Abstract
Forecasting crop water demand is a critical part of any greenhouse’s day-to-day operations. This study focuses on a region located in Essex County, Ontario Canada where water demand is dominated by commercial greenhouse operations (78% of capacity). Development of complex and elaborate forecasting methods such as artificial neural networks (ANN) can be costly to develop and implement, especially with the limited resources available to greenhouses. This study proposes simplified forecasting methods that would be used in conjunction with a more complex base model architecture. These simplified methods use one crop water usage as an indicator of another’s, and is titled crop-to-crop forecasting (C2C). In this study, tomatoes and peppers were evaluated, and three C2C models were developed along with an ANN base model to provide a basis for evaluation. The models were created using a dataset containing hourly watering data along with climatic and temporal data for the period between June 2015 and August 2016. The three C2C architectures used were linear regression (LR), quotient method (QM), and feed-forward neural network (FFNN), compared with the (ANN) model, which is a feed-forward neural network with extra inputs (FFNN-EI). Each model was evaluated using the root mean squared error (RMSE) and the normalized root mean squared error (NRMSE). The results show that all C2C methods have higher RMSE and NRMSE than that of the base model, with an average RMSE increase of 12% for peppers and 29% for tomatoes.
Highlights
Development of water demand forecasting models can be time consuming and costly
For a water utility located in Essex County, Ontario, Canada, forecasting commercial greenhouse water demand has become a critical aspect of day-to-day operations
This is due to the fact that almost 80% of the utility water demand is attributed to commercial greenhouses
Summary
For a water utility located in Essex County, Ontario, Canada, forecasting commercial greenhouse water demand has become a critical aspect of day-to-day operations. Current methods used in this region do not utilize current forecasting techniques [1], but instead rely on the greenhouse operators themselves to submit water requirements when a facility is being constructed. These water demands received from the greenhouse operators are fixed and are estimated demands from when the greenhouse was built. Technology and growing practices are constantly changing, and water demand from when the greenhouse may have been constructed will vary greatly from current demand This lack of proper forecasting technique poses a serious challenge on the water utility distribution system. Without proper forecasting, the system has to be significantly oversized to avoid demand uncertainties
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