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A Data-Driven Approach for PredictingVegetation-Related Outages in Power Distribution

SystemsMilad Doostan∗, Reza Sohrabi†, and Badrul Chowdhury∗

∗Electrical and Computer Engineering Department, University of North Carolina at Charlotte, NC, USA†Stitch Fix Inc., San Francisco, CA

Abstract—This paper presents a novel data-driven approachfor predicting the number of vegetation-related outages thatoccur in power distribution systems on a monthly basis. Inorder to develop an approach that is able to successfully fulfillthis objective, there are two main challenges that ought to beaddressed. The first challenge is to define the extent of thetarget area. An unsupervised machine learning approach isproposed to overcome this difficulty. The second challenge is tocorrectly identify the main causes of vegetation-related outagesand to thoroughly investigate their nature. In this paper, theseoutages are categorized into two main groups: growth-related andweather-related outages, and two types of models, namely timeseries and non-linear machine learning regression models areproposed 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 differenttypes of weather and geographical data are utilized to buildthe proposed approach. Finally, by utilizing various time seriesmodels and machine learning methods, a comprehensive casestudy is carried out to demonstrate how the proposed approachcan be used to successfully predict the number of vegetation-related outages and to help decision-makers to detect vulnerablezones in their systems.

Keywords—Data analytics, machine learning, outage prediction,power distribution system, time series analysis, vegetation-relatedoutage

I. INTRODUCTION

A. MotivationMany power utility companies recognize vegetation as the

primary cause of outages in their power distribution systems.As a matter of fact, sustained or momentary outages caused byvegetation present serious challenges to maintaining adequatepower quality and pose substantial risks to the reliability ofthe system [1]. In addition, vegetation growing near powerlines can cause wildfires, creating major hazards to humanand wildlife resources. In particular, over recent years, withmore frequent extreme weather conditions and adverse naturalphenomena caused by climatological factors, the risk of de-structive interaction between vegetation and power lines hasincreased, making this source of outage a growing concern forpower utilities [2], [3], [4].

In order to mitigate the damaging effects of vegetationon power systems, utilities are primarily interested in taking

preventive measures. This includes designing more resilientdistribution systems, conducting more frequent inspections,and scheduling regular tree-trimming operations [5]. Althoughtaking these pro-active measures may reduce the potentialrisk of vegetation-related interactions with power systems,it is often impossible to eliminate vegetation-related outagesunder adverse weather conditions. Moreover, depending onthe species, vegetation can sprout and grow very quickly,diminishing the impacts of trimming operations. As a result, inaddition to implementing preventive measures, it is importantfor utilities to take appropriate responses in dealing with theseoutages, either by identifying them immediately after theyoccur or by predicting them in advance.

Electric utilities have shown significant interest invegetation-related outage, and in general, outage cause identi-fication, especially in the smart grid era. In fact, identifying theroot cause of outages soon after they occur will enable utilitiesto accelerate the restoration process, ultimately improving thereliability of their systems [6]. This problem - specifically forvegetation-related outages - has received a great deal of atten-tion, leading to the development of various models [6], [7], [8],[9]. On the other hand, outage prediction has been historicallyneglected. Although predicting outages, particularly of thevegetation-related category in advance, will bring enormousbenefits to power companies, developing mathematical modelsfor fulfilling this task is extremely challenging. This practicaldifficulty lies in the complex nature of vegetation-relatedoutages and the fact that a substantial number of factors havea strong influence on the occurrence of these outages.

Nevertheless, in recent years, with the explosion of datagathering efforts within the smart grid framework and massiveimprovements in weather forecasting models, necessary datafor studying vegetation-related outages has become available.Furthermore, mathematical methodologies are now being com-bined with advanced data analytics techniques, creating pow-erful tools that can improve utilities’ predictive abilities on theaforementioned outages. Adopting these predictive approacheswill enable effective and timely decision-making actions byoperators as well as planners, ultimately improving operationalintegrity and resiliency [10].

B. Literature ReviewSeveral studies have aimed at exploring the underlying

causes of vegetation-related outages that occur in power distri-

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bution systems and developing analytical approaches to predictsome characteristics of these outages.

The authors in [11] propose an approach for predictingthe rate (number of outages per mile-year) of vegetation-related outages that are caused due to vegetation growth onan annual basis. They develop and evaluate four differentmodels, vis-a-vis, linear regression, exponential regression,linear multivariate regression, and artificial neural network.The main inputs to their models are historical outage data andclimatic variables that affect the vegetation growth.

Another study [12] proposes a statistical approach to predictvegetation-related outages under normal (non-storm) operatingconditions. In particular, the authors carry out an investigationon the impact of tree trimming on the frequency of vegetation-related outages. They utilize historical outage, geographical,and tree-trimming data in their approach. The data is fedinto three statistical models, namely Poisson generalized linearmodel, negative binomial generalized linear model, and Pois-son generalized linear mixed model, and the performance ofeach model is evaluated.

In [13] the authors present a study to evaluate the impact ofvarious factors on predicting vegetation-related outages underhurricane conditions. In particular, they use LiDAR data toderive variables that can model the height and location oftrees in the system under study. By utilizing the aforemen-tioned data along with vegetation management and systeminfrastructure information, they develop an ensemble machinelearning algorithm to predict whether or not a specific area willexperience vegetation-related outages under an extraordinaryweather condition.

In [14], the authors develop a data-driven approach to con-duct a comprehensive root cause analysis of outages that occurin distribution systems. Their primary goals are to characterizeoutages according to their underlying causes and to identifyimportant variables that strongly impact the outage frequency.By applying the proposed approach on vegetation-related out-ages, they demonstrate the importance of climatological andgeographical factors for predicting these outages.

Despite the fact that the aforementioned studies attemptto predict vegetation-induced outage rate or its characteristics(albeit some of them take advantage of advanced data gatheringtools and machine learning methods), an approach that has thepotential for implementation to predict the anticipated numberof these outages on a monthly basis for a specific location,has not yet been developed. In fact, the existing approachesare either not designed for this purpose, or have practical short-comings. These shortcomings include delivering a low degreeof accuracy when the number of outages is large, and lackingthe ability to make the prediction for a specific location in thesystem within a short-term horizon. Moreover, existing modelsmerely focus on one cause of vegetation-related outages andtherefore, fail to provide a comprehensive approach that takesvarious sources of such outages into consideration. It is evidentthat the main reason for the perceived lack of studies by theresearch community on this critical problem is the limitedaccess to a sufficient amount of outage data. In fact, a majorityof utility companies do not publish their outage data in greatdetail, and therefore this problem has not been explored to the

fullest extent yet.

