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Portfolio
Welcome to the portfolio. Here you’ll find a selection of data analysis visualization work. Explore the case study project works to learn more.
Case Study 1: Forecasted Meter Read Workload for mid sized power distribution utility
Problem
A mid-sized power distribution utility faced a significant challenge with the anticipated failure of its next-generation advanced metering infrastructure. The failure risk posed a direct threat to reliable customer billing and compliance with regulatory standards.
Solution
Initially, this utility had deployed the first generation of smart meters in the mid-2000s. However, these meters began to experience accelerated deterioration, particularly in their communication modules. After securing regulatory approval, the utility prepared to upgrade to next-generation meters. During field testing, it became apparent that these new meters were experiencing registration delays, taking over 30 days to connect to the network—a situation that could disrupt billing cycles.
To address this, the utility engaged our services to create a robust forecast of field workforce requirements under several worst-case scenarios. Utilizing raw data across multiple Excel worksheets detailing service territories and meter statuses by billing cycle, we applied Power BI tools to craft detailed visualizations of daily workloads, with particular attention to peak periods at the end of each billing cycle.
This analytical approach helped the utility understand the maximum meter read (MR) workload for each cycle. To ensure compliance and uninterrupted service, the utility proactively recruited and trained seasonal workers to manually read meters until they were fully registered in the network. For example, analysis for the month of June showed a peak need of 3,534 meter reads on the 10th, indicating the necessity to hire 35 seasonal workers in advance of this peak.
Result
Our analysis detailed daily meter read (MR) unit counts across different regions within the utility’s service area, organized by scheduled meter read dates. The visualizations provided a clear monthly view, exemplified by June 2024's data, showing day-by-day MR unit counts. This allowed the utility to strategically allocate the necessary workforce, prioritizing areas with the highest daily counts to ensure efficient, regulatory-compliant meter readings.
By effectively forecasting the impact of the registration delay, the utility not only maintained regulatory compliance but also ensured that customer billing remained accurate and uninterrupted during the transition to new technology. This strategic workforce management approach allowed the utility to navigate a potential crisis, turning a technical challenge into a demonstration of proactive, data-driven decision-making.
A mid-sized power distribution utility faced a significant challenge with the anticipated failure of its next-generation advanced metering infrastructure. The failure risk posed a direct threat to reliable customer billing and compliance with regulatory standards.
Solution
Initially, this utility had deployed the first generation of smart meters in the mid-2000s. However, these meters began to experience accelerated deterioration, particularly in their communication modules. After securing regulatory approval, the utility prepared to upgrade to next-generation meters. During field testing, it became apparent that these new meters were experiencing registration delays, taking over 30 days to connect to the network—a situation that could disrupt billing cycles.
To address this, the utility engaged our services to create a robust forecast of field workforce requirements under several worst-case scenarios. Utilizing raw data across multiple Excel worksheets detailing service territories and meter statuses by billing cycle, we applied Power BI tools to craft detailed visualizations of daily workloads, with particular attention to peak periods at the end of each billing cycle.
This analytical approach helped the utility understand the maximum meter read (MR) workload for each cycle. To ensure compliance and uninterrupted service, the utility proactively recruited and trained seasonal workers to manually read meters until they were fully registered in the network. For example, analysis for the month of June showed a peak need of 3,534 meter reads on the 10th, indicating the necessity to hire 35 seasonal workers in advance of this peak.
Result
Our analysis detailed daily meter read (MR) unit counts across different regions within the utility’s service area, organized by scheduled meter read dates. The visualizations provided a clear monthly view, exemplified by June 2024's data, showing day-by-day MR unit counts. This allowed the utility to strategically allocate the necessary workforce, prioritizing areas with the highest daily counts to ensure efficient, regulatory-compliant meter readings.
By effectively forecasting the impact of the registration delay, the utility not only maintained regulatory compliance but also ensured that customer billing remained accurate and uninterrupted during the transition to new technology. This strategic workforce management approach allowed the utility to navigate a potential crisis, turning a technical challenge into a demonstration of proactive, data-driven decision-making.
