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Using AI to Optimize the Flow of Energy Through the Built Environment

To achieve efficiency gains in our built environments, we need to modulate equipment performance over time. When you manage the thermal equilibrium of a building using dynamic modulation, you can optimize energy flow to ensure occupant comfort and achieve greater efficiency.

Optimizing the energy flow

Energy efficiency is a vast and largely untapped energy resource contained within our built environments that is key to meeting our future global energy needs. For over a decade, energy professionals have been talking about this hidden resource and about the magnitude of the potential energy savings for owners of commercial retail spaces, office buildings, manufacturing facilities, and many other conditioned spaces.

For instance, in its 2009 report entitled Unlocking Energy Efficiency in the U.S. Economy, McKinsey Global Energy and Materials determined that “the energy and operational savings from greater efficiency total some $1.2 trillion in present value to the U.S. economy: unlocking this value would require an initial upfront investment of approximately $520 billion [1].” A 2012 analysis by Deutsche Bank Climate Advisors came to a similar conclusion. They found that $279 billion in efficiency investments in the U.S. building sector would result in $1 trillion in savings over the next 10 years [2].

However, because of a number of persistent market barriers, these energy efficiency resources still remain mostly untapped…

This white paper details how BrainBox AI calculates the flow of energy through a building, predicts future conditions, and then optimizes existing HVAC control systems without human intervention.

  • 1.0 | Introduction

  • 2.0 | Characterizing the River of Energy

  • 3.0 | Predicting Energy Flow in a Building

  • 4.0 | Changing the Future

  • 5.0 | Conclusion

Download the full white paper