platform is completed by learning capabilities to analyze and forecast water consumption. The analysis consists of checking the data integrity and inconsistency, in looking for missing data, and in detecting abnormal consumption. Forecasting is based on the Long Short-Term Memory (LSTM) and the Back-Propagation Neural Network (BPNN). After evaluation, results show that the ML approaches can predict water consumption without having prior knowledge about the data and the users. The LSTM approach, by being able to grab the long-term dependencies between time steps of water consumption, allows the prediction of the amount of consumed water in the next hour with an error of some liters and the instants of the 5 next consumed liters in some milliseconds.

show abstract

Evaluating the Impact of Meteorological Factors on Water Demand in the Las Vegas Valley Using Time-Series Analysis: 1990–2014

Cited by 18 publications

References 20 publications

Retracted: Real-Time Water and Electricity Consumption Monitoring Using Machine Learning Techniques

Retracted: Real-Time Water and Electricity Consumption Monitoring Using Machine Learning Techniques

Short-Term Bathwater Demand Forecasting for Shared Shower Rooms in Smart Campuses Using Machine Learning Methods

A Real-Time Data Analysis Platform for Short-Term Water Consumption Forecasting with Machine Learning

Contact Info

Product

Resources

About