C. Contributions

In this paper, a novel approach to predict the number ofvegetation-related outages in power distribution systems on amonthly basis is proposed. Utilizing statistical and machinelearning predictive models, the proposed approach is an intel-ligent solution that combines weather and geographical datawith past vegetation-related outage information and makes aprediction for the anticipated number of future outages.

The proposed approach provides a meaningful knowledgeabout risks and locations of vegetation-related outage prob-lems. It presents a succinct view of the current system statusto the operators, which enables effective and timely decision-making actions with regards to vegetation-related problems. Byproviding a preliminary but accurate prediction, the proposedapproach allows operators to take high-resolution imagery ofareas with high risk of an outage, or utilize LiDAR data, ordispatch a crew to find the exact locations in the system wherea vegetation-related outage could occur.

What distinguishes this approach from the other studiesin the literature may be summarized in the following fourstatements:• Our proposed approach takes different characteristics of

vegetation-related outages into consideration, and suc-cessfully categorizes them according to their underlyingcauses. Moreover, the approach provides a specializedpredictive model for each category.

• Our approach offers a workable solution to address thechallenges brought about by the extent of the predic-tion’s target area.

• Our approach takes advantage of a considerable amountof vegetation-related outage data that is provided by amajor utility company.

• All the advantages of the proposed approach are builtupon generic outage data collected by utilities (if avail-able), and typical daily weather forecast data, which ispublicly available. This fact makes the implementationof the approach easily attainable within a reasonablelevel of accuracy.

• Several considerations taken in the formulation of theproposed approach enable its deployment to be highlyflexible to a variety of different settings and objectivessuch as prediction horizon.

D. Paper Organization

The rest of this paper is organized as follows. In Section II,a detailed problem description is provided where the objectivesand approach are explained in details. In Section III, thedata is described and a discussion of the data pre-processingprocedure is provided. In Section IV, the proposed data-drivenmethodology is explained. In Section V, a comprehensivecase study is carried out, and the results are presented anddiscussed. Finally, in Section VI, conclusions, as well asrecommendations, are provided.

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II. PROBLEM DESCRIPTION

In this study, a data-driven approach is proposed to predictthe anticipated number of sustained vegetation-related outagesfor a particular month (preferably the month ahead) in a givenarea within a power distribution system. In order to develop apractical approach for this purpose, two major challenges haveto be addressed comprehensively.

The first challenge is to define the extent of the prediction’starget area. In fact, if one’s intent is to point-predict the numberof vegetation-related outages at the location of any givensubstation, one might encounter serious difficulties. Thesedifficulties lie in the fact that the degree of randomness for thenumber of outages that occur for each substation is relativelylarge. Moreover, accurate radar weather forecasts may not beavailable at the exact location of each substation as a resultof the weather station being far from the substation. Conse-quently, developing an approach that is capable of predictingthe number of vegetation-related outages at an exact givenlocation in the system is neither realistic nor practical.

To the best of our knowledge, this problem has not beeninvestigated in detail yet. As mentioned before, one reasoncould be the limited access to a sufficient amount of data.However, thanks to the considerable amount of data providedby a large power company in the southeastern US, this problemcould be addressed in this paper. In order to deal with thisissue, we propose to aggregate substations and build larger ar-eas, in which each area includes multiple substations and localweather stations. As a result of doing so, the randomness in thenumber of outages would be harnessed considerably, leading togreater predictive capability. Moreover, carrying out this taskwill produce more comprehensive weather forecast since foreach area multiple weather stations will be considered, makingsure that at least one of the stations captures the major weatherevents.

The second challenge is to thoroughly investigate the na-ture of vegetation-related outages. In fact, vegetation-relatedoutages occur due to various reasons; however, they could becategorized into two coherent groups. The first group includesoutages that are caused by vegetation that naturally grow intoand make contact with distribution lines. The second groupconsists of those ones that are caused by vegetation fallinginto or making contact with distribution lines due to weather-related factors.

Vegetation-related outages that are caused by the naturalgrowth of vegetation mainly depend on the time of the year andmanifest a strong seasonal pattern. Moreover, their occurrenceis highly affected by vegetation management operations suchas regular trimming. On the other hand, outages that arecaused by weather-related factors do not show an apparenttrend. Although time could play a role in the frequencyof such outages, there are various other climatological andgeographical factors that make strong contributions to theoccurrence of such outages.

In this paper, in order to fully consider the aforementionedcharacteristics of vegetation-related outages, we propose todevelop two different models for the prediction purposes: usinga statistical approach based on time series algorithms to predict

Fig. 1: Technical flow-chart of the proposed approach

the outages that fall into the first group and employing machinelearning based approach for the second group. This separationwill significantly increase the predictive capability.

The technical flow-chart of the proposed approach is de-picted in Fig. 1. Each block of the flow-chart will be discussedin following sections.

III. DATA DESCRIPTION AND PRE-PROCESSING

A. Data description

As mentioned, the input data for the proposed approachare obtained from three main sources: 1) historically recordedoutages, 2) geographical information, and 3) radar weatherforecasts. The power systems outage data is collected byDuke Energy - a major investor-owned utility company inthe US. The data includes information on the exact time andresponsible substations of sustained vegetation-related outagesthat occurred around approximately 85 substations located inthe states of North Carolina and South Carolina between theyears 2011 and 2014. It is worth noting that the models for theproposed approach are developed based on the data gathered

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in years 2011 to 2013. The year 2014 will be used as acase study to show the performance of the proposed approach.The geographical data is also provided by Duke Energy, andcontains information about the exact location of differentsubstations. The hourly radar weather data are collected fromseveral external sources for all substations over the span ofthe aforementioned years. The data will be explained in moredetails in the upcoming sections.

B. Data pre-processing

Real-world outage, weather, and geographical datasets usu-ally contain various input errors, duplicate data, extreme andunexpected values, missing values, and outliers. These typesof data can result in misleading representations and interpre-tations of the collected data and may skew the operation ofdata-driven models. Therefore, the raw data should be exploredand appropriate actions ought to be taken in order to avoidpotential problems. In this study, after the data is cleansed, twomajor data pre-processing tasks, i.e. handling missing valuesand outliers are performed and discussed below. Providing thisdiscussion is important as the data pre-processing task couldhave a considerable impact on the performance of models thatwill be developed later on.

