AI HVAC Thermal Storage Optimization
Budget: $3,000 – $5,000 USD
REQUEST FOR PROPOSAL (RFP)
AI-Powered Thermal Energy Storage (TES) Optimization & Predictive Analytics
Integrated with Johnson Controls BMS Platform
1. Introduction
[Client Name] invites qualified vendors to submit proposals for the design, implementation, and commissioning of an AI-powered optimization and predictive analytics platform for a Thermal Energy Storage (TES) system integrated with an existing Johnson Controls Building Management System (BMS).
The objective is to enhance energy efficiency, reduce demand charges, enable dynamic charge/discharge optimization, and introduce predictive condition monitoring.
2. Project Objectives
The proposed solution shall:
Perform 48-hour cooling load forecasting
Optimize TES charge/discharge scheduling using:
Real-time chiller efficiency (kW/TR)
Utility tariff structure (ToU + demand charges)
Provide marginal cost of cooling analytics
Monitor TES degradation (thermal losses)
Integrate with Johnson Controls BMS via:
BACnet/IP
Modbus TCP/IP
Provide:
Advisory control (minimum requirement)
Supervisory control (optional Phase 1 scope)
3. Existing Infrastructure
BMS Platform: Johnson Controls
Available Integration Protocols:
BACnet/IP
Modbus TCP/IP
TES instrumentation:
Tank temperature stratification sensors
Flow meters
Tank level sensors
Chiller plant efficiency metrics (kW/TR)
Utility meter interface
4. Scope of Work
4.1 Data Integration Layer
Vendor shall:
Integrate with Johnson Controls BMS via BACnet/IP and/or Modbus TCP/IP
Extract real-time and historical data
Implement secure gateway architecture
Design tag mapping and data model
Deliverables:
Points list
Integration architecture diagram
Communication validation report
4.2 AI & Analytics Layer
A. Load Forecasting
48-hour rolling cooling load forecast
Weather-driven modeling
Forecast accuracy reporting (MAPE)
B. TES Optimization Engine
Dynamic marginal cost calculation
Charge/discharge schedule optimization
Demand charge reduction logic
Rolling horizon optimization (hourly recalculation)
C. Condition Monitoring
Thermal loss trend analysis
Stratification health monitoring
Degradation detection alerts
4.3 Control Mode Requirements
Minimum Requirement – Advisory Mode
AI generates recommended:
Charge/discharge schedule
Setpoints
Mode change timing
Visualization dashboard
Operator approval workflow
Optional Phase 1 – Supervisory Control
AI writes:
Mode request signals
Charge/discharge setpoints
Johnson BMS executes final control logic
PLC/BMS safety interlocks remain authoritative
Fail-safe fallback to standard ToU schedule
Direct control of field devices by AI is NOT permitted.
5. Cybersecurity & Network Segmentation Requirements
The solution must comply with OT cybersecurity best practices.
5.1 Network Segmentation
AI platform shall reside in:
Dedicated OT DMZ
Or segregated application server network
No direct access from internet to BMS network
Strict firewall rule enforcement
Role-based access control (RBAC)
5.2 Data Flow Architecture
Allowed pattern:
BMS → Gateway → AI Engine → BMS (supervisory writeback)
Not allowed:
Direct AI-to-field device communication
5.3 Compliance
Vendor shall comply with:
ISA/IEC 62443 principles
Corporate IT cybersecurity policies
Encrypted communication (TLS where applicable)
6. Deployment Model Options
Vendor shall propose costed options for:
Option A – On-Premises Deployment
AI engine hosted on local server
Located in data center or OT DMZ
No cloud dependency
Advantages:
Strongest cybersecurity posture
No internet dependency
Preferred for critical infrastructure
Challenges:
Higher CAPEX
Hardware procurement
Maintenance responsibility on-site
Option B – Cloud Deployment
AI hosted on secure cloud platform
BMS data forwarded via secure gateway
Weather and tariff APIs integrated natively
Advantages:
Lower upfront infrastructure cost
Faster deployment
Scalable
Easier ML model updates
Challenges:
Requires outbound OT connectivity
Higher cybersecurity governance
Ongoing subscription OPEX
Option C – Hybrid Model (Recommended)
Data historian + integration on-prem
AI processing in cloud
Secure outbound-only data push
Supervisory writeback via secure tunnel
Advantages:
Balanced cybersecurity
Lower CAPEX than full on-prem
Advanced ML capability
Operational flexibility
7. Cost Effectiveness Analysis (General Guidance)
