Improving the Performance of Water Demand Forecasting Models by Using Weather Input

Bakker, M.; Duist, H. Van; Schagen, Kim van; Vreeburg, J.H.G.; Rietveld, L.C.

doi:10.1016/j.proeng.2014.02.012

Cited by 79 publications

(48 citation statements)

References 25 publications

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“…Measuring forecast errors is crucial for the selection of the models' parameters as well as to monitor the accuracy and reliability of the generated forecasts; degradation of performance may require an updating of the models. The basic idea of any error measure consists of comparing forecasts with observations; several measures have been proposed, but the most widely adopted in the field of water demand forecasting is the Mean Absolute Percentage Error (MAPE) [2,7,[22][23][24]26]. This measure is denoted with:…”

Section: Support Vector Regression Based Demand Forecastingmentioning

confidence: 99%

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Clustering and Support Vector Regression for Water Demand Forecasting and Anomaly Detection

Candelieri

2017

Water

104

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This paper presents a completely data-driven and machine-learning-based approach, in two stages, to first characterize and then forecast hourly water demand in the short term with applications of two different data sources: urban water demand (SCADA data) and individual customer water consumption (AMR data). In the first case, reliable forecasting can be used to optimize operations, particularly the pumping schedule, in order to reduce energy-related costs, while in the second case, the comparison between forecast and actual values may support the online detection of anomalies, such as smart meter faults, fraud or possible cyber-physical attacks. Results are presented for a real case: the water distribution network in Milan.

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Section: Support Vector Regression Based Demand Forecastingmentioning

confidence: 99%

“…According to a recent study [2], water demand forecasts enabled a 3.1% reduction of energy consumption and a 5.2% reduction of energy costs at a Water Distribution Network (WDN) in the Netherlands.…”

Section: Introductionmentioning

confidence: 99%

Clustering and Support Vector Regression for Water Demand Forecasting and Anomaly Detection

Candelieri

2017

Water

104

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“…The city's major source of water is Lake Mead, which accounts for 90% of its water supply, while the rest is drawn from groundwater in the basin [3]. forecasting [5,9,21,22], but this study could be utilized for medium-to long-term forecasting. Lastly, we simulate water demand from the ARIMAX equations to estimate the sensitivity of water demand due to climate change impact.…”

Section: Study Areamentioning

confidence: 99%

“…Furthermore, none of the previous studies have developed ARIMAX equations specifying the number of lags on variables (for both deterministic and stochastic terms) to forecast monthly water demand. Additionally, time-series modeling has been predominately been used for short-term forecasting [5,9,21,22], but this study could be utilized for medium-to long-term forecasting. Lastly, we simulate water demand from the ARIMAX equations to estimate the sensitivity of water demand due to climate change impact.…”

Section: Introductionmentioning

confidence: 99%

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

Huntra¹,

Keener²

2017

IJGI

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Many factors impact a city's water consumption, including population distribution, average household income, water prices, water conservation programs, and climate. Of these, however, meteorological effects are considered to be the primary determinants of water consumption. In this study, the effects of climate on residential water consumption in Las Vegas, Nevada, were examined during the period from 1990 to 2014. The investigations found that climatic variables, including maximum temperature, minimum temperature, average temperature, precipitation, diurnal temperature, dew point depression, wind speed, wind direction, and percent of calm wind influenced water use. The multivariate autoregressive integrated moving average (ARIMAX) model found that the historical data of water consumption and dew point depression explain the highest percentage of variance (98.88%) in water use when dew point depression is used as an explanatory variable. Our results indicate that the ARIMAX model with dew point depression input, and average temperature, play a significant role in predicting long-term water consumption rates in Las Vegas. The sensitivity analysis results also show that the changes in average temperature impacted water demand three times more than dew point depression. The accuracy performance, specifically the mean average percentage error (MAPE), of the model's forecasting is found to be about 2-3% from five years out. This study can be adapted and utilized for the long-term forecasting of water demand in other regions. By using one significant climate factor and historical water demand for the forecasting, the ARIMAX model gives a forecast with high accuracy and provides an effective technique for monitoring the effects of climate change on water demand in the area.

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“…There is a common convention among water demand forecast modellers that short term forecasts are those targeting temporal resolutions hourly, daily, or weekly that are used for operational purposes of WDS [3]. Furthermore, other researchers considered temperature, precipitation, and humidity in their analysis [6][7][8][9][10][11]. The majority of the models in the literature are data-driven techniques, using water demand with a lead time to predict the future demand.…”

Section: Introductionmentioning

confidence: 99%

Gene Expression Programming Coupled with Unsupervised Learning: A Two-Stage Learning Process in Multi-Scale, Short-Term Water Demand Forecasts

Shabani

Candelieri

Archetti

et al. 2018

Water

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This article proposes a new general approach in short-term water demand forecasting based on a two-stage learning process that couples time-series clustering with gene expression programming (GEP). The approach was tested on the real life water demand data of the city of Milan, in Italy. Moreover, multi-scale modeling using a series of head-time was deployed to investigate the optimum temporal resolution under study. Multi-scale modeling was performed based on rearranging hourly based patterns of water demand into 3, 6, 12, and 24 h lead times. Results showed that GEP should receive more attention among the emerging nonlinear modelling techniques if coupled with unsupervised learning algorithms in detailed spherical k-means.

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Improving the Performance of Water Demand Forecasting Models by Using Weather Input

Cited by 79 publications

References 25 publications

Clustering and Support Vector Regression for Water Demand Forecasting and Anomaly Detection

Clustering and Support Vector Regression for Water Demand Forecasting and Anomaly Detection

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

Gene Expression Programming Coupled with Unsupervised Learning: A Two-Stage Learning Process in Multi-Scale, Short-Term Water Demand Forecasts

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