Tongjie Energy is a vertical technology provider for smart energy. Once the plant's optimisation logic is abstracted into DCCS and ECMS, it travels to dehumidification, grain storage, heat pumps and solar thermal — one core, six domains.
System-level optimisation across chillers, boilers, pumps and towers — up to EER 6.0+ in delivered projects.
View solution 02 / DEHUMIDIFICATIONDesiccant dehumidification tuned to process demand; ~20% lower dehumidification energy measured on site.
View solution 03 / GRAIN STORAGEGrain-condition monitoring linked to temperature and humidity control — aeration only when it helps.
View solution 04 / HEAT PUMP & PVTMetering and control strategies for solar thermal and heat pumps, so renewable output can be optimised.
View solution 05 / VRVCentralised management of VRV and split units — zone-level control, visible energy, no fixed setpoints.
View solution 06 / ROADMAPRoadmap scenarios built on the same modelling method: diagnose and validate before investing.
View roadmapWe do not build equipment. We build the intelligence behind it. One common core scales from group-wide orchestration, to real-time plant control, down to a single process loop — decided by compute and system scope, not by a different product philosophy.
Pulls scattered sites, subsystems and operating data into one view — then orchestrates strategy across them.
System-level control for the plant and air side. Full DCCS plus edge ECMS — it keeps running when the cloud is unreachable.
One process, one terminal, one closed loop: lightweight DCCS (CSV lookup models) and a trimmed ECMS that runs on site.
Three parts: driver configuration (BACnet, Modbus, CSV), model configuration (loading and running AI models) and strategy configuration (optimisation and dispatch). The edge runs pkl models; the device tier trades model complexity for plug-and-play CSV models.
Process screens, dashboards, trend charts, model and strategy management, alarm loops and large-screen views. The cloud edition carries many sites; the edge edition renders richer local screens; the device edition keeps only what is needed.
No client becomes a test bed. Every strategy is rehearsed on a digital twin before it touches real equipment. That method is why chiller-plant algorithms also work in dehumidification, grain storage and PVT.
Site walk-down plus portable efficiency testing to quantify the real performance and the available headroom.
Load, energy and equipment performance models built from equipment characteristics and historical operation.
Expert rules plus optimisation to define staging, frequency and temperature set-points.
Digital twin rehearses the strategy; abnormal conditions are caught in simulation, not on site.
Progressive cut-over, manual fallback and one-click rollback. The plant never stops during the works.
Operating data feeds the model; strategy iterates with season and load. Savings stay measurable.
The core algorithms are equipment-agnostic and jointly developed with research teams at Shanghai Jiao Tong University and Tongji University. One DCCS + ECMS base covers chiller plants, dehumidification, grain storage, heat pumps and PVT. We do not make equipment — we make it smarter.
Verified to GB/T 31349-2014 using either a similar-day comparison (measures off on one day, on for a comparable day) or a historical baseline comparison (same period before and after retrofit). These are delivered projects, not estimates.
Two BAS merged into one, pumps retrofitted with VFDs and four AI strategies commissioned across an 112,000 m² terminal.
Full VFD coverage on towers and pumps with self-learning staging: a similar summer day fell from 19,753 to 15,202 kWh.
A 250,000 m² campus plant on one platform with AI strategy; plant EER holds above 5.0 all season.
Existing BAS integrated and an AI engine added — 20% of plant energy removed without compromising process conditions.
Source: project energy-saving test reports and handover documents. Savings come from similar-day tests; actual annual savings vary with weather, load and operation, and are governed by the project acceptance documents.
Shanghai Pudong Airport Satellite Terminal · Ningbo Lishe Airport · Raffles City Hangzhou · ByteDance Chengdu office · Beijing Yuecheng Plaza · Beijing Zhonghai International Centre · Senlan Tech Park · Yangjie Electronics · Shanghai World Expo Museum · Hyatt Regency Qingpu · Jing'an Joy City · Qingdao High-Speed Rail Station · Shanxi Zhongyang People's Hospital · Pan'an People's Hospital · Jiangning Hospital · Red Star Macalline · Jiande Yaohan
The optimisation logic of a chiller plant also applies wherever temperature and humidity must be controlled. Proven scenarios ship first; planned ones follow the same method.
System-level optimisation of chillers, boilers, primary/secondary pumps, cooling towers and terminals — primary to tertiary networks.
Proven 02Precise humidity control for desiccant rotors and coating lines, following process demand instead of fixed parameters.
Delivered 03Condition monitoring with linked temperature and humidity control; cooling units run on what the silo actually needs.
Delivered 04Operation strategy and metering for solar-thermal and heat-pump systems, so renewable output can be measured and optimised.
Pilot 05Centralised management and operating optimisation for multi-split systems — beyond scattered remotes and opaque energy use.
Proven 06Planned scenarios using the same modelling and simulation method; start with a diagnosis and feasibility check.
RoadmapThe EnergyBox cloud platform continues as the EnerBlue SaaS entry point: web and mobile access to every site, local plus cloud backup, and a full audit trail of AI strategy execution and verified savings.
Screenshot of the live platform. The EnerBlue AI large-model entry is reserved and will open here.
Send twelve months of operating data and the plant drawings. Our portable efficiency kit delivers a measured baseline, a verified saving range and a retrofit list.
