Journal
ELECTRIC POWER SYSTEMS RESEARCH
Volume 191, Issue -, Pages -Publisher
ELSEVIER SCIENCE SA
DOI: 10.1016/j.epsr.2020.106885
Keywords
False outlier; Outlier detection and correction; Preprocessing; True outlier; Volatile data
Categories
Ask authors/readers for more resources
This paper examines and categorizes preprocessing methods for time-series data, evaluating and comparing the capabilities of each method. The application of these methods to commonly used time-series data in power systems is discussed, along with the impact on method performance and potential for improvement.
Outlier detection and correction of time-series referred to as preprocessing, play a vital role in forecasting in power systems. Rigorous research on this topic has been made in the past few decades and is still ongoing. In this paper, a detailed survey of different preprocessing methods is made, and the existing preprocessing methods are categorized. Also, the preprocessing capability of each method is highlighted. The well-established methods of each category applicable to univariate data are critically analyzed and compared based on their preprocessing ability. The result analysis includes applying the well-established methods to volatile time-series frequently used in power system applications. PV generation, load power, and ambient temperature time-series (clean and raw) of different time-step collected from various places/weather zones are considered for index-based and graphical-based comparison among the well-established methods. The impact of change in the crucial parameter(s) values and time-resolution of the data on the methods' performance is also elucidated in this paper. The pros and cons of methods are discussed along with the scope for improvisation.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available