Prediction uncertainty of greenhouse electrical power and gas demand: Part 1, the role of parameter uncertainty

Within the modern greenhouse horticultural sector energy usage is planned using mathematical models that simulate the greenhouse's future performance. These models contain parameters whose values can be inaccurate which create errors in model predictions. This reduces the effectiveness of energy management and planning done using these models. This study proposes and demonstrates an algorithm to quantify the impact of parameter errors on greenhouse gas and electric power prediction uncertainty. The proposed algorithm introduces a Polynomial Chaos Expansion as a method for the sensitivity analysis in the domain of greenhouse horticulture. Contrary to commonly used sensitivity analyses, this approach introduces the analysis of higher order interactions into the domain of greenhouse horticultural research. It was found that for both electric power and gas production the HPS lamp power rating was the most influential individual parameter. Moreover, this study found that for power demand the uncertainty in parameters relating to the lamp system were far more impactful than those related to the crop or greenhouse structure, with a respective coefficient of variation of 24 %, 5 % and 5 %. This study makes a notable and novel conclusion that for parameters related to the greenhouse structure, larger groups of parameters were responsible for prediction uncertainty through higher order interactions of second to sixth order. These results reinforce the importance of future greenhouse research considering the impact of higher order parameter interactions on prediction uncertainty using the algorithm proposed in this study.