1) Dealing with Missing Values: There is a relatively smallnumber of missing values in the weather dataset. For example,on some occasions, the information about gust intensity - oneof the main inputs to the proposed regression model, for aspecific area at a particular time-stamp is missing. In orderto properly handle this issue, the missing data is filled in bydrawing values from non-missing values utilizing a linear in-terpolation technique. The reason behind selecting this methodis that the nature of the variables that contain missing value issuch that they can reasonably be expected to change linearlyover short time intervals. Therefore, interpolation should be arational approach.

2) Dealing with Outliers: Outliers, being the extreme valuesthat deviate markedly from the other observations, could bepresent in real-world datasets. As a matter of fact, the outagedatasets used in this work includes a small number of samplesthat show an unexpectedly large number of outages recordedfor a specific area at a particular time-stamp. Moreover,the weather dataset contains some disproportionately extremevalues with regard to weather-related factors.

To deal with the existing outliers in both outage and weatherdatasets, we first detect the outliers by using the Inter-QuartileRange measure, and then remove them from the dataset. Itis worth mentioning that there are various other strategies todetect and accordingly handle the outliers [15], [16]. Selectingthe most effective strategy is not a straightforward task, anddepends on the situation and the dataset. In this study, webelieve that the presence of the outliers is mainly due toincorrectly entered or measured data. Moreover, we performeddifferent analyses and concluded that the selected approachis the most appropriate strategy for this study. In particular,taking the aforementioned action does not make any significantimpact on the distribution of the variables.

IV. PROPOSED DATA-DRIVEN METHODOLOGY

A. Aggregating substations and creating different areasAs mentioned, one of the main challenges with developing

the proposed approach is defining the extent of predictiontarget area. To deal with this issue, we propose to aggregatesubstations and to build larger areas, in which, each areaincludes multiple substations and local weather stations, wherethe weather forecast for each substation is obtained from itsclosest weather station. In order to define the aforementionedareas, k-means clustering algorithm is utilized. This algorithmis a widely used unsupervised machine learning algorithm,which aims at categorizing a given dataset into a certainnumber (k) of clusters. In this paper, this algorithm is usedto group substations into different clusters, where each clusterrepresents an area. The main idea of this algorithm is todefine k centroids at random, one for each cluster, and then tominimize the squared error function represented in (1) [17].

J(r, µ) :=1

2

m∑i=1

k∑j=1

rij ‖xi − µj‖2 (1)

where m is the number data points, k is the number of clusters,rij is an indicator, which is 1 if, and only if, xi is assigned tocluster j, xi is data point, µj is the centroid for cluster j, and‖.‖2 denotes the Euclidean distance. In this study, data pointsare locations of substations (approximately 85 data points),which are represented by latitude and longitude in a two-dimensional space.

One major challenge with this algorithm is to specify thenumber of clusters. In fact, there is no global theoreticalmethod to find the optimal value of this parameter; however, afew approaches are common among data scientists for dealingwith this problem. One workable approach is to run k-meansclustering for a range of different k values and to calculatethe aforementioned squared error function for each value.In this case, the error tends to decrease toward zero as kincreases; however, after a certain k value, the decrease wouldbe very gradual. Therefore, analyzing different values of k andfinding the aforementioned threshold could help on deciding areasonable number of clusters [18].

Applying k-means clustering algorithm and using the afore-mentioned method to calculate an appropriate number ofclusters for grouping substations will result in 14 areas for our

Fig. 2: Demonstration of different areas

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study. In order to solve the k-means clustering problem, theLloyds algorithm is utilized in this paper. Fig. 2, illustratesthese geographically dispersed areas. It is worth mentioningthat although there are other clustering approaches, we believethat the approach adopted here is the most suitable for thisstudy. In fact, since the goal is to cluster a two-dimensionaldata (i.e., latitude and longitude) and to work with distances,the k-means approach makes the most sense. Moreover, con-sidering the size of clustering data which is small, there wouldbe no need for utilizing more sophisticated algorithms.

B. Proposed model for predicting growth-related vegetationoutages

As discussed earlier, a category of vegetation-related outagesis comprised of those events that are caused by vegetationnaturally growing into, and making contact with distributionlines. This type of outage will be called growth-related veg-etation outage from this point. In order to identify this typeof outage, we analyzed weather and outage datasets carefully,and selected those outages for which no weather-event wasrecorded by any weather station within the stipulated area fora few hours prior to the occurrence of the outages. The factthat no weather-related event was recorded for those outagesensures that such outages have occurred due to natural reasons.It is also worth noting that gaining access to a sufficient amountof vegetation data could further help in identifying this typeof vegetation-related outages.

The main objective of this part is to propose a type ofmodel that is able to successfully predict the growth-relatedvegetation outages for a specific area in the system duringa specific month. With this context, the target for predictionis defined as Growth-related Vegetation Outage Count Index(GVOCI), which for area a and month m can be formulatedas in (2).

GV OCIam =∑s∈a

∑d∈m

H∑h=1

GV OCsdh (2)

where s, d, and h represent the substation, day in month, andhour in the day, respectively. The GV OC is hourly growth-related outage count and belongs to GV OC ∈ {0, 1, 2, 3, ..}.These parameters will allow the utility to have the optionto predict the number of outages that occur during specifichours in the day, specific days in the month, and for specificsubstations. In order to predict all potential outages, thesevalues should encompass all substations in the target area, 28-31 days (depending on the month) and 24 hours. Fig. 3, topplot, demonstrates the GV OCIam aggregated over all areasfor all months starting from 2011 to the beginning of 2014represented in a series.

To clearly describe the patterns in growth-related outage se-ries, the data can be decomposed into three main components,namely trend, seasonality, and residual. These componentsexplain the large-scale increase or decrease, the variations thatperiodically repeat, and the variations that is random in theseries, respectively. Fig. 3 illustrates these components.

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Fig. 3: Decomposition of growth-related vegetation outages

As observed in Fig. 3, the yearly trend shows that the num-ber of outages has decreased. We believe that this phenomenonis mainly due to performing regular tree-trimming operations.It should be noted that tree-trimming operations will resultin a smaller number of vegetation-related outages that arecaused by natural reasons. The model that we will employfor predicting this type of vegetation-related outages is ableto capture and adopt this pattern for predicting the number offuture outages. Also, it should be noted that depending on thespecies, vegetation can sprout and grow quickly, diminishingthe impacts of trimming operations. Moreover, based on thefigure, it can be observed that, on average, the number ofsuch outages in the summer months, especially in the monthsof June and July, is much higher than during other months.By applying further statistical analyses, it is understood thateven though the number of outages occurring in each monthis considerably different, their variance is relatively small,suggesting that there is a strong seasonal effect.