Model CAPEX OPEX Cyber Complexity Scalability Recommendation
On-Prem High Low Low Medium Best for high-security sites
Cloud Low Medium Medium/High High Best for fast deployment
Hybrid Medium Medium Controlled High Most balanced
Most Cost-Effective in Early Phase:
Hybrid model typically delivers the best ROI for Phase 1 because:
Avoids heavy hardware investment
Enables rapid AI iteration
Maintains OT security boundary
8. Performance Requirements
Vendor shall specify:
Forecast accuracy target (≤10% MAPE preferred)
Optimization ROI projection
Expected demand charge reduction %
System latency
Failover recovery time
9. Deliverables
Detailed system architecture (layered model)
Cybersecurity architecture diagram
Integration design with Johnson Controls BMS
AI model documentation
Testing & validation plan
Commissioning report
Operator training materials
ROI model
10. Vendor Qualification Requirements
Vendor must demonstrate:
Experience with Johnson Controls BMS integration
Experience with BACnet/IP and Modbus TCP/IP
Proven AI/ML optimization projects
Industrial cybersecurity implementation experience
References in HVAC or TES optimization projects
11. Proposal Submission Requirements
Proposal shall include:
Technical proposal
Control mode clarification (Advisory vs Supervisory)
Deployment architecture
Cybersecurity design
Detailed cost breakdown:
Software
Integration
Hardware (if on-prem)
Annual maintenance
Implementation timeline
Project governance structure
Strategic Recommendation (Executive Perspective)
For Phase 1:
Start with Advisory Mode + Hybrid Deployment
Design architecture ready for Supervisory upgrade
Implement strong cybersecurity segmentation from Day 1
Validate forecast & optimization ROI before enabling supervisory control
AI-Powered Thermal Energy Storage (TES) Optimization & Predictive Analytics
Integrated with Johnson Controls BMS Platform
1. Introduction
[Client Name] invites qualified vendors to submit proposals for the design, implementation, and commissioning of an AI-powered optimization and predictive analytics platform for a Thermal Energy Storage (TES) system integrated with an existing Johnson Controls Building Management System (BMS).
The objective is to enhance energy efficiency, reduce demand charges, enable dynamic charge/discharge optimization, and introduce predictive condition monitoring.
2. Project Objectives
The proposed solution shall:
Perform 48-hour cooling load forecasting
Optimize TES charge/discharge scheduling using:
Real-time chiller efficiency (kW/TR)
Utility tariff structure (ToU + demand charges)
Provide marginal cost of cooling analytics
Monitor TES degradation (thermal losses)
Integrate with Johnson Controls BMS via:
BACnet/IP
Modbus TCP/IP
Provide:
Advisory control (minimum requirement)
Supervisory control (optional Phase 1 scope)
3. Existing Infrastructure
BMS Platform: Johnson Controls
Available Integration Protocols:
BACnet/IP
Modbus TCP/IP
TES instrumentation:
Tank temperature stratification sensors
Flow meters
Tank level sensors
Chiller plant efficiency metrics (kW/TR)
Utility meter interface
4. Scope of Work
4.1 Data Integration Layer
Vendor shall:
Integrate with Johnson Controls BMS via BACnet/IP and/or Modbus TCP/IP
Extract real-time and historical data
Implement secure gateway architecture
Design tag mapping and data model
Deliverables:
Points list
Integration architecture diagram
Communication validation report
4.2 AI & Analytics Layer
A. Load Forecasting
48-hour rolling cooling load forecast
Weather-driven modeling
Forecast accuracy reporting (MAPE)
B. TES Optimization Engine
Dynamic marginal cost calculation
Charge/discharge schedule optimization
Demand charge reduction logic
Rolling horizon optimization (hourly recalculation)
C. Condition Monitoring
Thermal loss trend analysis
Stratification health monitoring
Degradation detection alerts
4.3 Control Mode Requirements
Minimum Requirement – Advisory Mode
AI generates recommended:
Charge/discharge schedule
Setpoints
Mode change timing
Visualization dashboard
Operator approval workflow
Optional Phase 1 – Supervisory Control
AI writes:
Mode request signals
Charge/discharge setpoints
Johnson BMS executes final control logic
PLC/BMS safety interlocks remain authoritative
Fail-safe fallback to standard ToU schedule
Direct control of field devices by AI is NOT permitted.
