Commercial greenhouse water demand sensitivity analysis: single crop case study

2016 ◽  
Vol 16 (5) ◽  
pp. 1185-1197 ◽  
Author(s):  
Dean C. J. Rice ◽  
Rupp Carriveau ◽  
David S.-K. Ting
Keyword(s):  
Sensitivity Analysis ◽  
Water Demand ◽  
Water Distribution ◽  
Demand Forecasting ◽  
Time Of Day ◽  
Bell Peppers ◽  
Demand Forecasting Model ◽  
Large Water ◽  
Demand Sensitivity

Today water distribution utilities are trying to improve operational efficiency through increased demand intelligence from their largest customers. Moving to prognostic operations allows utilities to optimally schedule and scale resources to meet demand more reliably and economically. Commercial greenhouses are large water consumers. In order to produce effective forecasting models for greenhouse water demand, the factors that drive demand must be enumerated and prioritized. In this study greenhouse water demand was modeled using artificial neural networks trained with a dataset containing eight input factors for a commercial greenhouse growing bell peppers. The dataset contained water usage, climatic and temporal data for the years 2012–2014. This model was then evaluated using the Extended Fourier Amplitude Sensitivity Test, a global sensitivity analysis, in order to determine the importance, or sensitivity, of each input factor. It was found that time of day, solar radiation, and outdoor temperature (°C) had the largest effects on the model output. These outputs could be used to contribute to the generation of a simplified demand-forecasting model.

scholarly journals Models for forecasting water demand using time series analysis: a case study in Southern Brazil

2021 ◽  
Author(s):  
Danielle C. M. Ristow ◽  
Elisa Henning ◽  
Andreza Kalbusch ◽  
Cesar E. Petersen
Keyword(s):  
Time Series ◽  
Water Consumption ◽  
Water Demand ◽  
Web Application ◽  
Water Resource Management ◽  
Distribution Systems ◽  
Water Distribution ◽  
Demand Forecasting ◽  
Model Quality

Abstract Technology has been increasingly applied in search for excellence in water resource management. Tools such as demand-forecasting models provide information for utility companies to make operational, tactical and strategic decisions. Also, the performance of water distribution systems can be improved by anticipating consumption values. This work aimed to develop models to conduct monthly urban water demand forecasts by analyzing time series, and adjusting and testing forecast models by consumption category, which can be applied to any location. Open language R was used, with automatic procedures for selection, adjustment, model quality assessment and forecasts. The case study was conducted in the city of Joinville, with water consumption forecasts for the first semester of 2018. The results showed that the seasonal ARIMA method proved to be more adequate to predict water consumption in four out of five categories, with mean absolute percentage errors varying from 1.19 to 15.74%. In addition, a web application to conduct water consumption forecasts was developed.

scholarly journals Variable selection methods for water demand forecasting in Ethiopia: Case study Gondar town

2018 ◽  
Vol 4 (1) ◽  
pp. 1537067 ◽  
Author(s):  
Mohammed Gedefaw ◽  
Wang Hao ◽  
Yan Denghua ◽  
Abel Girma ◽  
Mustafa Ibrahim Khamis
Keyword(s):  
Variable Selection ◽  
Water Demand ◽  
Demand Forecasting ◽  
Selection Methods ◽  
Water Demand Forecasting ◽  
Gondar Town

scholarly journals Artificial Neural Networks for Urban Water Demand Forecasting: A Case Study

2019 ◽  
Vol 1284 ◽  
pp. 012004 ◽  
Author(s):  
Leandro L Lorente-Leyva ◽  
Jairo F Pavón-Valencia ◽  
Yakcleem Montero-Santos ◽  
Israel D Herrera-Granda ◽  
Erick P Herrera-Granda ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Water Demand ◽  
Demand Forecasting ◽  
Urban Water ◽  
Water Demand Forecasting ◽  
Urban Water Demand ◽  
Artificial Neural

scholarly journals Water demand forecasting for the optimal operation of large-scale drinking water networks: The Barcelona Case Study.

2014 ◽  
Vol 47 (3) ◽  
pp. 10457-10462 ◽  
Author(s):  
Ajay Kumar Sampathirao ◽  
Juan Manuel Grosso ◽  
Pantelis Sopasakis ◽  
Carlos Ocampo-Martinez ◽  
Alberto Bemporad ◽  
...  
Keyword(s):  
Drinking Water ◽  
Water Demand ◽  
Large Scale ◽  
Demand Forecasting ◽  
Optimal Operation ◽  
Water Networks ◽  
Water Demand Forecasting

scholarly journals Charging for Scarce Rail Capacity in Britain: A Case Study

2008 ◽  
Vol 7 (1) ◽  
Author(s):  
Daniel Johnson ◽  
Chris Nash
Keyword(s):  
Opportunity Cost ◽  
East Coast ◽  
Demand Forecasting ◽  
Main Line ◽  
Forecasting Model ◽  
Passenger Demand ◽  
Demand Forecasting Model ◽  
The Way

The aim of this paper is to examine the feasibility of identifying an appropriate rail scarcity charge which would make operators pay for their use of rail capacity in line with the opportunity cost of the use of these slots and to give some idea of the likely effects of such charges. The way in which we do this is to use a passenger demand forecasting model, PRAISE, to consider a situation on the East Coast Main Line which is characterized by scarce capacity and a degree of competition.

scholarly journals Estimating High Resolution Temporal Scale of Water Demand Time Series – Disaggregation Approach (Case Study)

10.29007/4vfl ◽  
2018 ◽  
Author(s):  
Peyman Yousefi ◽  
Gholamreza Naser ◽  
Hadi Mohammadi
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Water Demand ◽  
Distribution System ◽  
Urban Areas ◽  
Water Distribution ◽  
Demand Forecasting ◽  
Temporal Scale ◽  
A New Technique ◽  
Cascade Method

A comprehensive understanding of water demand and its availability is essential for decision-makers to manage their resources and understand related risks effectively. Historical data play a crucial role in developing an integrated plan for management of water distribution system. The key is to provide high-resolution temporal-scale of demand data in urban areas. In the literature, many studies on water demand forecasting are available; most of them were focused on monthly-scales. Since monitoring of time series is a prolonged and costly procedure, the popularity of disaggregation methods is a most recent desirable trend. The objective of this research is to transfer low-resolution into high-resolution temporal scale using random cascade disaggregation and non-linear deterministic methods. This study defines a new technique to apply previously proposed random cascade method to disaggregate continuous data of the city of Peachland. The accuracy of the results is more than 90%. It represents a satisfactory application of the models. The proposed approach helps operators to have access to daily demand without acquiring high-resolution temporal scale values. Although the disaggregated values may not be precisely equal with observed values, it offers a practical solution for the low equipped WDS and leads to lesser number of drinking water-related problems.

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