Since such outages show obvious trend and seasonal struc-tures over time and it seems highly unlikely that differentoutside variables insert influence on these outages, we proposeto utilize a time series analysis to identify their nature and topredict their future values. In fact, time series analysis is awell-established statistical analysis and due to the aforemen-tioned reasons, appears to be the most sensible approach.

There are various models to analyze time series; however,the selection of the most appropriate model mainly depends onthe type of data and application. In the case study section thatfollows, two well-established time series models are exploredand utilized to demonstrate how growth-related vegetationoutages can be predicted.

C. Proposed model for predicting weather-related vegetationoutages

1) Proposed type of the model: Another type of vegetation-related outages is that results from vegetation contactingor damaging power distribution system infrastructure due to

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severe weather-related factors. For simplicity, these types ofoutages will be called weather-related vegetation outages fromthis point. In order to identify these outages, we exploredweather and outage datasets, and selected those outages forwhich a major weather-event was recorded by any weatherstations within the stipulated area during the time of theiroccurrence, and a few hours prior to that. The reason forconsidering a margin of few hours is to account for potentialerrors in weather forecasts.

As opposed to the growth-related vegetation outages,weather-related vegetation outages do not show a clear trendover time. Although, as will be shown, there could be acorrelation between time-related factors and the frequencyof such outages, the occurrence of these outages is moredependent on climatological and geographical factors. As aresult, the problem of predicting the future values of suchoutages cannot be simply defined as a time series problem.

Therefore, to successfully carry out the prediction task forweather-related vegetation outages, we propose to define theproblem as a supervised machine learning problem and todevelop regression models that can handle the non-linearityin the data. One crucial point to argue with regards to the typeof regression models is that linear models do not deliver asatisfactory performance for this problem. As a matter of fact,linear regression models assume a linear relationship betweenthe input variables and the output variable. More specifically,such models assume that the output can be calculated from alinear combination of the input variables. However, we haveconducted various statistical analyses (i.e., fitting linear modelsand analyzing residuals and checking linear model assump-tions) and concluded that linear models are not suitable for thisproblem. Therefore, we propose to utilize non-linear regressionmodels. We will demonstrate how well-established non-linearmodels perform through a comprehensive case study.

In order to develop highly capable data-driven models forpredicting weather-related vegetation outages, an essential stepis to gather as much information as possible about the factorsthat influence the occurrence of these outages. These factorscan be categorized into three main groups of climatological,geographical, and time-related variables. By utilizing thesevariables, different aspects of aforementioned outages includ-ing the severity of weather events, the characteristics of thedistribution system infrastructure, the vegetation density, andthe potential correlation between time and extreme weatherconditions can be explained.

After the necessary data is collected, a comprehensive pro-cess needs to be carried out to transform the raw data into thefeatures that accurately describe the inherent structures withinthe data. This process, which is known as feature engineering,will result in significant improvement in the accuracy ofthe model particularly when the scale and size of data arenot considerably large. In what follows, the data that wascollected is explained, the target variable is defined, the featureengineering task is performed, resultant features are explained,and their importance is evaluated.

2) Defining target variable and conducting feature engineer-ing: As explained, the main objective in this part is to predictthe number of weather-related vegetation outages that occur in

a specific area within the system on a particular month. Similarto GV OCIam, an index can be created for weather-relatedvegetation outages to represent the target variable. This index isdefined as the Weather-related Vegetation Outage Count Index(WVOCI). The mathematical formulation of the WVOCIfor area a and month m is expressed in (3).

WVOCIam =∑s∈a

∑d∈m

H∑h=1

WVOCsdh (3)

Where s, d, and h represent the substation, day inmonth, and hour in the day, respectively. The WVOC ishourly weather-related vegetation outage count and belongsto WVOC ∈ {0, 1, 2, 3, ..}.

In order to successfully predict the WVOCIam, varioustypes of information, namely weather, geographical, and time-related factors are gathered and processed. With regards to theweather-related factors, as frequently reported, the occurrenceof weather-related vegetation outages is highly influencedby the frequency of gust (extremely windy) conditions, theintensity of the gust events (mile/hour), and the occurrenceof heavy precipitation and thunderstorm conditions. Therefore,in order to include these factors into the model and representthem, necessary hourly weather data are collected and threeindices of Gust Count Index (GCI), Gust Intensity Index(GII), and Storm Count Index (SCI) are created. The math-ematical formulation of these indices for area a and month mis presented in (4) to (6).

GCIam =1

Sa

∑s∈a

∑d∈m

H∑h=1

GCsdh (4)

GIIam =1

Sa

1

Dm

1

H

∑s∈a

∑d∈m

H∑h=1

GIsdh (5)

SCIam =1

Sa

∑s∈a

∑d∈m

H∑h=1

SCsdh (6)

where GC, GI , and SC demonstrate the hourly number ofgust events, gust speed, and number of storm events and satisfythe following conditions: GC,SC ∈ {0, 1}, and GI > 8mph.Moreover, Sa represents the number of substations in the area,Dm shows the number of days that are investigated in thetarget month, and H demonstrates the number of hours that areconsidered for each day. With regards to the aforementionedindices, certain factors need further clarification:• The windy condition is considered as gust when the

speed exceeds the value of 8 mph. This threshold isselected based on [19], where the authors discuss theimpact of different wind speed values on vegetation. It isnecessary to mention that the value selected in this paperis slightly smaller than the minimum value presentedin [19] to make sure that no gust event is missed. Ifthe weather forecast shows any gust event, the GC willbecome one; otherwise, zero.

• In order to investigate the impact of gust intensity, differ-ent variables such as average, maximum, and minimum

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of gust speed were created. After a careful analysis, itwas observed that these variables show a considerablecorrelation with each other. Also, the average gust speedshowed a stronger relationship with the target variable.Hence, only the average variable, represented by GIIam,is utilized in this paper.

• The storm refers to both heavy precipitation conditionsand thunderstorms. Almost all publicly available weatherforecast sources report these conditions on an hourlybasis. If the weather forecast shows any of these event,the SC will become one; otherwise, zero.

