During rapid development of a heat pump-based heating system for a residential building, there is the need to compare configuration A with configuration B. A configuration entails not just the control algorithm but may include changes to pipework or some other physical change.
However, for a particular house, only one configuration can be active and for it to properly take effect, must be active for some period of time (such as a week). Nonetheless, the weather and use of the building may vary from week to week. The problem is how to compare the two configurations A and B?
One solution might be to interleave the configurations on a weekly basis (e.g., ABBA or BAAB) so that during a period whether the weather is changing (from winter to spring, e.g.) then the test is roughly fair to both. A problem, however, is that it may be the case that A performs well only in the current season but B is a better all-rounder. Thus it may be necessary to also test the configurations in a virtual setting and combine this with the real results to make the test fairer.
Another possible solution might be to look at the weather experienced during A versus that during B and to adjust accordingly. This has the difficulty is that it is unclear how to adjust the results to achieve a fair comparison.
Further considerations are how to fairly assess different configurations in terms of energy consumption and thermal environment. For example, is it sufficient to maintain a “comfortable” thermal environment according to some model of human thermal comfort perception, or should a target temperature be achieved.
Possible research questions include:
- What is a sufficiently `fair’ way to compare two configurations for a heating / cooling system?
- What are the appropriate metrics? For example, is Coefficient of Performance (COP) useful or do we also need to consider energy consumption or energy cost?
- How can virtual environments help with ensuring a fair comparison?