In this paper, we present a comparative study of five methodsfor the estimation of missing values in building sensordata. The methods that were implemented and evaluatedinclude linear regression, weighted K-nearest neighbors(kNN), support vector machines (SVM), mean imputationand replacing missing entries with zero. Usingdata collected from an actual office building, the methodswere evaluated using varying parameter settings. Correlationbased feature selection is used to evaluate how usingdifferent subsets of attributes may affect each method’sperformance. We also evaluate the effect of includinglagged variables as predictors. To test the robustness ofeach method, the amount of missing values were variedbetween 5% and 20%.