Demand Forecasting
Predict charging demand using historical and real-time data
Smart EV charging without grid stress
Context
As EV adoption grows, unmanaged charging increases peak loads and puts pressure on grid infrastructure. Charging without coordination leads to higher costs, overload risks, and inefficient energy usage across networks.
We usually work best with teams who know building software is more than just shipping code.
EV charging network operators
Fleet operators managing electric vehicles
Utilities managing grid load and demand
Smart city infrastructure planners
Commercial and residential charging providers
Organizations without EV charging infrastructure
Small setups with minimal charging demand
Projects without load management requirements
Teams not interested in automation or optimization
Use cases without multi-charger coordination
Problem framing
Simultaneous charging causes peak spikes and strain on transformers and feeders. Operators lack control over charging timing, struggle to forecast demand, and cannot balance loads effectively. This leads to higher operational costs and poor coordination across stations and fleets.
Allowing uncontrolled simultaneous charging
Manual scheduling of charging sessions
No demand forecasting or load planning
Ignoring time-of-day pricing and tariffs
Limited coordination across charging points
High peak loads and grid stress
Increased risk of infrastructure overload
Higher energy costs due to poor scheduling
Low visibility into demand and usage
Poor user experience and unpredictability
Delivery scope
Structured building blocks we use to de-risk delivery and keep enterprise programs predictable.
Predict charging demand using historical and real-time data
Priority-based charging based on vehicle needs, battery levels, and urgency
Distribute load across chargers to prevent overload and optimize usage
Schedule charging based on time-of-day pricing to reduce costs
APIs and integrations with utilities and charging infrastructure for control and coordination
Analyze charging patterns and demand behavior
Implement AI-based forecasting and scheduling models
Enable real-time load balancing across chargers
Integrate with grid systems and user interfaces
We build smart charging scheduling platforms that manage EV charging in real time. Using demand forecasting, AI-based scheduling, and dynamic load balancing, we help operators optimize charging while maintaining grid stability and improving user experience.
Measurable results teams plan for when we ship the full stack, integrations, and governance together.
Reduced peak load and grid stress
Lower energy costs through optimized charging
Improved efficiency for charging operations
Better user experience with predictable charging
Share scope, constraints, and timelines. We respond with a clear delivery approach, not a generic pitch deck.
Start the conversationStraight answers procurement and engineering teams ask before a build kicks off.
Yes. We integrate via OCPP and custom APIs.
Yes. The system scales from small complexes to large CPO networks.
Yes. User preferences are factored into scheduling.
Yes. Utilities can send signals for load control or tariff changes.
Absolutely, fleet-first scheduling and prioritisation are built in.
Short answers if you are deciding who builds and supports this kind of work.
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Share your details with us, and our team will get in touch within 24 hours to discuss your project and guide you through the next steps