A Different Energy Problem
Most energy technology platforms are built around a single objective: reduce total energy consumption. Use less. Be more efficient.
For commercial and industrial facilities, however, the dominant cost driver is often not how much energy is used, but when it is used.
Electricity tariffs for large facilities are typically governed by peak demand—the highest 15-minute average power draw within a billing cycle. That single peak can represent 30–50% of a monthly electricity bill, regardless of how efficiently the building operates the rest of the time. A brief overlap of loads—such as a chiller restart coinciding with elevator usage and EV charging—can permanently lock in elevated costs for the entire month.
This demand problem has consequences beyond billing. Demand spikes place stress on local grid infrastructure, often triggering requirements for costly transformer upgrades or limiting the ability to electrify assets such as vehicle fleets and industrial equipment. In dense urban environments, these constraints are becoming a major bottleneck to decarbonization.
Wattif Technologies was founded to address this precise challenge: intelligent demand management at scale, using real-time data to predict, shape, and coordinate energy usage across complex facilities.
Why Data Architecture Matters for Demand Intelligence
Demand optimization is fundamentally a time-series problem. To understand and control demand, a platform must:
- Ingest high-frequency telemetry from HVAC, electrical panels, meters, sensors, and control systems
- Analyze rolling 15-minute demand windows in real time
- Correlate behavior across heterogeneous equipment types
- Compare current performance against historical baselines spanning weeks, months, and seasons
- Scale from a single building to portfolios of sites without redesigning the data model
Traditional relational databases struggle with this workload due to schema rigidity, join complexity, and performance degradation at scale. Wattif required a time-series foundation that could support both operational responsiveness and long-term analytical depth.
Why TDengine
Wattif selected TDengine TSDB as the core data platform for its energy intelligence stack based on several architectural advantages:
- Supertable architecture aligned with physical assets TDengine’s supertable model maps naturally to equipment hierarchies—buildings, floors, systems, and individual assets—allowing Wattif to query across entire portfolios without complex joins or duplicated schemas.
- High-throughput ingestion with efficient compression The platform reliably ingests thousands of data points per second from distributed systems while achieving 10:1 or greater compression, enabling long retention periods without excessive storage costs.
- Native time-window analytics for demand tracking Continuous aggregation and time-window functions enable precise, real-time monitoring of rolling 15-minute demand intervals—the metric that directly determines peak charges.
- Low-latency historical queries Wattif can compare current operating conditions against historical demand patterns in real time, supporting both predictive insights and automated control strategies.
- Operational simplicity at scale As deployments expand across multiple sites and sectors, TDengine’s schema flexibility allows Wattif to reuse equipment templates and analytics logic with minimal reconfiguration.
Current Deployment: The GEAR, Singapore
The GEAR is a commercial facility in Singapore dedicated to sustainability, innovation, and next-generation building operations. It serves as a flagship deployment for Wattif’s demand intelligence platform.
Using TDengine as the data backbone, Wattif streams real-time telemetry from HVAC systems, chillers, electrical distribution panels, and submeters. The platform establishes dynamic demand baselines, continuously evaluates rolling demand windows, and identifies optimization opportunities across interconnected systems.
Rather than reacting after a peak occurs, the system enables anticipatory control—adjusting equipment sequencing, ramp rates, and load coordination to avoid costly demand spikes while preserving occupant comfort and operational reliability.
Scaling Across Industries and Use Cases
Wattif is extending this architecture to a broader set of facility types, all built on the same TDengine foundation:
- Large retail and mixed-use developments Facilities with complex HVAC zoning, lighting schedules, and tenant-driven loads benefit from coordinated demand shaping that reduces peak charges without impacting tenant experience.
- Fuel station portfolios with EV charging As EV chargers are added across multiple sites, intelligent load coordination prevents simultaneous peaks that would otherwise require grid infrastructure upgrades.
- Cold storage and industrial facilities High-intensity refrigeration loads with strict temperature constraints require precise control. Time-series intelligence enables demand reduction without compromising product integrity.
Across these sectors, Wattif reuses standardized equipment templates and analytics patterns, adapting optimization logic to domain-specific constraints while maintaining a consistent data architecture.
Business Impact
By combining domain expertise in energy systems with a purpose-built time-series platform, Wattif delivers measurable value to facility operators:
- Reduced peak demand charges and lower total electricity costs
- Deferred or avoided grid infrastructure upgrades
- Improved readiness for electrification initiatives
- Portfolio-level visibility without operational complexity
- A scalable foundation for advanced analytics and automation
About Wattif Technologies
Wattif Technologies is a Singapore-based energy intelligence company focused on solving the demand side of the energy equation. By leveraging real-time data and advanced time-series analytics, Wattif enables commercial and industrial operators to manage peak demand proactively—reducing costs, improving grid resilience, and accelerating the transition to a more electrified future.
