AI HVAC Thermal Storage Optimization

@LiveExperts

Hello, At Live Experts, we bring a comprehensive set of AI-powered solutions tailored perfectly for your project. For starters, regarding the intricate integration of data, our expertise in diverse protocols such as BACnet/IP and Modbus TCP/IP comes in handy, enabling smooth integration with the Johnson Controls BMS system. Our grasp on AI control optimisation will facilitate an efficient, predictive model for marginal costs analysis. Furthermore, when it comes to AI-based applications, our team excels in performing 48-hour load forecasting, optimising TES charge/discharge scheduling, as well as assessing TES degradation. Our proficiency spans across Machine Learning (ML), Deep Learning and Big Data Analysis tools, enabling us to effectively analyse large datasets to provide precise insights. In fact, we have experience with Spark and Hadoop that can significantly benefit your project. Regarding cybersecurity and network segmentation, we take client confidentiality seriously – encrypted communication and strict firewall enforcement being our norm. Based on your deployment requirement, we are adaptable – whether you prefer on-premises deployment with stronger cybersecurity infrastructure or cloud deployment which is faster and scalable. Regardless of the option you choose, our proven track record will ensure that your project is executed seamlessly while adhering to regulatory protocols. In conclusion, our broad range of skills in Thanks!

@AwaisChaudhry

Hello! I’m excited to help you build an AI-powered TES optimization that talks to your Johnson Controls BMS. I will study your 48-hour cooling load needs, dynamic charge/discharge rules, and predictive condition monitoring to cut energy use and demand charges. My approach starts with a robust data layer that securely pulls real-time and historical data from BACnet/IP and Modbus TCP/IP, then a lightweight AI/ML core that forecasts loads, computes marginal cooling costs, and optimizes schedules within a rolling horizon. I’ll deliver an Advisory mode as baseline with a clear path to Supervisory control in Phase 1, ensuring safe, auditable changes and tight OT cybersecurity per ISA/IEC 62443. I’ll design dashboards, validation tests, and an ROI model so you see the value early. 1) What is the current 48-hour forecast accuracy and how is it measured? 2) Which chiller efficiency metrics (kW/TR) exist now and are they accessible in real-time? 3) Can you share the ToU and demand charge details for precise cost modeling? 4) What level of data history is available for training the AI models? 5) How will the advisory recommendations be presented and approved in the operator workflow? 6) Do you have preferred cloud, on-prem, or hybrid deployment constraints? 7) What are your security expectations for gateway, gateway-to-AI, and RBAC? 8) What integration testing and commissioning milestones do you require? 9) How should degradation alerts be prioritized in the UX and alerting system? 10)

@irsolutions

With over a decade of experience in web and mobile development, specializing in AI, blockchain, and more, I understand the importance of enhancing energy efficiency and reducing demand charges in your Thermal Energy Storage (TES) system integrated with Johnson Controls BMS. In past projects, I have successfully implemented AI-powered solutions in sectors like FinTech, HealthTech, and eCommerce, delivering tailored products that drive results. My expertise in blockchain and Web3 projects, along with a track record of serving over 1 million users with Telegram Mini Apps, ensures seamless integration for crypto-related projects. I propose a cost-effective Hybrid Model deployment for your AI HVAC Thermal Storage Optimization project. By combining on-premises data integration with cloud-based AI processing, we can achieve a balanced cybersecurity posture, advanced ML capabilities, and operational flexibility. This approach aligns with your objectives while maintaining a strong cybersecurity boundary and ensuring rapid AI iteration for the best ROI. Let's collaborate to optimize your TES system and introduce predictive analytics that will revolutionize your energy management. Contact me to discuss further details and kickstart this transformative project.

@diektech

Hi, this is Elias from Miami. I’ve reviewed your RFP and understand you’re aiming to deploy an AI-driven TES optimization & predictive analytics platform integrated with your Johnson Controls BMS to forecast 48-hour cooling loads, minimize demand charges, and deliver advisory/supervisory control within a secure OT architecture. I’ve delivered similar energy optimization and BMS analytics solutions, including BACnet/IP integrations, load forecasting, and chiller plant efficiency optimization. This project aligns very closely with my experience. A few focused questions: Q1: Is historical BMS/TES data already stored in a historian, or should we design the storage/retention layer as part of Phase 1? Q2: For optimization constraints, do you have fixed operational limits (tank SoC bands, chiller staging rules, comfort thresholds)? Q3: Regarding Supervisory Mode, which points are approved for writeback (mode requests, setpoints, schedules)? Happy to discuss architecture options (hybrid vs on-prem), expected ROI, and deployment strategy. Looking forward to your thoughts. Regards.

