Virtual active power sensor for eolic self-consumption installations based on wind-related variables

Green energy production is expanding in individual and large-scale electricity grids, driven by the imperative to reduce greenhouse gas emissions. This research performs a comparative analysis of several linear and non-linear regression models, intending to identify the most effective method to estimate the active power produced for a mini wind turbine using meteorological variables, looking for a reliable virtual sensor. The modeling process followed a feature selection step before applying eight machine learning techniques whose results were statistically analysed to determine the best performance. The implemented virtual sensor accurately estimated the active power, being an interesting tool for anomaly detection, maintenance management or decision-making.