The continuous rising trend shown by greenhouse emissions has led to a global situation in which the promotion of clean alternative technologies is crucial. In this context, small green power self-consumption installations represent an effective and clean solution to reduce climate change. However, they must be subjected to exhaustive supervision of the process, from mechanical, electrical, or electronic components, to ensure good performance and economic feasibility. This work proposes different data imputation techniques to deal with missing data derived from sensor missreadings in a minieolic installation. The performance of regression techniques over each reconstructed set is evaluated with successful results.