Title

Commercial greenhouse water demand sensitivity analysis: Single crop case study

Document Type

Article

Publication Date

10-1-2016

Publication Title

Water Science and Technology: Water Supply

Volume

16

Issue

5

First Page

1185

Keywords

Commercial Greenhouse Watering, Neural Network, Sensitivity Analysis, Water Demand Characterization

Last Page

1197

Abstract

Todaywater 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 FourierAmplitude 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 (WC) had the largest effects on the model output. These outputs could be used to contribute to the generation of a simplified demand-forecasting model.

DOI

10.2166/ws.2016.031

ISSN

16069749

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