@Herculesz

Hi I can deliver an AI-powered TES optimization and predictive analytics platform that solves your core technical challenge: integrating Johnson Controls BMS data (via BACnet/IP and Modbus TCP/IP) into a secure OT-segmented architecture while applying advanced forecasting and charge/discharge optimization. I’ve built ML-driven load forecasting models (weather + historical TES behavior), rolling-horizon optimizers for chiller/TES coordination, and industrial-grade analytics that maintain ISA/IEC 62443 compliance. My approach includes a fully documented integration layer, secure gateway design, thermal-loss monitoring, stratification analytics, and marginal cooling cost modeling. The optimization engine will run in advisory mode first—aligned with your control and cybersecurity requirements—while the architecture remains upgrade-ready for supervisory control. I can propose on-prem, cloud, or hybrid deployment, with hybrid typically offering the strongest ROI and the fastest operational ramp-up. You will receive full system documentation, commissioning reports, architecture diagrams, and a validated forecasting/optimization performance package. Thanks, Hercules

@Feriver

Hello, I understand that you are seeking an experienced specialist to design and implement an AI-powered Thermal Energy Storage optimization platform integrated with your existing Johnson Controls BMS. My approach will focus on secure BACnet/IP and Modbus TCP/IP integration, structured data modeling, and deployment within a segmented OT architecture aligned with ISA/IEC 62443 principles. I will develop a 48-hour weather-driven cooling load forecast, implement rolling-horizon TES charge/discharge optimization using real-time kW/TR and tariff structures, and deliver marginal cost analytics along with thermal degradation and stratification health monitoring. The solution will begin in Advisory Mode with a Hybrid deployment model to ensure cybersecurity, scalability, and measurable ROI before supervisory expansion. I have strong experience in AI optimization, HVAC analytics, industrial integration, and predictive modeling, ensuring a secure and performance-driven implementation. Q1: What volume and granularity of historical BMS data is available for model training? Q2: Is supervisory writeback limited to predefined objects within Johnson Controls or configurable? Thanks, Asif

@juanjosec63

Hello, I’m excited about the opportunity to contribute to your project. With my experience in AI-driven optimization, industrial integrations over BACnet/IP and Modbus TCP/IP, and secure OT/IT architectures aligned with ISA/IEC 62443 principles, I can design and commission a hybrid-deployed TES optimization platform integrated cleanly with your Johnson Controls BMS in advisory mode, with supervisory-ready architecture from day one. I’ll architect a secure data integration layer in an OT DMZ, implement 48-hour weather-informed load forecasting with measurable MAPE targets, deploy a rolling-horizon TES optimization engine tied to kW/TR efficiency and tariff signals, and provide condition monitoring analytics with degradation detection while preserving BMS authority and fail-safe ToU fallback. You can expect a layered system architecture, cybersecurity design documentation, ROI modeling with demand charge reduction projections, structured commissioning, and operator-ready dashboards and training materials that align with your Phase 1 strategic objectives. Best regards, Juan

@trm66614

Hi there, Good morning I am Talha. I have read you project details i saw you need help with Data Science, AI Model Development, Machine Learning (ML), Statistical Analysis, Python, Matlab and Mathematica, Electrical Engineering, Predictive Analytics, AI (Artificial Intelligence) HW/SW and Mathematics I am excited to submit my proposal for your project, which focuses on a comprehensive project plan. To begin, we will thoroughly understand your project's objectives and requirements, ensuring alignment on scope and goals. We will provide a clear and realistic project timeline with manageable milestones to ensure timely completion Please note that the initial bid is an estimate, and the final quote will be provided after a thorough discussion of the project requirements or upon reviewing any detailed documentation you can share. Could you please share any available detailed documentation? I'm also open to further discussions to explore specific aspects of the project. Thanks Regards. Talha Ramzan

