Retail Energy Service Providers

For retail energy providers who are looking to differentiate their offerings and generate new revenue streams, offer innovative product and service offerings for customers, and leverage predictive models that improve profitability.

Increase Revenue Streams and Differentiate Your Offerings for Retail Energy Service Providers

Featured Use Cases for Retail Energy Service Providers

Customer Engagement | Grid4C

New Revenue Streams | Customer Engagement

Home Appliance Fault Prediction, Detection, and Diagnosis Grid4C’s algorithms can detect, diagnose, and in many cases predict malfunctions, inefficiencies and behavioral anomalies for many homes and small business appliances, without the installation of expensive hardware. These issues are identified for major appliances, including HVAC systems, water heaters, pool pumps, the home envelope, refrigerators, and smart thermostats. Utilities can easily benefit from these capabilities to:

• Monetize these faults by selling repair services, appliance warranties, or new appliances to customers as a value-add service

• Increase customer trust and loyalty by notifying customers when their appliances are malfunctioning or operating inefficiently

• Show customers how much inefficient appliances are costing them on their energy bills, and how much they can save by making the repairs

Non-Intrusive Load Disaggregation | Grid4C

Customer Engagement | Non-Intrusive Load Disaggregation

By analyzing smart meter usage data, Grid4C’s algorithms can disaggregate energy use for each premise, and for each appliance within that premise, producing views for the customer that show usage and costs for each appliance on a monthly, daily, or hourly basis. Predictive models are run and re-run every day for each premise, projecting appliance usage based on machine learning algorithms on an hourly basis, as much as 31 days in advance.

Predictive Analytics | Grid4C

Energy Efficiency Demand Response | Predictive Analytics for TOU or Demand Rate Customers

Predictive models are valuable to determine the expected usage for each customer and each appliance for every hour, as much as 31 days in advance. Customers on TOU or demand rates would particularly benefit from alerts that can be generated from predictive models showing expected high usage for certain appliances during high price tiers. Customers can:

• Manage their energy bills with a better understanding of how much each appliance costs them, with monthly, daily, or hourly views

• Save money and energy to run appliances more efficiently, especially customers on TOU or demand rates

• Be notified when predictive models indicate certain appliances are expected to use more energy than necessary during high price tiers

• Receive alerts when certain appliances exceed usage or cost thresholds set by the customer

Grid4C's Algorithms for predictive models | Grid4C

Customer Engagement | Bill and Usage Forecasting

Grid4C’s algorithms build predictive models for every customer that predict how customers will behave and how much energy each of their appliances will consume for the next 31 days, normalized for weather. Customers can view forecasted daily costs and electricity usage for the forthcoming week. The models also generate daily and weekly forecasted electricity usage in one hour intervals. Customers are able to:

• View their predicted energy bill for each month

• Set and configure alerts when certain appliances exceed usage or cost thresholds

• Better budget their energy costs

New Revenue Streams | Grid4C

New Revenue Streams | Energy Efficiency | Smart Thermostat Optimization

By combining customer usage data with smart thermostat data, we can help customers optimize thermostat settings and predict and detect faulty home appliances to deliver real customer value.

• Deliver energy savings

• Optimize smart thermostat settings and simulate how any temperature setting changes will impact a customer’s forecasted energy bill

• Alert customers when inefficient or fault home appliances are detected for major home appliances and home envelope issues

• Predict and diagnose HVAC faults

• Present customers with energy use and costs on a monthly, daily, or hourly basis

• Alert customers when deviations from their usage patterns are detected

Customer Churn Analysis | Grid4C

Customer Churn Analysis

Churn Analysis solution analyzes customer parameters, consumption behavior profiles and patterns, and spatial data, to predict each customer's probability to churn as a function of time and provide customer root-cause analysis.

Granular Load Forecasting | Grid4C

Granular Load Forecasting

Load forecasting at the meter/transformer level provides accurate load forecasts at the meter and sub-hour levels. Bottom-up aggregations and top-down disaggregations enable the forecast at any level of granularity (appliance/ transformer/feeder/ industry/territory level etc.). Advanced visualization tools enable the end user to self-explore the data, run simulations and what-if analysis and more.

Solar and DER Forecasting | Grid4C

Solar and DER Forecasting

An adaptive automated engine provides accurate solar power forecasting based on proprietary machine learning algorithms, can detect inefficiencies with solar panels, and maximize profitability of solar usage against the predictive model for each home for customers with solar.

Fault Predictions for Grids | Grid4C

Fault Predictions and Detections for Grid Assets

Grid4C’s algorithms can detect, diagnose, and in many cases predict malfunctions and inefficiencies for assets on the grid, without the installation of expensive hardware.

Outage Restoration | Grid4C

Improving Outage Restoration

Grid4C algorithms leverage smart meter data and other historical outage data to predict outage restoration times, outage impact, and outage root cause to improve outage response times and communication to customers.

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