High-rise urban development project

This case study examines a large-scale urban development project focused on optimizing HVAC (Heating, Ventilation, and Air Conditioning) and domestic hot water (DHW) systems to achieve significant cost savings and environmental benefits. The project team sought to balance initial capital expenditure with long-term operational efficiency while minimizing carbon emissions. By leveraging advanced energy modeling tools and Deep Energy AI, the project compared multiple HVAC system designs to select the most efficient solution. The findings provided valuable insights for decision-makers, guiding both system selection and the procurement of an embedded network operator to manage energy distribution effectively.

Challenges

The project faced several key challenges, particularly around optimizing energy efficiency and minimizing costs for both HVAC (Heating, Ventilation, and Air Conditioning) and domestic hot water (DHW) systems. The project had to balance between lower initial capital costs and the long-term operational costs of these systems. Another critical challenge was selecting an embedded network operator (ENO) capable of handling large-scale energy management while ensuring compliance with carbon reporting requirements. Acoustic concerns from the plant room and lifecycle replacement costs of the equipment were also significant hurdles to overcome.

Concept

The concept was to evaluate and compare two HVAC system designs:

• The as-designed system, which used Daikin/Mitsubishi VRV/VRF air-sourced heat pumps and air-sourced DHW plant.
• The proposed system, which involved a heat-exchanger (Heat-Ex) HVAC system with chiller/boiler and water-sourced heat pumps.

By conducting physics-based modeling of the site, the team aimed to simulate yearly energy consumption and assess the cost-effectiveness of these systems. The model incorporated detailed data such as building fabric, weather conditions, and occupancy schedules, which were analyzed using Deep Energy AI software. This software helped generate financial projections and compare embedded network tariff options.

Purpose with goals

The primary purpose of this study was twofold:

• To inform the HVAC system selection for the project.
• To provide guidance on selecting the appropriate embedded network operator for the site.

The key goals of the project were:

• Achieving operational cost savings through a more energy-efficient HVAC system.
• Maximizing financial returns via optimized embedded network tariffs.
• Balancing capital expenditure with long-term lifecycle costs.
• Ensuring sustainability by reducing carbon emissions and energy consumption.

Modelling scenarios

The team used the Deep Energy AI software to model and compare two primary scenarios:

1. As-Designed VRV/VRF HVAC System: This scenario involved air-sourced heat pumps and DHW systems. While it had lower initial capital costs, the long-term operational inefficiencies made it less suitable for larger buildings.
2. Proposed Heat-Ex HVAC System: This system included water-sourced heat pumps, chillers, and boilers. While the initial capital expenditure was higher, the long-term operational costs were significantly lower due to higher energy efficiency. This scenario also considered lifecycle replacement costs, acoustic improvements, and reduced carbon emissions.

Results

The Heat-Ex HVAC system provided a $460,000 reduction in initial capital costs and an estimated operational savings of $1,025,225 over 10 years, lowering tenants' annual power bills by $264 on average. It also reduced annual electricity consumption from 2.45 GWh to 2.0 GWh, improving energy efficiency while maintaining or increasing embedded network tariffs. Additionally, the system cut carbon emissions by 939 tonnes of CO2 over 10 years and offered greater long-term viability, with a longer lifecycle for key components, reducing future replacement costs.

10-years NPV scenario camparison in Deep Energy AI

Forward projection of 50-year cashflows in Deep Energy AI

Optimal scenario results in Deep Energy AI

Why deep energy AI was critical

The proposed Heat-Ex HVAC system delivered significant financial and environmental advantages, and these results would have been difficult to achieve without the use of the Deep Energy AI software. Here's why:

• Precise Cost Savings Analysis: Deep Energy AI allowed the team to simulate the energy consumption of different HVAC systems under real-world conditions, using hourly interval data based on building fabric, weather, and occupancy. This precise modeling enabled the team to identify a $460,000 reduction in initial capital costs by opting for the Heat-Ex system over the as-designed VRV/VRF system. Additionally, it projected operational savings of $1,025,225 over 10 years\u2014insight that would be hard to gather without the software's ability to handle large datasets and complex energy models.
• Enhanced Energy Efficiency Assessment: The software's capability to model long-term energy consumption helped reduce annual electricity usage from 2.45 GWh to 2.0 GWh. By comparing the two systems in terms of energy efficiency and cost-effectiveness over time, the analysis pinpointed that these savings could be achieved while maintaining or even increasing the embedded network tariffs without raising tenant costs. Without Deep Energy AI, it would have been much harder to balance these variables accurately.
• Carbon Emissions Reduction: The ability of Deep Energy AI to incorporate carbon emissions calculations, factoring in energy usage and system efficiency, revealed a potential reduction of 939 tonnes of CO2 over 10 years. This level of detail in carbon impact would be difficult to model using traditional methods, but Deep Energy AI streamlined the process by integrating it into the same financial and energy performance framework.
• Lifecycle and Long-term Viability: The software's capacity to run 10- and 50-year projections allowed the team to assess lifecycle replacement costs comprehensively. The longer lifespan of the Heat-Ex system's components (water-sourced heat pumps, chillers, and boilers) compared to the VRV/VRF system was evident only because of Deep Energy AI's ability to model long-term financial viability, incorporating future equipment replacement costs and energy pricing forecasts.

In short, Deep Energy AI played a crucial role in achieving these results. By allowing for detailed physics-based simulations and long-term financial modeling, it provided insights that would have been nearly impossible to obtain manually, such as the delicate balance between initial capital costs, long-term operational savings, and sustainability outcomes.