@OutsourceMan

⭐⭐⭐⭐⭐ We at CnELIndia, in collaboration with Raman Ladhani, can successfully deliver your AI-powered TES optimization project by leveraging our deep expertise in Johnson Controls BMS integration, BACnet/IP and Modbus TCP/IP protocols, and advanced AI/ML predictive analytics. Our approach will start with designing a secure, segregated data integration layer, mapping TES instrumentation, and validating communication with the BMS. Using weather-driven modeling and real-time chiller efficiency metrics, we will develop a 48-hour cooling load forecast and a dynamic optimization engine for advisory charge/discharge schedules, ensuring demand charge reduction. Raman Ladhani will guide AI model development, predictive condition monitoring, and supervisory control readiness, while CnELIndia ensures hybrid deployment, cybersecurity compliance, operator dashboards, and ROI validation, enabling phased adoption from advisory to supervisory control seamlessly.

@nabeelraza1993

TES optimization projects succeed when AI logic, BMS integration, and OT cybersecurity are designed together instead of as separate workstreams. Well, what I can do for you as an electronics engineer is deliver a structured technical proposal for this RFP covering Johnson Controls BMS integration via BACnet/IP and Modbus TCP/IP, 48 hour forecasting, TES charge discharge optimization, condition monitoring, and a secure advisory first architecture with a clear path to supervisory control. In fact, I designed a high power 10000 watt LED dimmer for a UK client and I also built an 8 bit SAR ADC logic in Cadence, and I have done many masters and PhD level research documentations, academic manuscripts, and technical reports for UK and USA clients, so I can present complex technical systems in a clear, decision ready format.

@vorasiddh4it

Hello alamusa, I’m an AI Energy Optimization & Industrial Analytics Engineer with hands-on experience building predictive optimization platforms for HVAC, TES, and BMS-integrated energy systems using Python-based AI pipelines aligned with industrial software services standards. I have implemented forecasting + optimization engines integrated with BACnet/Modbus environments, and I can show demo code, architecture diagrams, and analytics workflows before we finalize the deal. ✅ What I Will Deliver • AI-driven TES optimization + 48-hour cooling load forecasting • Johnson Controls BMS integration (BACnet/IP & Modbus TCP/IP) • Marginal cost of cooling & demand-charge optimization • Advisory-mode dashboard (Supervisory-ready architecture) • Cybersecure Hybrid/On-Prem deployment design • Complete documentation, validation & commissioning support ? Techniques & Architecture Data ingestion gateway + tag mapping (OT-safe architecture) ML forecasting (LSTM/XGBoost + weather-driven models) Rolling horizon optimization (MILP/Reinforcement Learning) TES degradation & stratification analytics Secure OT DMZ deployment (ISA/IEC 62443 aligned) BACnet writeback advisory signals with fail-safe logic Python stack: Pandas, PyTorch/Sklearn, FastAPI, Timeseries DB ? Relevant Projects • Smart Predictive Charge/Discharge Optimizer • IndustrialEnergy Analytics Platform I follow advisory-first deployment ensuring cybersecurity, ROI validation, and smooth supervisory upgrade.

@maxscender

I can help you. To achieve a 10% MAPE in Saudi Arabia’s extreme climate, the forecasting model must be physics-informed; purely data-driven models fail during peak-summer anomalies that deviate from historical averages. The project requires a multi-model architecture. We can use Temporal Fusion Transformer (or LSTM with Attention) + PINN Chiller Efficiency Model (PINN) + TES Tank Dynamics Model (PINN) + Optimization Engine

@Engumer480

As a highly-skilled and experienced AI developer with expert proficiency in Python and Machine Learning (ML), I have the knowledge and know-how to deliver exceptional results for your AI HVAC Thermal Storage Optimization project. Combining the powers of AI, ML, and my technical capabilities with BACnet/IP and Modbus TCP/IP integration, I can design and implement the secure gateway architecture for extracting real-time and historical data that aligns perfectly with your Lastly, I pride myself on adhering to the highest cybersecurity standards like the ISA/IEC 62443 principles and corporate IT policies. I'm well-versed in implementing strict network segmentation controls to ensure the secure flow of data between your Johnson Controls BMS platform and AI engine without exposure to potential cyberattacks. Choose me for a seamless deployment whether you opt for an on-premises model guaranteeing the strongest security or a cloud-based model ensuring scalability and faster deployment. Let's optimize your HVAC system like never before!

