Weather forecast error modelling and performance analysis of automatic greenhouse climate control

Abstract

In the published simulation studies on greenhouse climate control that employ optimal control, often non-realistic weather forecasts are employed, e.g. the realisation of the weather or artificially created forecasts are used. This research aims to quantify the effect of weather forecast errors on the performance of the controlled greenhouse system measured in terms of operational return. The operational return is defined as the difference between the cost of resources (resourceuse×cost) and the income through yield (yield×productprice). A stochastic model of the weather forecast error was identified based on historical weather observations and forecasts from a weather forecasting service. An uncertainty analysis using the stochastic model showed that a considerable number of control inputs are sensitive to the forecast errors. A simulation study involving three 7day-intervals throughout the growing season showed, however, that the performance of the controlled greenhouse system is not significantly affected by the forecast error, a performance decrease of 0.03euro.m−2 (2%) was observed with respect to the case in which perfect forecasts were used. The results suggest that an optimal control algorithm which (a) is updated every 15min with the full state information, (b) uses forecasts published every 6h and (c) uses published forecasts with a weather forecast error similar to the weather forecasting service used here, is able to mitigate the effect of the weather forecast error on the performance of the greenhouse system.