• Since obtaining accurate values for the intensity ofprecipitation and thunderstorms is difficult, it is decidednot to include such information in the model; however,if accurate values were available, their inclusion may beuseful.

• For the specific distribution system under investigation,snow is not a common weather-related phenomenonbecause of its geographical location; hence, no factorsrelated to snow was considered in this study. However,in areas with heavy snowfall potential, it is highlyrecommended to include a variable to describe the countand intensity of the snow as an input.

In order to demonstrate the relationship between the pro-posed variables and the WVOCIam, these variables are evenlycategorized into eight different categories, and the relativefrequency of the WVOCIam is calculated for each categoryand plotted. The selection of the number of categories dependson the analyst’s preference. Fig. 4 illustrates such relativefrequency for these variables. According to the figure, it can berealized that as the value for the proposed variables increases,the WVOCIam increases. This observation is not surprisingbecause it is expected as the number and intensity of gust andstorm events increases, more vegetation-related outages occur.However, this observation confirms that the proposed variablescan successfully explain the variability of the target value andcapture the statistics of the data.

The second types of variables that have significant impactson weather-related vegetation outages are geographical vari-ables. In fact, these variables can describe the infrastructureof the distribution system under investigation, how prone thesystem is to the outage, how much interaction the system haswith the vegetation, etc. These factors differ from area to

1 2 3 4 5 6 7 8Category Number

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CIam

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ativ

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Fig. 4: Weather-related factor’s impact

area. A major challenge with these factors is that obtaininginformation about them could be extremely difficult. As amatter of fact, the data that contain such information typicallyis either not available or is kept confidential. Hence, toaddress this problem, and to consider the effect of geographicalfactors, we propose to create a variable based on historicaloutages and weather-related factors to explain the geographicalinformation. The proposed variable is defined as Area OutageIndex (AOI) which, for area a, is formulated as in (7).

AOIa =

∑Yy=1

∑Mm=1WVOCIyam∑Y

y=1

∑Mm=1(GCIyam + SCIyam)

(7)

where Y represents the number of years for which outage andweather data are investigated (i.e. 3 in this study). The AOIshows the ratio of the number of all weather-related vegetationoutages that occurred in an area over the number of extremeweather conditions that area experienced.

Fig. 5 is provided to better illustrate the AOI variable.As seen in the figure, bottom plot, during years 2011 to2013, each area experienced a different number of gust andstorm conditions. It turned out that the average gust intensityfor all areas is almost the same. In the top plot of Fig. 5,the green bars show the number of outages that each areaexperienced during the same time span. It can be understoodthat although some areas experienced a smaller number ofweather-related events, the occurrence of outages was morefrequent for them. Therefore, the geographical characteristicsof these areas are such that they are more prone to outage.The gray bar plots representing the AOI variable clearlydemonstrate this effect. Areas with large AOI value, have highpotential for weather-related vegetation outages. Besides beingof use as an input, this variable can inform utility asset ownersabout the vulnerable zones in their systems. It is worth notingthat in case that any geographical information is available, itis highly recommended to include it into the model; however,we believe that the proposed variable can successfully capturethe geographical statistics in the data.

Finally, as mentioned before, there is a correlation betweentime-related factors and the WVOCIam. In fact, since the timeof the season has a significant effect on vegetation-growth and

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Fig. 5: AOIa representation

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density, it can affect the amount of influence weather-relatedfactors can have on the vegetation. In order to capture thiscorrelation, a variable for each month is created. This variableis called MOI which, for month m, is formulated as in (8).

MOIm =

∑Yy=1

∑Aa=1WVOCIyam∑Y

y=1

∑Aa=1(GCIyam + SCIyam)

(8)

This variable shows the ratio of the number of all weather-related vegetation outages that occurred in each month overthe number of extreme weather conditions that month expe-rienced over the span of all available years. Fig. 6 showsthe relationship between month, weather-related factors, andoutage. As seen in the figure, bottom plot, winter months ex-perienced a larger number of weather-related events; however,the occurrence of weather-related vegetation outages (top plot,pink bars) is not as frequent as in the summer months. Thiscan be explained by the lower density of vegetation duringthese months. In fact, the MOI variable, which is shown inthe top plot with purple bars, demonstrates that the summermonths, especially June, are more critical since vegetationdensity reaches its maximum and therefore weather-relatedfactors are more influential. It is worth mentioning here thatthe data suggest the average gust intensity is almost the samein different months; hence, this variable is not included in theformulation of MOI .

3) Conducting feature importance analysis: After the neces-sary features are created and explained, it is important to assesstheir significance in explaining the variability of the vegetation-related outages caused by weather-related factors. As demon-strated, such variability could be explained by various features,in which some take on a high importance, and some may beless significant. Conducting the feature importance analysis isparticularly vital when the number of features is considerablylarge and obtaining some of them is difficult. In order to findthe importance of each feature, the problem is formulated asan all-relevant feature selection problem in this paper.

Since the number of features used in this study is relativelysmall, and these features are not sparse, a random forest-based algorithm, introduced in [20], is used to perform the all-relevant feature selection analysis. To find the relevant features

1 2 3 4 5 6 7 8 9 10 11 120

100

200

300

400

500

Out

age

freq.

TotalMOIm

1 2 3 4 5 6 7 8 9 10 11 12Month

0

100

200

300

400

Eve

nt fr

eq.

GCImSCImGIIm

0.0

0.3

0.6

0.9

1.2

1.5

0

10

20

30

Fig. 6: MOIm representation

0.0 0.2 0.4 0.6 0.8 1.0Normalized Importance

AOIa

MOIm

GIIam

SCIam

GCIam

Feat

ures

Fig. 7: Feature importance analysis

and their importance, this algorithm calculates the sensitivityof the estimation model to random permutations of featurevalues. The rationale behind the permutation is that alteringvalues for features that are not useful for estimation does notlead to a significant reduction in the model’s performance.However, important features are usually more sensitive to thepermutation, and will therefore gain more importance [21].

By implementing the random forest algorithm, all featuresdiscussed in this paper are confirmed to be important. Theimportance of all features is demonstrated in Fig. 7. Accordingto this figure, it can be understood that climatological factorstake higher importance compared to geographical features;however, their difference is not considerable. It is worth notingthat such conclusion could not be generalized and should beinvestigated for new datasets.

