This post was co-written by Louis Lim, a manager in Accenture AWS Business Group, and Soheil Moosavi, a data scientist consultant in Accenture Applied Intelligence (AAI) team.
Virtually every electric customer in the US and Canada has, at one time or another, experienced a sustained electric outage as a direct result of a tree and power line contact. According to the report from Federal Energy Regulatory Commission (FERC.gov), Electric utility companies actively work to mitigate these threats.
Vegetation Management (VM) programs represent one of the largest recurring maintenance expenses for electric utility companies in North America. Utilities and regulators generally agree that keeping trees and vegetation from conflicting with overhead conductors. It is a critical and expensive responsibility of all utility companies concerned about electric service reliability.
Vegetation management such as tree trimming and removal is essential for electricity providers to reduce unwanted outages and be rated with a low System Average Interruption Duration Index (SAIDI) score. Electricity providers are increasingly interested in identifying innovative practices and technologies to mitigate outages, streamline vegetation management activities, and maintain acceptable SAIDI scores. With the recent democratization of machine learning leveraging the power of cloud, utility companies are identifying unique ways to solve complex business problems on top of AWS. The Accenture AWS Business Group, a strategic collaboration by Accenture and AWS, helps customers accelerate their pace of innovation to deliver disruptive products and services. Learning how to machine learn helps enterprises innovate and disrupt unlocking business value.
In this blog post, you learn how Accenture and AWS collaborated to develop a machine learning solution for an electricity provider using Amazon SageMaker. The goal was to improve vegetation management and optimize program cost.
Overview of solution
VM is generally performed on a cyclical basis, prioritizing circuits solely based on the number of outages in the previous years. A more sophisticated approach is to use Light Detection and Ranging (LIDAR) and imagery from aircraft and low earth orbit (LEO) satellites with Machine Learning models, to determine where VM is needed. This provides the information for precise VM plans, but is more expensive due to cost to acquire the LIDAR and imagery data.
In this blog, we show how a machine learning (ML) solution can prioritize circuits based on the impacts of tree-related outages on the coming year’s SAIDI without using imagery data.
We demonstrate how to implement a solution that cross-references, cleans, and transforms time series data from multiple resources. This then creates features and models that predict the number of outages in the coming year, and sorts and prioritizes circuits based on their impact on the coming year’s SAIDI. We show how you use an interactive dashboard designed to browse circuits and the impact of performing VM on SAIDI reduction based on your budget.
- Source data is first transferred into an Amazon Simple Storage Service (Amazon S3) bucket from the client’s data center.
- Next, AWS Glue Crawlers are used to crawl the data from the source bucket. Glue Jobs were used to cross-reference data files to create features for modeling and data for the dashboards.
- We used Jupyter Notebooks on Amazon SageMaker to train and evaluate models. The best performing model was saved as a pickle file on Amazon S3 and Glue was used to add the predicted number of outages for each circuit to the data prepared for the dashboards.
- Lastly, Operations users were granted access to Amazon QuickSight dashboards, sourced data from Athena, to browse the data and graphs, while VM users were additionally granted access to directly edit the data prepared for dashboards, such as the latest VM cost for each circuit.
- We used Amazon QuickSight to create interactive dashboards for the VM team members to visualize analytics and predictions. These predictions are a list of circuits prioritized based on their impact on SAIDI in the coming year. The solution allows our team to analyze the data and experiment with different models in a rapid cycle.
We were provided with 6 years worth of data across 127 circuits. Data included VM (VM work start and end date, number of trees trimmed and removed, costs), asset (pole count, height, and materials, wire count, length, and materials, and meter count and voltage), terrain (elevation, landcover, flooding frequency, wind erodibility, soil erodibility, slope, soil water absorption, and soil loss tolerance from GIS ESRI layers), and outages (outage coordinated, dates, duration, total customer minutes, total customers affected). In addition, we collected weather data from NOAA and DarkSky datasets, including wind, gust, precipitation, temperature.
Starting with 762 records (6 years * 127 circuits) and 226 features, we performed a series of data cleaning and feature engineering tasks including:
- Dropped sparse, non-variant, and non-relevant features
- Capped selected outliers based on features’ distributions and percentiles
- Normalized imbalanced features
- Imputed missing values
- Used “0” where missing value meant zero (for example, number of trees removed)
- Used 3650 (equivalent to 10 years) where missing values are days for VM work (for example, days since previous tree trimming job)
- Used average of values for each circuit when applicable, and average of values across all circuits for circuits with no existing values (for example, pole mean height)
- Merged conceptually relevant features
- Created new features such as ratios (for example, tree trim cost per trim) and combinations(for example, % of land cover for low and medium intensity areas combined)
After further dropping highly correlated features to remove multi-collinearity for our models, we were left with 72 features for model development. The following diagram shows a high-level overview data partitioning and number of outages prediction.
Our best performing model out of Gradient Boosting Trees, Random Forest, and Feed Forward Neural Networks was Elastic Net, with Mean Absolute Error of 6.02 when using a combination of only 10 features. Elastic Net is appropriate for smaller sample for this dataset, good at feature selection, likely to generalize on a new dataset, and consistently showed a lower error rate. Exponential expansion of features showed small improvements in predictions, but we kept the non-expanded version due to better interpretability.
When analyzing the model performance, predictions were more accurate for circuits with lower outage count, and models suffered from under-predicting when the number of outages was high. This is due to having few circuits with a high number of outages for the model to learn from.
The following chart below shows the importance of each feature used in the model. An error of 6.02 means on average we over or under predict six outages for each circuit.
We designed two types of interactive dashboards for the VM team to browse results and predictions. The first set of dashboards show historical or predicted outage counts for each circuit on a geospatial map. Users can further filter circuits based on criteria such as the number of days since VM, as shown in the following screenshot.
The second type of dashboard shows predicted post-VM SAIDI on the y-axis and VM cost on the x-axis. This dashboard is used by the client to determine the reduction in SAIDI based on available VM budget for the year and dispatch the VM crew accordingly. Clients can also upload a list of update VM cost for each circuit, and the graph will automatically readjust.
This solution for Vegetation management demonstrates how we used Amazon SageMaker to train and evaluate machine learning models. Using this solution an Electric Utility can save time and cost, and scale easily to include more circuits within a set VM budget. We demonstrated how a utility can leverage machine learning to predict unwanted outages and also maintain vegetation, without incurring the cost of high-resolution imagery.
Further, to improve these predictions we recommend:
- A yearly collection of asset and terrain data (if data is only available for the most recent year, it is impossible for models to learn from each years’ changes),
- Collection of VM data per month per location (if current data is collected only at the end of each VM cycle and only per circuit, monthly, and subcircuit modeling is impossible), and
- Purchasing LiDAR imagery or tree inventory data to include features such as tree density, height, distance to wires, and more.
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