Case Study 2: Strategic Enhancement of SMOC Capabilities for Smart Meter Deployment
Challenge
A leading utility was set to expand its infrastructure capabilities through an advanced smart metering initiative. The Smart Metering Operations Center (SMOC), initially designed to manage up to 70,000 meters, faced a capacity challenge with the planned simultaneous ramp-up in 28 optimization areas. Analysis showed that without adjustments, meter management would exceed this threshold by 2026, potentially straining resources and affecting service during this crucial upgrade phase.
Solution
Our consultancy was tasked with optimizing the utility's data management to prepare for this expansion. We transformed the existing Excel datasets for better compatibility and integration with Power BI. This process involved reorganizing the data to ensure consistent reporting across all areas, establishing a system for week-by-week analysis over the next five years.
We redesigned the data structure to ensure that each of the 28 optimization areas was aligned and could be aggregated effectively. This setup enabled a comprehensive view of operations and facilitated detailed planning for resource allocation and workload management across the utility’s network.
Result
The revamped data management approach allowed the utility to effectively monitor and manage meters across all optimization areas, providing clear visibility into operations on a weekly basis for five years. This enhanced system supported proactive management of resources, ensuring operational stability and efficiency during the smart meter rollout.
This strategic update not only maintained operational fluency but also positioned the utility as a forward-thinking player in technology adoption, equipped to scale up and tackle future demands with robust solutions.
A leading utility was set to expand its infrastructure capabilities through an advanced smart metering initiative. The Smart Metering Operations Center (SMOC), initially designed to manage up to 70,000 meters, faced a capacity challenge with the planned simultaneous ramp-up in 28 optimization areas. Analysis showed that without adjustments, meter management would exceed this threshold by 2026, potentially straining resources and affecting service during this crucial upgrade phase.
Solution
Our consultancy was tasked with optimizing the utility's data management to prepare for this expansion. We transformed the existing Excel datasets for better compatibility and integration with Power BI. This process involved reorganizing the data to ensure consistent reporting across all areas, establishing a system for week-by-week analysis over the next five years.
We redesigned the data structure to ensure that each of the 28 optimization areas was aligned and could be aggregated effectively. This setup enabled a comprehensive view of operations and facilitated detailed planning for resource allocation and workload management across the utility’s network.
Result
The revamped data management approach allowed the utility to effectively monitor and manage meters across all optimization areas, providing clear visibility into operations on a weekly basis for five years. This enhanced system supported proactive management of resources, ensuring operational stability and efficiency during the smart meter rollout.
This strategic update not only maintained operational fluency but also positioned the utility as a forward-thinking player in technology adoption, equipped to scale up and tackle future demands with robust solutions.
Case Study 3: IDN Substation Feeder Analysis for Enhanced Distribution Efficiency
Problem
The IDN substations were experiencing challenges with voltage stability across their network, prompting a need to analyze feeder performance to identify and address areas with significant voltage drops. The goal was to categorize each substation feeder into voltage drop percentage groups, focusing specifically on those with drops under 5%, to prioritize and allocate resources effectively.
Solution
Our team used line-to-line and line-to-neutral measurements to calculate the total kilovolt-ampere (kVA) load for each feeder. This process involved pairing higher voltages (kV) with lower amperages (Amps) and vice versa, considering the varying voltage levels each feeder handled. This calculation helped us identify feeders with total kVA loads and voltage drops under the crucial 5% threshold.
To simplify the analysis and presentation, we introduced a "relative value" metric—a consolidated figure representing the total kVA load for each station. This metric was crucial in assessing the operational efficiency of each substation and determining the necessary task force allocations based on the specific needs identified.
Result
The analysis culminated in a detailed graphical representation of the total kVA loads for stations with voltage drops under 5%. The graph, presented to utility executives, organized stations alphabetically and showcased the relative value of each station's load. This visual tool was instrumental in facilitating strategic discussions on workforce allocation. It enabled the executives to make informed decisions about where to deploy resources to optimize voltage stability across the network.
By providing a clear and actionable overview of the substations requiring attention, the analysis supported the utility in enhancing its operational efficiency and ensuring a more reliable power distribution to its customers.
The IDN substations were experiencing challenges with voltage stability across their network, prompting a need to analyze feeder performance to identify and address areas with significant voltage drops. The goal was to categorize each substation feeder into voltage drop percentage groups, focusing specifically on those with drops under 5%, to prioritize and allocate resources effectively.
