Prediction uncertainty of greenhouse electrical power and gas demand: Part 2, the role of parametric and weather forecast error

Abstract

This study breaks new ground by assessing the influence of individual and collective errors in weather forecast variables and errors in model parameters on the prediction error of greenhouse power and gas demand. To achieve this a sample-based and a Polynomial Chaos based sensitivity analysis using higher order sensitivity indices is proposed. This is accompanied by a sensitivity analysis of the impact of reducing individual weather forecast errors on greenhouse energy demand prediction error. The findings of this study show that weather forecast errors have a far greater role in creating mean gas (27Wm−2) and power (24Wm−2) prediction uncertainty than parametric errors (5.7Wm−2 and 4.6Wm−2). In addition, and crucially, weather forecast and parameter errors were found to be independent factors. Reducing weather forecast error exhibited large diminishing returns with the reduction in prediction error. For instance, a scenario where the forecast error of all variables is reduced by 80–90 % resulted in only a 50 % decrease in gas and electrical power prediction error. The radiation forecast errors emerged as the primary contributor to power demand prediction errors, exhibiting the potential to reduce the power demand prediction error by approximately 60 %. Reductions of forecast errors in wind and outdoor air temperature were identified as the predominant contributors, offering a respective potential for a 17 % reduction in gas demand prediction error.