V. CASE STUDY

A. DescriptionIn order to demonstrate the effectiveness of the proposed

approach for predicting the anticipated number of vegetationoutages, a comprehensive case study is carried out in thissection. As mentioned earlier, the proposed models and fea-tures are derived from the data that was obtained during theyears 2011 to 2013. To conduct the case study, data from theyear 2014 is utilized. During 2014, the outage information isavailable only for the first seven months; hence, the case studyis carried out for these months only.

In order to implement each step of the proposed approachdifferent statistical and machine learning algorithms are uti-lized. To compare the performance of these algorithms twodifferent strategies are employed. The first strategy is to definean error metric and to analyze it. This metric is defined asNormalized Mean Absolute Error (NMAE), which for areaa, is formulated as in (9).

NMAEa =1

M

M∑m=1

∣∣∣ ˆOCIam −OCIam∣∣∣

OCIamax −OCIamin

(9)

where M is the total number of months included in the testdataset (seven in this case study), ˆOCIam is the predictedtarget variable, OCIam is the actual target variable, andOCIa

max − OCIamin represents the range of outages that

occurred for area a in the test dataset. It is necessary tomention that OCIam is a general term and can represent any

9

of GV OCIam, WVOCIam, and TOCIam, which will bediscussed later. This metric demonstrates the rate of predictionerror considering how large the number of outages is in eacharea. As a matter of fact, including the range of outages in theformula is essential as it allows to normalize the error valuefor different areas and therefore show the result without anybias, making it easy to compare and evaluate. The usage ofother metrics, which could possibly show better results, wouldbe misleading.

The other strategy to compare the performance of theadopted algorithms is to statistically and visually investigatethe predicted versus actual values of the target variable for eachmonth aggregated over all areas. It is necessary to mentionagain that the ultimate goal of the study is to predict thenumber of outages in each month for each area; however, togain a comprehensive understanding about the performance ofthe models, the aforementioned aggregated representation ishighly beneficial. In what follows, the proposed approach isimplemented, the error analysis is conducted, and the resultsare presented and discussed.

B. Predicting growth-related vegetation outagesAs mentioned earlier, in order to predict the number growth-

related vegetation outages, it is proposed to utilize time seriesanalysis. There are various models to analyze and predict timeseries data. Among them, Auto-Regressive Integrated MovingAverage (ARIMA) and Holt-Winters exponential smoothingmethods are two of the most widely known algorithms. Bothof these algorithms have different parameters that make themcapable of modeling the major aspects of a times series data.Hence, these methods are employed in this case study toconduct the prediction task.

A brief explanation of these two algorithms is providedbelow [22].

ARIMA: This model is an extension of the auto-regressivemoving average model so that non-stationary time series canalso be handled. The AR part of ARIMA refers to the fact thatthe target value is regressed on its own lagged values. The Irefers to the feature that the data values have been replacedwith the difference between their values and the previousvalues (possibly more than once). The MA part indicates thatthe regression error is actually a linear combination of errorterms in the past. Also, seasonal ARIMA (SARIMA) can takeinto account the seasonal component of the time series. Thegeneral forecast formula of a SARIMA(p, q, d)(P,Q,D)m isas (10)

yt = µ+

p∑i=1

φiyt−i+

q∑i=1

θiet−i+

P∑i=1

Φiyt−im+

Q∑i=1

Θiet−im

(10)where yt and yt are the differenced (according to the valuesof d and D) versions of the predicted target values and actualtarget values at time t, respectively. Also, et = yt − yt, andµ, φi, Φi, θi, Θi are the parameters that have to be estimatedbased on the training data.

Holt Winters Seasonal (HWS): This algorithm is based ondecomposing the uni-variate target value into three different

components and applying exponential smoothing to thesecomponents over time. These components can be combined inan additive or multiplicative way. In the additive form (usedin this paper), if the objective is to predict the value y at timet+h given the data for all the times up to and including time twhich is denoted as yt+h|t, then the following components arecalculated based on the previous values for these componentsas:

Lt = α(yt/St−m) + (1− α)(Lt−1 +Bt−1) (11)

Bt = β(Lt − Lt−1) + (1− β)(Bt−1) (12)

St = γ(yt/(Lt−1 +Bt−1)) + (1− γ)(St−m) (13)

and after that yt+h|t which is the prediction for time-step t+hcan be derived as:

yt+h|t = (Lt + hbt)St+h−m(k+1) (14)

where m is the length of the seasonal cycle, yt is the actualvalue of the target at time t, and k = b(h− 1)/mc. Also,α, β, γ (all belonging to the interval [0, 1]) are the tunableparameters of the algorithm, and there are effective methods tocalculate their optimal values based on the data in the trainingdataset.

As demonstrated, the growth-related vegetation outagesshow a strong seasonal pattern; therefore, it is necessaryto model the seasonality component. As a result, SeasonalARIMA model (SARIMA) and a Holt-Winters Seasonal(HWS) method are utilized. It is worth noting that sincethese are well-established methods whose formulation andparameters are readily available [23], the further mathematicaldetails of these models are not discussed in this study.

In order to build effective SARIMA and HWS models, it isnecessary to choose the optimal values for their parameters. Toconduct this task, after a careful data pre-processing, we createrolling based training and testing sets by utilizing the dataobtained from years 2011 to 2013. At each step, we considerdifferent sets of parameters, perform a grid search, calculatethe NMAEa on the validation set, select the combination ofparameters that deliver the lowest error, and use them to makethe prediction on the test set. At the end, the combination ofparameters that results in the lowest error value on average invalidation sets is selected as the optimal parameters. Afterward,we build the model based on the selected set of parametersand apply those to make predictions for the year 2014. Thisprocedure, which is known as k-fold cross validation, preventsover-fitting problem and helps demonstrate how the modelresults could be generalized. Fig. 8 shows this procedure.

Fig. 9 demonstrates the NMAEa of all areas for SARIMAand HWS for the year 2014. The plot includes two types ofstatistical analyses, namely box plot and confidence interval ofthe average error. The box plot demonstrates the distribution ofthe error metric for all areas. The confidence interval providesa range of values which is likely to contain the populationerror value. The confidence interval is constructed based onthe t-distribution, as well as a confidence interval of 95%.

As observed in Fig. 9, the HWS method delivers a narrowerdistribution of error values. Moreover, the average error for

10

Train Test

Train

Train

Train

Train

Test

Test

Test

Test

Training Validation

Fig. 8: k−fold cross validation procedure

0.05 0.10 0.15 0.20 0.25NMAEa

HWS

SARIMA

Alg

orith

m

Mean Confidence Interval

Fig. 9: NMAEa for time series algorithms

HWS is smaller compared to SARIMA. However, as seen, theconfidence intervals for the methods overlap, suggesting thatthere is no convincing evidence of the difference between thepopulation average error for these methods. The reason forHWS outperforming SARIMA for this case study could beexplained from Fig. 10.