Solution
Our team used line-to-line and line-to-neutral measurements to calculate the total kilovolt-ampere (kVA) load for each feeder. This process involved pairing higher voltages (kV) with lower amperages (Amps) and vice versa, considering the varying voltage levels each feeder handled. This calculation helped us identify feeders with total kVA loads and voltage drops under the crucial 5% threshold.
To simplify the analysis and presentation, we introduced a "relative value" metric—a consolidated figure representing the total kVA load for each station. This metric was crucial in assessing the operational efficiency of each substation and determining the necessary task force allocations based on the specific needs identified.
Result
The analysis culminated in a detailed graphical representation of the total kVA loads for stations with voltage drops under 5%. The graph, presented to utility executives, organized stations alphabetically and showcased the relative value of each station's load. This visual tool was instrumental in facilitating strategic discussions on workforce allocation. It enabled the executives to make informed decisions about where to deploy resources to optimize voltage stability across the network.
By providing a clear and actionable overview of the substations requiring attention, the analysis supported the utility in enhancing its operational efficiency and ensuring a more reliable power distribution to its customers.
Case Study 4: Enhancing Data Integration for Meter Replacement Strategy Across Combined Regions
Problem
The integration of two geographic regions, Couchiching (COU) and Orillia (OLA), presented a significant challenge for a utility preparing for a meter replacement field trial. The merger required a detailed analysis to address discrepancies in meter read data between the two regions. Accurate scheduling of meter reads was crucial to efficiently manage the workload on specific dates throughout 2023 and 2024.
Solution
To tackle this challenge, our team consolidated datasets from two distinct systems: PIQ and PEC. We first harmonized the data into a single dataset covering both COU and OLA. This involved a meticulous organization of the data by next meter read date (MRD) followed by meter read units (MRUs) to ensure precise data management and effective visualization.
The structured data was then imported into Power BI, allowing for advanced querying and analysis. We also converted geographical data from the original Excel files into KMZ files for visualization in Google Earth, which provided a detailed geographical perspective on meter locations within each region.
This comprehensive approach enabled us to not only identify but also visually represent the discrepancies between the existing datasets. It also facilitated a more efficient preparation for the meter replacement trial by providing a clear geographical layout of meter locations and a detailed timeline of meter reading schedules.
Result
The final output was a series of detailed visualizations, displayed in Power BI, which illustrated the distribution of MRUs by day, month, and year for each region. These visuals were initially created for each region separately and then combined to provide a unified view. The visuals included an advanced filtering mechanism to highlight data points where the count of MRUs was ten or higher, ensuring focus on areas with significant activity.
These visualizations effectively displayed the total meter reads required for each MRU group on specific days, using color coding to differentiate between the two regions and to highlight areas with the highest meter read demands.
The strategic data integration and visualization process not only streamlined the trial’s preparation phase but also equipped the utility’s management team with the tools needed to make informed decisions on resource allocation and scheduling. This led to improved efficiency and readiness for the upcoming meter replacement trial across the newly combined regions.
The integration of two geographic regions, Couchiching (COU) and Orillia (OLA), presented a significant challenge for a utility preparing for a meter replacement field trial. The merger required a detailed analysis to address discrepancies in meter read data between the two regions. Accurate scheduling of meter reads was crucial to efficiently manage the workload on specific dates throughout 2023 and 2024.
Solution
To tackle this challenge, our team consolidated datasets from two distinct systems: PIQ and PEC. We first harmonized the data into a single dataset covering both COU and OLA. This involved a meticulous organization of the data by next meter read date (MRD) followed by meter read units (MRUs) to ensure precise data management and effective visualization.
The structured data was then imported into Power BI, allowing for advanced querying and analysis. We also converted geographical data from the original Excel files into KMZ files for visualization in Google Earth, which provided a detailed geographical perspective on meter locations within each region.
This comprehensive approach enabled us to not only identify but also visually represent the discrepancies between the existing datasets. It also facilitated a more efficient preparation for the meter replacement trial by providing a clear geographical layout of meter locations and a detailed timeline of meter reading schedules.
Result
The final output was a series of detailed visualizations, displayed in Power BI, which illustrated the distribution of MRUs by day, month, and year for each region. These visuals were initially created for each region separately and then combined to provide a unified view. The visuals included an advanced filtering mechanism to highlight data points where the count of MRUs was ten or higher, ensuring focus on areas with significant activity.
