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
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 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 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
• 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
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
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.
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.
An adaptive automated engine provides accurate solar power forecasting based on proprietary machine learning algorithms, can detect inefficiencies with solar panels, and maximize
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.
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.
Back to Top