IHCL Vivanta Hotel Projects Group

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Project Story
Step 1 in the HVAC AI Energising and Automation across the Taj Vivanti Campus of Hotels

Digital Twin AI HVAC Solutions in the Hospitality Sector: A Case Study of the IHCL Vivanta Taj

The IHCL Vivanta Taj hotel in Delhi faced several challenges with its HVAC system, resulting in suboptimal performance, energy inefficiencies, and guest discomfort. These challenges included:
Inconsistent Temperature: The HVAC system struggled to maintain consistent temperatures throughout the hotel, leading to hot and cold spots in guest rooms and public areas.
High Energy Consumption: The inefficient HVAC system consumed excessive energy, resulting in increased operational costs for the hotel.
Equipment Breakdowns: Frequent breakdowns of HVAC equipment caused disruptions to hotel operations and compromised guest satisfaction.
Solution:
To address these challenges, the hotel partnered with a leading technology provider specializing in digital twin AI HVAC solutions. The solution involved the following key components:
Digital Twin Model: A digital twin model of the hotel's HVAC system was created using real-time data from sensors and IoT devices installed throughout the building. This model accurately reflected the physical system, enabling comprehensive monitoring and analysis.
AI-Powered Optimization: Advanced AI algorithms were integrated into the digital twin model, allowing the system to learn from historical data and make intelligent decisions to optimize HVAC performance.
Remote Monitoring and Control: The hotel's staff gained remote access to the digital twin model, enabling them to monitor the HVAC system in real-time and make adjustments as needed.

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IHCL Vivanta Hotel Projects Group

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Project:
VCS: Stage: Developer: View ↗
Source: LIVE
Tier:
CCY:
REC units: EF
Outlook: Haircut %
EUA
GEO
N-GEO
GO
I-REC
V-REC
EEC
CCUS
EV

REC Assumptions

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Each year's tranche valued at today's price (flat) vs the forecast price of that year.

Voluntary: REC (Med-Low)

Voluntary: EEC / Removal (Med-High)

Compliance: EU ETS

REC Projects & SDG Synergies

  • SDG 7: More renewables displacing fossil power.
  • SDG 13: Verified reductions, market signal for clean power.
  • SDG 11: Cleaner air & resilient grids.

EEC Assumptions

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Each year's tranche valued at today's price (flat) vs the forecast price of that year.

Voluntary: EEC / Removal (Med-High)

Compliance: EU ETS

EEC Projects & SDG Synergies

  • SDG 9: Industrial modernisation, AI/dMRV.
  • SDG 12: Resource efficiency & demand-side abatement.
  • SDG 7: "First fuel" energy efficiency.

CCUS Assumptions VM0049 · Industrial CCS

Pricing: bilateral offtake $30–80/t · 45Q floor $60–85/t (US) · no liquid spot market

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Each year's tranche valued at today's price (flat) vs the forecast price of that year.

CCUS / Industrial CCS (VM0049)

Compliance: EU ETS

CCUS Projects & SDG Synergies

  • SDG 9: Industrial innovation — point-source capture at cement, steel, refining facilities.
  • SDG 13: Hard-to-abate sector decarbonisation; permanent geological storage.
  • SDG 11: Cleaner industrial zones, reduced local air pollutants alongside CO₂.

VM0049 (Verra, June 2024): modular CCS methodology covering geological storage, CO₂ utilisation, and transport pathways. 45Q tax credit provides a $60–85/t USD policy floor in the US.

EV Fleet Assumptions VM0038 · Transport Electrification

Pricing: $2–5/t spot VCM · $6–10/t premium bilateral · CORSIA-eligible · 46.4% CAGR sector

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Investor Inputs (EV Fleet)

Total @ Today price
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Per-Year Valuation — Today vs Forecast

Each year's tranche valued at today's price (flat) vs the forecast price of that year.

EV Fleet Credits (VM0038)

Voluntary: REC (Med-Low)

EV Fleet Projects & SDG Synergies

  • SDG 7: Clean transport powered by renewable electricity.
  • SDG 11: Sustainable cities — reduced urban air pollution, quieter streets.
  • SDG 13: Transport decarbonisation; CORSIA-aligned fleet credits.

VM0038 (Verra): telematics-verified VKT reduction, smart charging, baseline grid displacement. Transport credits are the fastest-growing VCM segment (46.4% CAGR). DePIN sensor networks add verifiability premium.

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Venues: ICE (EUA), CME (GEO / N-GEO), EEX (GO); I-REC registry (regional indications); EEC modeled.

FX: ECB euro reference (cached daily).

Methodology

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REC pricing: V-REC/GO/I-REC converted to selected units (MWh or tCO₂e-eq via EF) and currency; tier factor applies to REC/EEC classes.

Financials: All internal calculations in the selected currency. Annual net flow = (credit revenue × (1−haircut) + optional savings) − O&M. KPIs: IRR (Newton-Raphson), NPV and discounted payback at your rate.

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Status Updates

Results:
The implementation of digital twin AI HVAC solutions at the IHCL Vivanta Taj hotel resulted in significant benefits, including:
Improved Guest Comfort: The hotel achieved consistent and personalized temperature control in guest rooms and public areas, leading to enhanced guest comfort and satisfaction.
Energy Savings: The AI-powered optimization algorithms reduced energy consumption by up to 20%, resulting in substantial cost savings for the hotel.
Reduced Equipment Breakdowns: Predictive analytics and condition monitoring capabilities of the digital twin model helped identify potential equipment issues early on, preventing breakdowns and ensuring uninterrupted operations.
Enhanced Operational Efficiency: The hotel staff experienced improved operational efficiency due to remote monitoring and control capabilities, enabling them to respond promptly to any issues and optimize the HVAC system's performance.

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