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 effect on the model output. These outputs could be used to contribute to the generation of a simplified demand-forecasting model.
- commercial greenhouse watering
- neural network
- sensitivity analysis
- water demand characterization
- First received 26 October 2015.
- Accepted in revised form 22 February 2016.
- © IWA Publishing 2016