Awesense Use Case Library

Assess and analyze wildfire risk associated with electric utility assets.

Dispatch, execute and manage the work orders for teams deployed to work on the grid.

Analyze the impact of Time-of-Use (ToU) rates on consumption patterns, consumption peaks and utilities’ income.

Ability to conduct electric vehicle (EV) and appliance desegregation analysis to pinpoint hidden loads of interest.

An use case for analyzing smart meter flags to detect energy theft and identify patterns of abnormal usage.

A use case incorporating the DLR to evaluate line operating capacity based on actual conditions, improving grid reliability and efficiency while supporting renewable energy integration.

A use case for effective load capacity analysis of feeders and laterals to find out where the loads exist and where they pose the biggest bottlenecks.

A use case for visualizing data from the feeder’s voltage monitoring equipment to improve the power quality and reliability of an electrical system. It enables engineers to identify patterns, trends, and anomalies in the data, which aids in problem detection and diagnosis.

A use case for integrating and contextualizing power quality measurement data from different sources for better diagnosis, improved reliability, compliance, safety, and cost savings.

A use case for verification of proper billing in the presence of instrumental potential transformer and instrumental current transformer.

A use case to accurately assess load changes to avoid unnecessary costs, such as replacing transformers earlier than necessary.

A tool for mitigating imbalanced feeders by suggesting locations of the grid and loads which can be reconnected (shifted) to different phase.

A use case for accurate load forecasting for utilities to operate efficiently, ensure reliable power supply, and lower costs.

A use case for easy, fast and inexpensive meter-to-transformer association data verification and correction.

A use case for estimation of increased load from EV growth and grid locations identification where the impact of this load will be concerning.

A use case for analytical approach to reveal incorrect meter wiring connection.

A use case to check and ensure that the phase identification data in the system of records is aligned with the actual field wiring.

Load profile estimation is used when real profile data are missing to obtain a realistic energy consumption profile. These load profiles have various uses, such as short-term analysis, grid load estimates, energy balances, and load flows.

Optimizing the grid by reconnecting medium/low voltage stations to high/medium voltage stations is a cost-effective solution. Automation and data analysis are necessary for efficient optimization.

Model outages to understand previous incidents and assess the risk and impact of future ones.

Address electricity distribution challenges by synchronizing grid asset and time series data into a single map.

Real energy theft detection achieved through grid data analytics.

Non-technical loss investigation management for effective loss reduction programs.

Use energy balancing to reduce utility losses; maximize on grid modernization and reliability resources.

Deploy smart meter and IoT devices across the grid using strategies driven by data.

Create weighted analysis models to understand distribution grid segments and individual elements.

Topological grid segmentation on the Awesense Platform allows utilities to understand consumer behavior and asset status with greater precision, as well as deploy IoT sensors in a more targeted manner.

Deep dive into grid data with automated alerts & warnings that are georeferenced to a map.

Increase awareness of behind-the-meter microgeneration, so that safety and the utility-customer relationship are improved.

Monitor, collect historical data, and plan for the future grid with conneted IoT devices and Awesense’s TGI platform.

Achieve full asset situational awareness, prevent unnecessary damages, and avoid harmful influences in the grid on the Awesense Platform.

Leverage various grid data sources to conduct precise grid capacity analysis.

Conduct PV generation analyses more quickly and effectively with synchronized data from multiple sources on the grid.

Streamline and increase data integrity in utility data challenges using the Awesense Energy Transition Platform by providing easy access to valuable data and a whole library of use cases.

Forecast load that reflects today’s increasingly distributed grid, where DERs lower demand and mask additional load also connected to the grid.

Make master circuit break values accurately reflect consumption, saving the customer money and avoiding costly grid upgrades.

Errors in meter-to-phase assignation can result from inaccurate data about the phase the energy consumer connects. Such errors adversely influence grid operations & outage management.

Discover the real capacity of your grid to support EVs using data from chargers, meters, and other assets. Help your customers manage costs and avoid unnecessary grid upgrades.

Lower CAPEX of substation assets through data-driven asset management on The Awesense Energy Transition Platform.

Bring together granular asset data to help utilities make informed business and operational decisions for their assets.

With cleansed data, a utility can perform insightful analysis requiring asset situational awareness and better address the challenges of managing the energy transition and improving grid reliability.

Simulate and quantify the PV capacity for a Low Volt grid before reaching a state of reversed power flow.

Identify how much EV Charging infrastructure can be accommodated given transformer load capacity, at certain times.

Pin-point phase imbalances on the grid and find where they can be best optimized. Balance phases accordingly.

Visualize, analyze, and correct power factor values to optimize the electrical grid.

Utilize alternative sensors located in the grid for customer billing purposes where metering data is not reliable.

Analyze transformers downstream from where a planned outage will occur to identify opportunities for asset upgrades.

Calculate Coincident Peak (CP) and Non-Coincident Peak (NCP) at various levels of aggregation and geographies.

Automated outage impact analysis for utility revenue and their customers.

Analyze transformer loading patterns and health across all transformers in a service territory.

Closely monitor grid temperatures and calculate risk using multiple types of grid and weather data.