ABSTRACT
Data preprocessing is the first step before data-driven model development. Outlier detection is one of the preprocessing activities. Most of the time, detected outliers can’t be excluded from the dataset; therefore, outlier correction becomes essential and has gained immense interest among data-related researchers. This chapter aims to highlight lucrative outlier detection and correction algorithms suitable for power and energy applications. The requirement for outlier detection and correction is highlighted, and well-established methods are introduced. Further, each method’s merits and demerits are elaborated. The lack of suitable metrics to judge a particular preprocessing method as a better method has led the authors to propose a new set of reliable metrics, which adds one more dimension to inculcate interest in novice researchers in data preprocessing. A critical analysis of well-established methods, metrics, and their applications is expected to benefit researchers in selecting apt algorithms based on the applications in the power and energy sectors.