These visualizations effectively displayed the total meter reads required for each MRU group on specific days, using color coding to differentiate between the two regions and to highlight areas with the highest meter read demands.
The strategic data integration and visualization process not only streamlined the trial’s preparation phase but also equipped the utility’s management team with the tools needed to make informed decisions on resource allocation and scheduling. This led to improved efficiency and readiness for the upcoming meter replacement trial across the newly combined regions.
Case Study 5: Ensuring Data Integrity through Rigorous Cleansing Before Integration
Problem
As part of a broader initiative to integrate datasets from two overlapping regions, Couchiching (COU) and Orillia (OLA), a utility encountered significant discrepancies in meter data attributes. These inconsistencies needed to be addressed to ensure data integrity for effective analysis and integration. The utility required a detailed day-by-day, month-by-month review of meter read units (MRUs) for 2023 and 2024 to prepare the datasets for integration into Power BI.
Solution
To tackle this challenge, our team implemented a structured approach to cleanse the datasets for each region separately. This process was critical to maintaining the accuracy and consistency of the data before any integration could occur. We established a procedure where each region’s data was first aligned and formatted according to a standardized Mass Deploy master dataset. This included sorting by MRU and adding a next meter read date (next_MRD) column to streamline subsequent data merging.
We also conducted a detailed review of device locations within the datasets, particularly identifying and labeling any MRUs lacking precise device location data before they were imported into Power BI. To further ensure data quality, we compared latitude and longitude data for the OLA region using KMZ files in Google Earth, which allowed us to directly compare the geographic data provided by different sources (PEC vs. PIQ).
Result
The rigorous data cleansing process enabled the creation of precise and informative visualizations for each region, handled separately to maintain clarity and focus. These visuals were generated in Power BI and displayed MRU counts by day, month, and year, with an advanced filtering applied to highlight counts greater than or equal to 20 MRUs.
Each visualization provided a clear, detailed view of the data, organized chronologically and segmented by specific areas within each region, as indicated by the first eight letters of the region code. These visuals not only showcased the total meter reads required per day but also highlighted which MRU groups within the regions were most active, using color coding to distinguish between them.
The final presentations of these visuals for October 2023 through August 2024 were compiled into an Excel file with clear labels and screenshots, prepared for executive review. This approach not only facilitated a more informed discussion among utility executives but also ensured that the data integration phase could proceed with confidence in the accuracy and completeness of the datasets.
As part of a broader initiative to integrate datasets from two overlapping regions, Couchiching (COU) and Orillia (OLA), a utility encountered significant discrepancies in meter data attributes. These inconsistencies needed to be addressed to ensure data integrity for effective analysis and integration. The utility required a detailed day-by-day, month-by-month review of meter read units (MRUs) for 2023 and 2024 to prepare the datasets for integration into Power BI.
Solution
To tackle this challenge, our team implemented a structured approach to cleanse the datasets for each region separately. This process was critical to maintaining the accuracy and consistency of the data before any integration could occur. We established a procedure where each region’s data was first aligned and formatted according to a standardized Mass Deploy master dataset. This included sorting by MRU and adding a next meter read date (next_MRD) column to streamline subsequent data merging.
We also conducted a detailed review of device locations within the datasets, particularly identifying and labeling any MRUs lacking precise device location data before they were imported into Power BI. To further ensure data quality, we compared latitude and longitude data for the OLA region using KMZ files in Google Earth, which allowed us to directly compare the geographic data provided by different sources (PEC vs. PIQ).
Result
The rigorous data cleansing process enabled the creation of precise and informative visualizations for each region, handled separately to maintain clarity and focus. These visuals were generated in Power BI and displayed MRU counts by day, month, and year, with an advanced filtering applied to highlight counts greater than or equal to 20 MRUs.
Each visualization provided a clear, detailed view of the data, organized chronologically and segmented by specific areas within each region, as indicated by the first eight letters of the region code. These visuals not only showcased the total meter reads required per day but also highlighted which MRU groups within the regions were most active, using color coding to distinguish between them.
The final presentations of these visuals for October 2023 through August 2024 were compiled into an Excel file with clear labels and screenshots, prepared for executive review. This approach not only facilitated a more informed discussion among utility executives but also ensured that the data integration phase could proceed with confidence in the accuracy and completeness of the datasets.
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