On-demand webinar: Optimisation of HVACR systems

– based on new guidelines to structure data collection from International Energy Agency (IEA) Annex 52

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There is an increasing need to work more effectively with energy optimisation.

IEA-HPT Annex 52 released “New Guideline for Instrumentation and Data” in January 2022 which will help building owners, designers, and commissioning agents to evaluate, benchmark, and optimise heat pump and chiller systems. The report focuses on ground source systems, but it will influence how the industry works with the optimisation of all air conditioning, heat pump, and refrigeration systems. This is because it identifies the key parameters that need to be measured and recommends data collection and analyses that take variations in operating conditions into account. 

HVACR systems – which consume 20% of global electricity – often use 20-30% more energy than they should because they are not optimised effectively. Most problems are caused by poor commissioning methods and a failure to detect performance deviations early.

Working cost-effectively with energy optimisation

The first step to work cost-effectively with energy optimisation and predictive maintenance is to establish strategies for data collection and analyses. These should be scientifically based and streamlined for all installations to make benchmarking possible. 

Measuring systems that are set up on a case-by-case basis does not deliver results that can be used without first performing an unrealistic amount of engineering work. 

As most of today’s installations are equipped from the start with sensors, it is more a question of how the data is collected, stored, and analysed than whether a new level of sensors should be added.

Data sampling

How data is sampled, stored, and made available for analyses can make the difference between data acquisition being useless or highly valuable.

Data is sampled in different ways in different systems. For example, some values are accumulated whereas others might be averaged, making them useless for early detection and difficult to analyse if there is a failure after the event.

Increasing requirements to document the distribution of energy consumption in facilities risk being costly without benefits unless they are a part of a planned measuring strategy.

During this webinar you will learn:

  • How to measure the performance of the air conditioning and heat pump systems using the Annex 52 guidelines.
  • How verification projects can identify savings potentials. 
  • How to plan new projects and upgrade existing systems.
  • How this report is relevant for, and relates to, other types of HVACR systems.

We will run a questions and answers session with our expert speakers at the end of the webinar.

Webinar was sent live on March 9th 16:00 CET

Jeffrey D. Spitler

Jeffrey D. Spitler is Regents Professor of mechanical engineering at Oklahoma State University where he holds the OG&E Energy Technology Chair. He has 30 years of experience researching heating and cooling systems, building load calculations, and related topics, with the results published in more than 120 technical papers and nine books.  He is a Fellow of ASHRAE and IBPSA, and currently serves as Editor-in-Chief of the journal Science and Technology for the Built Environment

Klas Berglöf

Klas Berglöf is the Head of R&D at ClimaCheck and the inventor of the “Internal Method” for analysing refrigeration processes (patented in 1986). Klas has a Master of Science degree in Applied Thermodynamics and Refrigeration from the Royal Institute of Technology in Stockholm and 30 years of experience, including management positions in maintenance, distribution, and manufacturing companies in the industry. He has been involved in research and development projects during most of his career and is a member of the Institute of Refrigeration in the UK and ASHRAE in the US where he has participated in the SPC207 workgroup dedicated to fault detection and diagnosis. In 2004, he co-founded ClimaCheck, the market leader in performance analytics and predictive maintenance.

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