Energy efficiency consulting for building performance

Building systems are in a constant state of flux, responding to internal and external changes: energy prices, weather conditions, changing tenant and occupant dynamics. All of which make it difficult to answer energy efficiency consulting questions in assessing building perfomance.

The Challenge

It's no secret that understanding building performance is a path towards improving occupant comfort, reducing costs and emissions. And that understanding starts with having access to quality data in an appropriate context. For example:

• A building owner needs high level performance metrics for quarterly and annual reporting.
• A building manager needs to track performance and maintain daily operations.
• Engineers need detailed information from models, plans and fault logs to design and/or uplift existing systems.
• Accounts teams need to track expenditure for budgets and reporting.
• Sustainability teams require accurate consumption figures for carbon reporting against KPIs and on and on...

Constant Change

However, buildings like other systems are in a constant state of flux, responding to internal and external changes: energy prices, weather conditions, changing tenant and occupant dynamics. All of which make it difficult to answer simple questions like:

• 'How are we tracking compared to last year?'
• 'Which buildings are the best energy performers and why?'
• 'Which buildings are performing poorly and would benefit from an upgrade?'
• 'What type of upgrade would benefit us, by how much and where should we invest for maximum returns?'

Answering these questions starts with quality data but also requires normalisation of data: to adjust figures across a building and/or portfolio, by taking into consideration the disturbances that effect changing conditions over time.

Quality data

Most building portfolios are diverse with large variability in age and systems contained within which present the following problems:

• Buildings are in a constant state of flux, receiving continual and ongoing upgrades.
• Inaccessible data due to on-premise silos created by legacy equipment and/or vendor systems.
• PDF or paper documents such as electricity and gas invoices used as source data.

As a consequence, modeling data becomes a per-building activity, which means that any new tech comes at great cost.

Normalisation

The ability to filter out disturbances that effect changing conditions over time is challenging. Several key factors impacting on benchmarking building performance are:

• The building envelope, the external building facade, type, widows and cladding as well as the internal characteristics, ceiling height, fittings and fixtures that effect thermal mass.
• The internal state of equipment and its efficiency.
• External disturbances such as weather and the effect of fuel prices, electricity and gas as well as internal factors like occupancy.

Some progress has been made with regards to benchmarking buildings using KPIs like intensity of energy per m2 or per unit of production. However until effective normalisation occurs we are not likely to make a meaningful comparisons. For example, the highest energy consumer per m2 in a portfolio may actually be the most efficient, without properly considering the factors impacting upon performance you just won't know for sure.

Key developments

Several key developments are on the horizon to help owners, operators and tenants in the built environment:

• Brick: unified metadata schema for buildings that enables naming conventions to be consistently applied from one building to another.
• The DCH: a scalable and horizontally integrated data platform that enables easy onboarding of building applications for testing with reduced capital costs.
• Advanced algorithms and real-time control to understand and tune building performance, detect faults and manage operation of equipment in response to internal and external factors such as temperature, occupancy and price.

Conclusion

As a leading smart building consultant, we have actively participated in the development of the Brick metadata schema, on-boarded buildings to the DCH and are actively developing a modelling tool application on top of this architecture.

The modeling tool, Deep Energy AI is designed to help building and portfolio owners to address key data problems and find the optimal technology mix, quickly.

See Deep Energy AI for more details and/or contact BE to find out how smart building technologies are providing answers to energy efficiency consulting questions as a path to improving building perfomance.