Energy management strategies use short‐term water consumption forecasting to minimize cost of pumping operations

Jentgen, Larry; Kidder, Harold; Hill, R.; Conrad, Steve

doi:10.1002/j.1551-8833.2007.tb07957.x

Cited by 30 publications

(27 citation statements)

References 3 publications

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“…ANNs were introduced following Rosenblatt's concept of perceptron [22], and their application usually involves a comparative assessment of the performance with TSR models (e.g., feed-forward back-propagation neural network (FFBP-NN), generalized regression neural networks (GRNNs), radial basis neural networks (RBNNs), and MLR) [1,10,[23][24][25][26], with UTS models (e.g., dynamic artificial neural network (DAN2), ARIMA and FFBP-NN) [27] or with both UTS and TSR models (e.g., simple linear regression (SLR), MLR, UTS models, and ANN models) [5,28,29]. Nonetheless, in order to achieve the second objective of this paper, FFBP-NN (see Equation (5)) is considered [1].…”

Section: Artificial Neural Network (Ann) Forecasting Methodsmentioning

confidence: 99%

“…Forecasting with hybrid models (i.e., combined forecasts from two or more predictive models) has found wide application [6,11,24,[30][31][32][33][34], since it leads to better forecasting performance. For instance, Equation (6) is applied in a case where forecasts from different models are combined in order to obtain a hybrid forecast.…”

Section: Hybrid Forecasting Methodsmentioning

confidence: 99%

“…Useful for better prediction of daily water demand [24]. Relevant for setting water rates, revenue forecasting, and financial planning exercises.…”

Section: Hybrid Forecasting Methodsmentioning

confidence: 99%

“…ANN models: FFBP-NN, GRNN, RBNN, DAN2 [5,35] Used with TSR models [1,[24][25][26][27], with UTS models [27], or with both UTS and TSR models [5,28,29]. According to [11], ANN outperforms UTS and TSR models.…”

Section: Hybrid Forecasting Methodsmentioning

confidence: 99%

“…According to [11], ANN outperforms UTS and TSR models. However, the results of [24,25] were inconclusive.…”

Section: Hybrid Forecasting Methodsmentioning

confidence: 99%

See 4 more Smart Citations

Overview, Comparative Assessment and Recommendations of Forecasting Models for Short-Term Water Demand Prediction

Anele

Hamam

Abu‐Mahfouz

et al. 2017

Water

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Abstract:The stochastic nature of water consumption patterns during the day and week varies. Therefore, to continually provide water to consumers with appropriate quality, quantity and pressure, water utilities require accurate and appropriate short-term water demand (STWD) forecasts. In view of this, an overview of forecasting methods for STWD prediction is presented. Based on that, a comparative assessment of the performance of alternative forecasting models from the different methods is studied. Times series models (i.e., autoregressive (AR), moving average (MA), autoregressive-moving average (ARMA), and ARMA with exogenous variable (ARMAX)) introduced by Box and Jenkins (1970), feed-forward back-propagation neural network (FFBP-NN), and hybrid model (i.e., combined forecasts from ARMA and FFBP-NN) are compared with each other for a common set of data. Akaike information criterion (AIC), originally proposed by Akaike (1974) is used to estimate the quality of each short-term forecasting model. Furthermore, Nash-Sutcliffe (NS) model efficiency coefficient proposed by Nash-Sutcliffe (1970), root mean square error (RMSE) and mean absolute percentage error (MAPE) are the forecasting statistical terms used to assess the predictive performance of the models. Lastly, as regards the selection of an accurate and appropriate STWD forecasting model, this paper provides recommendations and future work based on the forecasts generated by each of the predictive models considered.

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Section: Artificial Neural Network (Ann) Forecasting Methodsmentioning

confidence: 99%

Section: Hybrid Forecasting Methodsmentioning

confidence: 99%

“…Useful for better prediction of daily water demand [24]. Relevant for setting water rates, revenue forecasting, and financial planning exercises.…”

Section: Hybrid Forecasting Methodsmentioning

confidence: 99%

Section: Hybrid Forecasting Methodsmentioning

confidence: 99%

“…According to [11], ANN outperforms UTS and TSR models. However, the results of [24,25] were inconclusive.…”

Section: Hybrid Forecasting Methodsmentioning

confidence: 99%

See 3 more Smart Citations

Overview, Comparative Assessment and Recommendations of Forecasting Models for Short-Term Water Demand Prediction

Anele

Hamam

Abu‐Mahfouz

et al. 2017

Water

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Assessment of the Present State and Future Fate of River Saraswati, India: Water Quality Indices and Forecast Models as Diagnostic and Management Tools

Pramanik

Kaviraj

Saha

2023

CLEAN Soil Air Water

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Water quality assessment is key to the conservation and management of rivers. River Saraswati, a distributary of the river Ganga, serves as a lifeline to many villages in the district Hooghly in West Bengal, India. As the river is gradually dying due to diverse man‐made pollution, ten water quality parameters in two sampling spots (PR‐1 and PR‐2) in the river are monitored month‐wise from March 2017 to February 2020, and these are compared with those from a reference pond. The water quality index (WQI) is determined for the two riverine spots and the reference pond based on the Canadian Council of Ministers of Environment WQI (CCMEWQI) and weighted arithmetic WQI, respectively. In addition to actual observations, three different forecasting methods, exponential smoothing, autoregressive integrated moving average, and artificial neural network, are used to predict WQI for the next two years. This study indicates that free CO2, dissolved oxygen, and turbidity are the key parameters to evaluate this river's anthropogenic stress and health. The actual and forecasted results reflect the precipitous degradation of CCMEWQI in PR‐2. Therefore, the immediate intervention of all stakeholders is required to adopt an integrated and comprehensive river management plan to save the river from utter obliteration.

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Leveraging thewater‐energynexus to derive benefits for the electric grid throughdemand‐sidemanagement in the water supply and wastewater sectors

et al. 2021

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Leveraging the potential flexibility of large electrical loads has become an attractive option for maintaining grid reliability, especially in electric grids with high penetrations of variable renewable energy. The water sector is a particularly attractive option for demand‐side flexibility due to its vast water storage infrastructure, large interruptible pumping loads, and energy generation opportunities. Shifting the timing of water supply and wastewater utility operations can reduce peak load and temporally align energy‐consuming activities with periods of cheap electricity prices and/or high renewable energy generation. This paper presents a general overview of demand‐side management strategies in water and wastewater systems, focusing primarily on demand‐response measures. We find that while there is consensus in the literature about the potential for water systems to provide flexible demand‐side management services, there is a need for developing comprehensive water‐energy models to examine the value of load management as a source of revenue for water utilities and as a source of flexibility for the electric grid. More experimental studies and simulation efforts are also needed to address the technical complexities and water quality concerns associated with interrupting water and wastewater utility operations. This article is categorized under: Engineering Water > Sustainable Engineering of Water Engineering Water > Planning Water

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Energy management strategies use short‐term water consumption forecasting to minimize cost of pumping operations

Cited by 30 publications

References 3 publications

Overview, Comparative Assessment and Recommendations of Forecasting Models for Short-Term Water Demand Prediction

Overview, Comparative Assessment and Recommendations of Forecasting Models for Short-Term Water Demand Prediction

Assessment of the Present State and Future Fate of River Saraswati, India: Water Quality Indices and Forecast Models as Diagnostic and Management Tools

Leveraging thewater‐energynexus to derive benefits for the electric grid throughdemand‐sidemanagement in the water supply and wastewater sectors

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