To fit the unique goals & budgets of any city, state or utility, we’ve distributed the features of our software into four tiered modules.
Our software platform can be deployed in select pilot programs for a neighborhood or comprehensively for an entire city or state, depending on the available data.
Module A contains all the features of our Customer Targeting & Analytics Tool. This plan provides program implementers and city, state, or utility staff with a user-friendly filtering and mapping tool that helps determine the best candidates to target for rooftop PV, fuel switching, and other energy upgrades. In addition to geographic and structural filters, users can also sort and filter by property owners’ environmental attitudes, family makeup, propensity to invest in upgrades, and other census data.
Target homes and buildings can be viewed and filtered geographically and users can easily export all available data, including each address on any target list. This tool also features visual replacement timelines to better understand and depict how soon a city or state can meet their carbon reduction goals.
Module B includes the Lead Tracking Dashboard & Consumer-Facing DIY Roadmap Tool. The Lead Tracking tool helps contractors, utilities, and associated programs seamlessly process, manage, and report on the outcomes of leads generated by the Customer Targeting & Analytics tool. This module also unlocks the platform’s ability to generate consumer-facing custom energy roadmaps for each building in a selected area. Leads can be synced with efficiency program and utility data, and then automatically integrated into a presentation-ready sales proposal that can include any combination of energy upgrades and a 20-year cash flow analysis.
Module C includes the Hourly Energy Modeling & Grid Impact Tools. This module leverages our hourly energy modeling capabilities, which determine the cost effectiveness of various energy improvements for a specified building. Module C takes these insights a step further, analyzing the specific impacts that any individual improvement package will have on the utility grid, such as transformer or substation overloading.
Module D includes the Market Factor Analysis. Utilizing machine learning, we determine how certain market factors will influence the cost effectiveness of different efficiency upgrade scenarios. These factors could include interest rates, solar, the costs of electricity and fuels, and the type and extent of the potential energy efficiency improvements. This plan provides the ability to quickly identify the the policies, rebates, or education to deploy in order to support the efficiency upgrade scenario that is under consideration.