Modelling the Inventory of Hydropower Plants

Abstract

Life cycle inventories are data intensive by definition and missing data continues to hinder more complete and accurate assessments. This article proposes a statistical approach to address data gaps in life cycle inventories applied to large scale hydroelectric power. The procedure relies on relationships between the technical characteristics of hydropower plants and the material and energy flows necessary throughout the life cycle of such systems. With highly flexible estimators known as kriging, predicting the value of material and energy flows suddenly becomes more accurate. From relatively small sample sizes, kriging allows better estimation without averaging out any of the original data. Similarly, parameter estimation and model validation can be performed through cross validation which assumes very little on the data itself. Mean absolute errors for various forms of kriging and regression show that the former performs better than the latter, more so in cases of incomplete data.