Meeting the challenges of energy modelling for new builds

Modeling an embedded network for a new building is challenging due to several factors:

1. Lack of Real-World Data: Without actual energy consumption data, predictions are less accurate.
2. Uncertain Design Specifications: Incomplete design details make precise modeling difficult.
3. Variability in Construction Materials: Different materials affect energy efficiency, and choices may not be finalized.
4. Regulatory Uncertainty: Future changes in energy regulations can impact compliance.
5. Dynamic Occupancy Patterns: Predicting usage and occupancy is speculative.
6. Integration with Existing Infrastructure: Aligning new systems with existing ones is complex.
7. Technological Advancements: Rapid tech changes can make initial models obsolete. These challenges highlight the need for flexible and adaptive modeling tools to accommodate uncertainties and changes during the planning and construction phases.

Using software simultations to meet the challenge

The new approach aimed to assess the best mix of HVAC systems and embedded network tariffs for an efficient energy solution. The main challenges included optimizing capital and operational costs while addressing potential inefficiencies in existing HVAC systems and minimizing long-term energy consumption and emissions.

eQUEST, developed by the U.S. Department of Energy and maintained by James J. Hirsch & Associates, is a powerful energy modeling software designed to address the challenges of modeling embedded networks for new buildings. It offers various wizards for different stages of building development, the ability to import or construct building geometry, extensive default inputs based on energy codes, and integration of long-term weather data. These features ensure accurate and flexible energy modeling.

Deep Energy AI offers an advanced AI-driven platform that simplifies the modeling process for embedded networks. By leveraging machine learning and data science, the platform allows consultants to quickly analyze energy opportunities, model multiple scenarios, and integrate financial and engineering data seamlessly.

The output of physics modelling in eQUEST was used as an input to Deep Energy AI to produce rapid cost models and inform HVAC system, and embedded network options for consideration for strategic and engineering teams.

The solution

The proposed solution involved switching from the initially approved VRV/VRF system to a more efficient Heat-Ex system. This decision was supported by physics-based modeling, which analyzed energy consumption, operational costs, and equipment lifecycle. The report concluded that the Heat-Ex system, coupled with optimized embedded network tariffs, would offer better long-term financial and operational benefits. It also examined scenarios for passing operational savings onto tenants or leveraging those savings for a more competitive tariff structure.

Options Analyzed

VRV/VRF System (Approved)

Pros: Lower upfront capital costs, suitable for smaller buildings.

Cons: Higher operational costs for larger buildings, shorter equipment lifecycle (15-20 years), higher CO2 emissions due to refrigerants.

Cost: Tenants pay $1,366 annually, 2.45 GWh annual consumption.

NPV: $2.47 million over 10 years.

Heat-Ex System (Proposed)

Pros: Higher efficiency, longer equipment life (20-30 years), lower CO2 emissions, reduced acoustic issues, $460,000 capital savings.

Cons: Higher initial capital investment.

Cost: Tenants pay $1,102 annually, 2.0 GWh annual consumption.

Savings: $62,587 annual tenant savings, $1,025,225 in energy savings over 10 years.

Embedded Network Scenarios

VRV/VRF: Can charge 24c/kWh with an NPV of $2.47 million.

Heat-Ex: Allows higher tariffs (up to 28c/kWh) due to lower energy consumption, benefiting operators while maintaining tenant costs.

Environmental Impact: Heat-Ex system reduces CO2 emissions by 939 tons over 10 years.

Conclusions

Deep Energy AI produced fully simulated yearly outputs from input variables. In addition to mutated load profiles, a flexible load analysis, costs & tariff analysis, the key output is the 10 & 50 year benefit-cost-ratio (BCR) and net-present value (NPV) cash flow tables for each scenario simulated.

Financial report outputs enabled the top benefit options to be realised enabling the energy consultant to get buy-in from key stakeholders for further detailed physics based modelling of technologies depending on design variables, architectural massing, insulation, air-tightness, fenestrations, plug loads, use cases and resultant HVAC system selection. From this information it was held that The Heat-Ex system provides greater long-term savings, environmental benefits, and flexibility in tariff structures, making it the preferred option over the VRV/VRF system for large-scale developments. And, engineers were able to enter into negotiations with the network operator, to push for a HV connection rather than several LV connections to, enable WSP participation, and reduce demand charges via the HV tariff.

Analysis of optimal embedded network scenario in Deep Energy AI