The age of hypervision

36

Pascal Torres explains why we need a multi-system overview in order to achieve comfort and energy efficiency in smart buildings.

We live in an age of ultra connection, ultra-monitoring, and continuous exposure to information and data – and this includes our buildings. At any moment, it is possible for us to know the real-time temperature of every room, the heathing power of each air-conditioning unit, the airflow of each air-handling unit (AHU).

On a macro level, ‘smart’ buildings and energy efficiency make a sizeable contribution to Europe’s progress towards its ‘green’ goals.

On an individual building level, comfort and energy efficiency are the primary objectives of buildings’ technical installations. But are we achieving this? In most cases, probably not. Too much information can kill information: We are receiving a lot of data – but over-instrumentation can make data analysis even more confusing.

Enter the “age of Hypervision”. A systemic and multi-criteria analysis tool, Hypervision aggregates all the information collected by a building management system (BMS), making it possible to establish correlations between the various incidents it detects, and giving operators additional clues to anticipate and resolve these problems.

Hypervision’s major advantage is that it gives operators a more global view of all systems – all technical installations – and allows for more in-depth analysis of performance indicators. This saves precious time, allows operators to better understand and process data, and to be proactive in avoiding malfunctions that could have serious impacts.

While Hypervision requires specialised technical skills for its initial setup, along with specific, relevant algorithms designed and implemented for a given building, it is an easy system to operate.

For the once-off setup, a Hypervision specialist-provider would need to:

• Establish a systemic analysis: Break down the overall installation into subassemblies, and into objects.
• Identify correlations: The balance of incoming and outgoing energy flows, for example.
• Identify and implement any additional data required (weather data, usage data, etc.).
• Identify data related to comfort.
• Set up energy-optimization scenarios.
• Set up decision support dashboards.
• Configure automatic dissonance alerts.

In line with Leonardo da Vinci’s maxim, “simplicity is the ultimate sophistication,” Hypervision’s user interface – for in-house operators – must be simple and visual, including charts and some indicators relating to information synthesis.

The user interface must allow:

• A rapid analysis of alerts that are generated automatically, and their root causes.
• Relevant help in refining optimization scenarios and associated dashboards.
• Easy identification of changes to be made to the system to make it more efficient.

Take the example of Hypervision operating in a meeting room. The objectives: Comfort and energy efficiency.

First, comfort:

A simple and standardized indicator (see ISO 7730 standard) is the ‘predicted mean vote’ (PMV). The PMV is an index that predicts the mean value of the votes of a large group of people on a seven-point thermal-sensation scale (from ‘hot’ at +3, to ‘cold’ at -3), based on the heat balance of the human body. Thermal balance is obtained when the internal heat production in the body is equal to the loss of heat to the environment.

Hypervision will periodically calculate this indicator (for example, every five minutes, or after a period defined by the operator), and provide a daily or weekly summary. The Hypervision system will perform a root-cause analysis of the periods when ‘comfort’ has not been achieved (less than -1 or greater than +1). It will take a range of elements into account: airflow, blowing temperature, ambient temperature, humidity, CO2, setpoints applied, cooling and heating power, and room preparation time.

Next, energy efficiency:

Operators can consider some simple and relevant elements drawn from Hypervision:

• Did the room remain in occupied mode when it was empty?
• Were the ventilation-, cold- and heat consumption consistent with actual use and weather conditions? (a self-learning model would refine this, over time)?

Beyond detecting overconsumption, Hypervision’s anomaly detection can also anticipate more serious failures.

A McKinsey analysis quantified these potential gains: Reduction of maintenance costs by 10 to 40%, reduction of breakdowns by half, and reduction of investments by 3 to 5% (by increasing the lifespan of the system).

Hypervision is an essential tool for managing and understanding building systems – and potentially a major lever in making progress towards Europe’s green targets.

Pascal Torres

About the author

Pascal Torres is the CEO of ENOLEO, a Monaco-based global entity focused on energy-efficiency optimisation for buildings and industrial processes. Since 2008, ENOLEO has specialised in the implementation of Hypervision hardware and software solutions.