@cesur135

With over several years of extensive experience in industrial automation in both water and wastewater treatment plants, I bring a wealth of knowledge that perfectly aligns with the AI HVAC Thermal Storage Optimization project you're looking to undertake. A particular highlight of my expertise was developing robust automation solutions for monitoring and controlling complex plant systems using Siemens TIA Portal, Simatic Manager, and WinCC SCADA programs - all pivotal technologies in your project's Johnson Controls BMS integration. Moreover, as a detail-oriented professional, I not only have an excellent grasp of AI algorithms and analytics critical to your 48-hour cooling load forecasting but also understand the core principles that drive energy efficiency such as dynamic charge/discharge optimization and utility tariff structure. My skills can further be leveraged in implementing appropriate cybersecurity measures (ISA/IEC 62443) to ensure data flow of your BMS via the secure gateway to my proposed AI engine without compromising your system's safety.

@techsty2014

Hello, I am excited to propose an AI-powered optimization and predictive analytics platform for your TES system, fully integrated with the Johnson Controls BMS. My approach will encompass 48-hour cooling load forecasting and dynamic charge/discharge optimization, while ensuring seamless integration via BACnet/IP and Modbus TCP/IP. I will design a secure data integration layer, complete with a detailed tag mapping and data model. My AI & analytics layer will focus on demand charge reduction, thermal loss trend analysis, and degradation detection alerts. The solution will initially operate in Advisory Mode with a hybrid deployment, providing scalability and optimal cybersecurity. Questions: • Do you have a preference for the deployment model, or are you leaning towards the hybrid option? • What is your expected timeline for the implementation and commissioning? I am committed to delivering a solution that enhances energy efficiency and aligns with your strategic goals. Let's discuss how we can achieve this together. Thanks and best regards, Faizan

@AZTURK1

Hi there, I understand you need an AI-driven TES optimization and predictive analytics platform integrated with Johnson Controls BMS; I’m confident I can deliver a secure, production-ready Phase 1 (Advisory + Hybrid) that validates ROI and prepares for supervisory control. - Points list, integration architecture diagram, communication validation report - 48-hour cooling load forecasting (weather-driven) + forecast MAPE reporting - TES optimization engine: marginal cost, rolling-horizon charge/discharge schedules - Condition monitoring, dashboards, operator approval workflow Skills: ✅ Matlab and Mathematica ✅ Python ✅ Rolling horizon optimization ✅ BACnet/IP and Modbus TCP/IP integration ✅ ISA/IEC 62443 compliance ✅ Hybrid cloud/on‑prem deployment Certificates: ✅ Microsoft® Certified: MCSA | MCSE | MCT ✅ cPanel® & WHM Certified CWSA-2 I can start immediately and deliver Phase 1 architecture, integration, and models within 10-12 weeks. Do you have an existing points list or data historian (timestamped BMS tags) and can you share sample tags and a week of historical data for model training (anonymized if needed)? Best regards,

@mughiraaa

Hi, we’ve reviewed your RFP. our team has direct experience building AI-driven optimization platforms for energy systems, including integration with Johnson Controls BMS via BACnet/IP and Modbus TCP. we can deliver the full stack from data integration and load forecasting to TES optimization and condition monitoring with a strong focus on cybersecurity and OT compliance. We’ve done similar projects in HVAC and energy storage. happy to propose a phased approach starting with Advisory mode and a Hybrid deployment model for balanced cost, security, and scalability. Let’s have a detailed discussion, as it will help me give you a complete plan, including a timeline and estimated budget. I will share our portfolio in the chat. Mughiraa

@krisk63

Hi there, I'm Kristopher Kramer from McKinney, Texas. I’ve worked on similar projects before, and as a senior full-stack and AI engineer, I have the proven experience needed to deliver this successfully, so I have strong experience in Python, Predictive Analytics, Mathematics, AI Model Development, Electrical Engineering, Data Science, AI (Artificial Intelligence) HW/SW, Machine Learning (ML), Statistical Analysis and Matlab and Mathematica. I’m available to start right away and happy to discuss the project details anytime. Looking forward to speaking with you soon. Best regards, Kristopher Kramer

@revival786

Greetings! I’m a top-rated freelancer with 16+ years of experience and a portfolio of 750+ satisfied clients. I specialize in delivering high-quality, professional AI HVAC thermal storage optimization services tailored to your unique needs. Please feel free to message me to discuss your project and review my portfolio. I’d love to help bring your ideas to life! Looking forward to collaborating with you! Best regards, Revival