WFEC: Wind farms economic classifier using big data analytics

Abstract

Wind energy projects have recently been associated with huge investments. This led researchers to dig more into managing the costs of wind farms. The operation and maintenance (O&M) costs have a big effect on the success of wind farm projects. Thus, monitoring wind turbines and predicting the O&M costs become of a crucial demand. O&M costs are related to some main parts in wind turbines, like the spare parts and rotor blades that are exposed to damage because of the surrounding environmental factors. Hence, they can be monitored via deployed sensors that generate massive quantities of incomplete, and heterogeneous data. Therefore, a big data analytical system is required to analyze these data. In this paper, we propose the Wind Farms Economic Classifier (WFEC) System that uses big data analytics to manage the data volume, variety and veracity to predict the O&M costs. WFEC proposes an enhanced Flexible Naïve Bayes Classifier (FNBC) to classify wind farms profitability according to the predicted O&M costs. Experiments show that WFEC achieves high classification accuracy with less processing time.