ElectroTempo offers a predictive analytics platform that forecasts EV charging demand for utilities and fleet operators. Its data-driven projections help clients de-risk EV investments by providing granular, hour-by-hour demand forecasts to optimize infrastructure planning and grid management.
Funding
$5M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
The transition to electric vehicles (EVs) presents significant challenges for utilities and fleet operators in accurately forecasting charging demand and optimizing infrastructure investments. This lack of precise data hinders efficient EV adoption and can lead to suboptimal grid management and capital allocation.
Solution
ElectroTempo provides a predictive analytics platform designed to de-risk major electric vehicle investments for utilities and fleet operators. Leveraging deep subject matter expertise in transportation and utility infrastructure, the platform generates data-driven projections for EV charging demand. These forecasts enable clients to accurately assess costs, benefits, and risks associated with EV adoption and infrastructure needs at scale. By integrating fleet operations data with utility asset information, ElectroTempo delivers granular, hour-by-hour charging demand forecasts resolved down to the street block level. This empowers organizations to make informed decisions regarding infrastructure deployment, grid upgrades, and fleet electrification strategies, ultimately accelerating the transition to e-mobility.
Target Audience
ElectroTempo serves electric utilities, fleet operators, and port and terminal operators who are planning and managing the transition to electric vehicle infrastructure.
Features
- Predictive analytics platform for EV charging demand forecasting, resolving to hourly and street-block levels.
- Integrates fleet operations data with electric utility asset information for comprehensive analysis.
- Machine learning models to forecast EV demand and its impact on utility grids and infrastructure.
- Site analysis tools (EV In-Sites) to evaluate property suitability for EV charging infrastructure deployment.
- ZEV Risk Management Toolkit for identifying suitable zero-emission vehicles and assessing environmental impact.
- Operational modeling to simulate fleet electrification scenarios and optimize charging schedules.
- Financial analysis tools to assess total cost of ownership and identify funding opportunities for ZEV infrastructure.
- Corridor charging demand analysis capabilities, demonstrated in utility Integrated Resource Plans.
- Data-driven insights for utilities to proactively plan grid upgrades and identify charging hotspots.
- Tools for fleet managers to assess electrification feasibility, optimize operational schedules, and reduce costs.