Fig. 10 demonstrates the predicted versus actual targetvalues for each month aggregated over all areas. Moreover, theerror for each algorithm is shown with dotted lines. Accordingto this figure, SARIMA delivers a better performance for thewinter months; however, during summer months, especiallymonth July, its performance is poorer than HWS. The monthof July for the year 2014 is a special month as the numberof outages that occurred in this time period is considerablylower compared to previous years. This could be because oferror in recording outages. Therefore, based on the error valuesobserved from the figures, the authors believe that for this

1 2 3 4 5 6 7Month

20

0

20

40

60

80

GVO

CIm

Act

ual v

s. Pr

edict

ed SARIMAHWSActualError (SARIMA)Error (HWS)

Fig. 10: Actual vs prediction for time series algorithms

specific case study the HWS outperforms SARIMA, however,this argument cannot be generalized and should be tested fornew outage datasets.

C. Predicting weather-related vegetation outagesIn order to predict the number of weather-related vegetation

outages, as proposed, machine learning regression modelsthat can handle the non-linear interactions within the data,seem to be appropriate choices. As a result, three well-known models, namely Random Forest (RF), Support VectorMachine (SVM), and Neural Network (NN) were selected tocarry out the prediction task in this case study. It is worthmentioning that several other machine learning methods suchas KNN and Naive Bayes were also investigated; however,the aforementioned models (RF, SVM, and NN) delivered thebest performance. It also should be noted that since the above-mentioned methods are all well-established methods whoseformulation and descriptions are readily available, the detailsof these methods are not discussed here.

In order to train the aforementioned models, the data ob-tained from years 2011 to 2013 is utilized. Moreover, thehyper-parameters associated with each model are tuned byusing the k-fold cross-validation technique, where k is selectedto be five. Considering the fact that the size of the data usedin this study is not considerably large, k-fold cross-validationappears to be essential, particularly to avoid the over-fittingproblem. Hyper-parameters that, on average, performs best oncross-validation are selected as the optimal set of parametersto train models. Afterwards, the trained models are applied tothe test data, the year 2014, and predictions are produced. Theoptimal hyper-parameters are as follow:

1) RF: trees = 20, mean sample leaf = 1, depth = 42) SVM: kernel = rbf, degree = 4, C = 13) NN: hidden layers = 1, hidden units = 120Fig. 11 illustrates the distribution of the error as well as the

confidence interval of the average error for each algorithm. Asseen in the figure, the inter-quartile range for SVM is narrower,suggesting the variability of error for this algorithm is lowerin this case study. Also, it can be observed that the maximumerror for SVM is smaller compared to the same statisticfrom two other algorithms. However, overlapping confidenceintervals indicate that there is no convincing evidence forthe superiority of any algorithm in terms of the populationaverage error. The performance of these algorithms can also

0.10 0.15 0.20 0.25 0.30 0.35NMAEa

SVM

RF

NN

Alg

orith

m

Mean Confidence Interval

Fig. 11: NMAEa for machine learning algorithms

11

be evaluated by analyzing the predicted versus actual targetvalues for each month aggregated over all areas shown in Fig.12.

Based on the figure, it can be realized that the SVMfollows the actual target value better, delivering smaller errors(shown in dotted line), particularly in the summer months. It isworth noting that while the SVM results in better performancefor this case study, such superiority cannot be generalized,and therefore should be explored for each dataset separately.Moreover, considering the fact that the scale of data used inthis case study is not significantly large, it would be difficultto state an obvious reason as to why the SVM works better.

D. Producing final predictionsThe total number of vegetation-related outages can be de-

fined as Total Outage Count Index (TOCI). The prediction ofthis index for area a and month m may be calculated using(15).

ˆTOCIam = ˆGV OCIam + ˆWVOCIam (15)

whereˆdenotes the predicted value.The HWS and SVM deliver the best performances on cross-

validation; hence, the final prediction is produced by usingthese algorithms. Fig. 13 demonstrates the actual numberof vegetation-related outages that occurred in the year 2014versus the prediction generated by the proposed approach foreach month aggregated over all defined areas. In order to showthe effectiveness of the proposed approach and to compareit with very simplistic approaches, we have created a naivemodel and presented its results in the figure as well. In thenaive model, we look at the outages that occurred in the pastfor each area on each month and simply calculate the averagevalue.

Based on the figure, it can be understood that the proposedapproach, in general, is able to generate prediction values thatclosely follow the actual value and its trend. In particular,as seen, for months February, March, May, and June, theproposed approach delivers excellent performance. However,differences between the prediction and actual counts for themonths January, April, and July may be observed. As ex-plained, the months of April and July in the year 2014 werespecial months since the number of weather-related vegetation

1 2 3 4 5 6 7Month

0

20

40

60

80

WVO

CIm

Act

ual v

s. P

redi

cted NN

RFSVMActualError (NN)Error (RF)Error (SVM)

Fig. 12: Actual vs. prediction for machine learning algorithms

Fig. 13: Final actual vs. prediction

outages that occurred during this time period were significantlylower than during the same time in previous years (we notethat this may be due to recording errors). It is also worthmentioning that although the proposed approach can be usedto make the prediction for any month in the future, the bestperformance is expected to be achieved when the model isutilized for one month ahead. This is because the most accurateweather forecasts are available for one month ahead. Moreover,time series models are particularly suitable for short-termpredictions.

Compared to the proposed approach, the naive model de-livers a poor performance. As seen in the figure, especiallyfor months April to July, the naive model over-predicts theoutages by a substantial margin of error. This demonstratesthat simply calculating the average number of outages by usingthe historical data and using those for making the predictiondoes not lead to satisfactory results. Moreover, this confirmsthat efforts devoted to developing the proposed approach resultin a significant improvement in producing a prediction forvegetation-related outages.

