  This paper presents a novel data-driven approach for predicting the number of
vegetation-related outages that occur in power distribution systems on a
monthly basis. In order to develop an approach that is able to successfully
fulfill this objective, there are two main challenges that ought to be
addressed. The first challenge is to define the extent of the target area. An
unsupervised machine learning approach is proposed to overcome this difficulty.
The second challenge is to correctly identify the main causes of
vegetation-related outages and to thoroughly investigate their nature. In this
paper, these outages are categorized into two main groups: growth-related and
weather-related outages, and two types of models, namely time series and
non-linear machine learning regression models are proposed to conduct the
prediction tasks, respectively. Moreover, various features that can explain the
variability in vegetation-related outages are engineered and employed. Actual
outage data, obtained from a major utility in the U.S., in addition to
different types of weather and geographical data are utilized to build the
proposed approach. Finally, by utilizing various time series models and machine
learning methods, a comprehensive case study is carried out to demonstrate how
the proposed approach can be used to successfully predict the number of
vegetation-related outages and to help decision-makers to detect vulnerable
zones in their systems.
