Abstract
In this paper, a brief survey of data preprocessing methods is presented. Specifically, the data preprocessing methods used in the smart grid (SG) domain are surveyed. Also, with the advent of SG, data collection on a large scale became possible. The data is essential for electricity demand, generation and price forecasting, which plays an important role in making energy efficient decisions, and long and short term predictions regarding energy generation, consumption and storage. However, the forecasting accuracy decreases when data is used in raw form. Hence, data preprocessing is considered essential. This paper provides an overview of the data preprocessing methods and a detailed discussion of the methods used in the existing literature. A comparison of the methods is also given. A survey of closely related survey papers is also presented and the papers are compared based on their contributions. Moreover, based on the discussion of the data preprocessing methods, a narrative is built with a critical analysis. Finally, future research directions are discussed to guide the readers.