The performance of the proposed approach can also beevaluated by exploring the normalized error values. In orderto demonstrate the superiority of the proposed approach withregards to error values, we will utilize two other modelsto generate the final prediction and compare the results ofthose models with the proposed approach. The first modelis the naive model (i.e., simple average), which was intro-duced earlier. The second model is a benchmark machinelearning model. In the benchmark model, as opposed to thepropose approach in which we categorize outages into twogroups and build separate models for them, we feed allavailable inputs into one model and generate the prediction.The inputs to the benchmark model are all features shownin Fig. 7, as well as area number (dummy variable) andvarious time-related features. These inputs are fed into threemachine learning algorithms of SVM, NN, and RF to generatethe final prediction. By investigating the performance of theaforementioned algorithms, we realized that combining thepredictions produced by those (i.e., averaging the predictionsof all three algorithms) leads to obtaining the best performancefor the benchmark model. It is worth-mentioning that thehyper-parameters of those algorithms are tuned properly using10-fold cross-validation procedure. The values are as follow:

12

1) RF: trees = 10, mean sample leaf = 1, depth = 42) SVM: kernel = rbf, degree = 3, C = 13) NN: hidden layers = 1, hidden units = 100Fig. 14 presents the NMAE for each area and each model

shown with bars. As seen, the performance of the proposedapproach for some areas is remarkable with a minimum errorof 0.12. Also, on many occasions, especially for areas 7and 8, the proposed approach outperforms the benchmarkmachine learning model considerably. There are a few areas,such as areas 0 and 10 in which the proposed approachdoes not outperform the two other approaches. A possiblereason for that could be abrupt changes in the patterns oftree-trimming operations for those areas. Since the proposedapproach separately models the growth-related outages anduses time series analysis to capture their trend (reflecting tree-trimming impact) and seasonal behavior, any sudden change intrimming operations would impact the proposed model more.However, in case that the tree-trimming operation data wouldbe available, one might include that in the proposed approachto make sure that sudden changes will be captured as well.The naive model, on the other hand, delivers large errors onmost cases compared two other models.

In order to draw a meaningful comparison between the threemodels, we calculate the average error of all areas for eachmodel and present the result in Table I. From the table, it couldbe understood that the proposed approach results in the bestprediction performance by delivering an average error of 0.23.The error for the benchmark machine learning model is 0.35,which is considerably higher. The results of the naive model,as expected, is the worst. From the results, the following canbe inferred:

1) The proposed approach outperforms the naive model,demonstrating that efforts devoted to developing acomprehensive approach leads to obtaining significantimprovements compared to very simplistic approaches;

2) The proposed approach outperforms the benchmarkmachine learning model, demonstrating that categoriz-ing vegetation-related outages and building separatingmodels for them leads to improvement compared tobuilding a single model without differentiation betweencategories of outages

A visual demonstration of what the proposed approach canoffer to the decision makers is given in Fig. 15. Here, we

Fig. 14: NMAEa for final prediction for different areas

TABLE I: AVERAGE NMAE FOR DIFFERENT MODELS

Model Mean NMAENaive 0.61

Benchmark ML 0.35Proposed approach 0.23

Fig. 15: A demonstration of the application of the proposedapproach for different areas

present the prediction for the month of May in the year 2014for each area. Since we predict the number of outages thatoccur in the system, it provides great flexibility because theutility companies can decide whether or not the number ofoutages is critical based on their criteria and subsequently cancategorize outages based on their severity. For the sake ofillustration, the number of predicted outages are categorizedinto four distinct categories and color-coded, accordingly inthis study. For example, one can realize that areas 4 and 10have a high number of outages. On the other hand, areas 3,7, and 8 will experience a smaller number of outages in theaforementioned month. Using this platform, the operators canquickly identify vulnerable zones, and take necessary actions.

VI. CONCLUSIONS

A data-driven approach was proposed for predicting thenumber of vegetation-related outages in power distributionsystems on a monthly basis. Based on this study, the followingconclusions can be drawn.

1) In order to develop a practical approach, various typesof information including historical records of outages,climatological, and geographical variables should beobtained and processed.

2) A key step in building a realistic approach is to ade-quately define the extent of the predictions’ target area.Aggregating substations and creating broader geograph-ical areas by using clustering algorithms seem like aworkable solution for this purpose. This helps to harnessthe randomness of the number of outages and to obtainmore accurate weather information.

3) Vegetation-related outages occur due to various reasons;however, they could be categorized into two maingroups of growth-related and weather-related outages.Each category of the vegetation-related outage revealsa different pattern, and therefore requires a differenttreatment. Such categorization is necessary to build anaccurate model.

13

4) Due to the complex nature of vegetation-related out-ages and several factors that influence their occur-rence, utilizing simplistic approaches such as calcu-lating the average number of outages based on his-torical data does not lead to obtaining an accuratepredictive model; therefore, more sophisticated modelsare required. Moreover, the occurrence of these outagedepends on various subject which are subject to changeduring time; hence, models that take these factors intoconsiderations are required.

5) Time series models can successfully explain the patternsexisting within growth-related vegetation outages, andare able to produce convincing predictions.

6) Machine learning regression models that can handle thenon-linear interaction in the data are effective tools forpredicting weather-related vegetation outages.

Although many different pieces information pertaining tovegetation-related outages were considered in this study, wedid not account for all possible factors due to lack of accessto related data. As a result, the performance of the proposedapproach may be improved by the inclusion of additionalclimatological and geographical information (e.g., satelliteimages for more vegetation data). In fact, as mentioned earlier,all the advantages of the proposed approach are built upongeneric outage data collected by utilities, and typical dailyweather forecast data, which is publicly available. This factmakes the implementation of the approach easily attainablewithin a reasonable level of accuracy. However, the approachprovides the flexibility to be improved by utilizing variousother sources of data.

Moreover, obtaining a considerably larger scale of outagedata may open up unique opportunities for utilizing the mostadvanced predictive models including deep learning algorithmsfor predicting vegetation-related outages. It is worth mention-ing that the main contribution of this paper is not to comparethe performance of different predictive algorithms, but ratherproviding an approach for building robust predictive models forvegetation-related outages and creates solid foundations for it.Hence, utilizing more sophisticated time series models and AIapproaches within our proposed approach is highly encouragedand could result in more accurate predictions.

Results of this study could be very informative to utilitycompanies in gaining insight about their vegetation-relatedoutage problems, and to build better models for predictingthem. Especially, by providing a preliminary but accurateprediction, the proposed approach enables operators to takehigh-resolution imagery of areas exhibiting high risk of anoutage, or utilize LiDAR data, or dispatch a crew to findthe exact locations in the system that a vegetation-relatedoutage could occur. Moreover, in this paper, we have proposedworkable solutions to some existing problems and generatedseveral new features that can be used by researchers to improvetheir outage predictive models.

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