5. Cybersecurity & Network Segmentation Requirements
The solution must comply with OT cybersecurity best practices.
5.1 Network Segmentation
AI platform shall reside in:
Dedicated OT DMZ
Or segregated application server network
No direct access from internet to BMS network
Strict firewall rule enforcement
Role-based access control (RBAC)
5.2 Data Flow Architecture
Allowed pattern:
BMS → Gateway → AI Engine → BMS (supervisory writeback)
Not allowed:
Direct AI-to-field device communication
5.3 Compliance
Vendor shall comply with:
ISA/IEC 62443 principles
Corporate IT cybersecurity policies
Encrypted communication (TLS where applicable)
6. Deployment Model Options
Vendor shall propose costed options for:
Option A – On-Premises Deployment
AI engine hosted on local server
Located in data center or OT DMZ
No cloud dependency
Advantages:
Strongest cybersecurity posture
No internet dependency
Preferred for critical infrastructure
Challenges:
Higher CAPEX
Hardware procurement
Maintenance responsibility on-site
Option B – Cloud Deployment
AI hosted on secure cloud platform
BMS data forwarded via secure gateway
Weather and tariff APIs integrated natively
Advantages:
Lower upfront infrastructure cost
Faster deployment
Scalable
Easier ML model updates
Challenges:
Requires outbound OT connectivity
Higher cybersecurity governance
Ongoing subscription OPEX
Option C – Hybrid Model (Recommended)
Data historian + integration on-prem
AI processing in cloud
Secure outbound-only data push
Supervisory writeback via secure tunnel
Advantages:
Balanced cybersecurity
Lower CAPEX than full on-prem
Advanced ML capability
Operational flexibility
7. Cost Effectiveness Analysis (General Guidance)
Model CAPEX OPEX Cyber Complexity Scalability Recommendation
On-Prem High Low Low Medium Best for high-security sites
Cloud Low Medium Medium/High High Best for fast deployment
Hybrid Medium Medium Controlled High Most balanced
Most Cost-Effective in Early Phase:
Hybrid model typically delivers the best ROI for Phase 1 because:
Avoids heavy hardware investment
Enables rapid AI iteration
Maintains OT security boundary
8. Performance Requirements
Vendor shall specify:
Forecast accuracy target (≤10% MAPE preferred)
Optimization ROI projection
Expected demand charge reduction %
System latency
Failover recovery time
9. Deliverables
Detailed system architecture (layered model)
Cybersecurity architecture diagram
Integration design with Johnson Controls BMS
AI model documentation
Testing & validation plan
Commissioning report
Operator training materials
ROI model
10. Vendor Qualification Requirements
Vendor must demonstrate:
Experience with Johnson Controls BMS integration
Experience with BACnet/IP and Modbus TCP/IP
Proven AI/ML optimization projects
Industrial cybersecurity implementation experience
References in HVAC or TES optimization projects
11. Proposal Submission Requirements
Proposal shall include:
Technical proposal
Control mode clarification (Advisory vs Supervisory)
Deployment architecture
Cybersecurity design
Detailed cost breakdown:
Software
Integration
Hardware (if on-prem)
Annual maintenance
Implementation timeline
Project governance structure
Strategic Recommendation (Executive Perspective)
For Phase 1:
Start with Advisory Mode + Hybrid Deployment
Design architecture ready for Supervisory upgrade
Implement strong cybersecurity segmentation from Day 1
Validate forecast & optimization ROI before enabling supervisory control