Special Issue on Build Trust in Building Energy Simulation
• 大类 : 工程技术 - 2区
• 小类 : 结构与建筑技术 - 2区
• 小类 : 能源与燃料 - 3区
• 小类 : 工程：土木 - 1区
Reproducibility promotes trust within our society - in individual research findings, in researchers, and in science more broadly. It is the key to the credibility of our field and the confidence in our research findings. Although breakthroughs and new discoveries provide important steps forward in our knowledge, replication studies give credibility to the research and help us identify results that are not strong enough to build on. Hence, replication is a vital part of the scientific process.
Same as other scientific fields, building is such a complex system involving multiple disciplines and numerous elements of dynamics and uncertainty. A great number of articles (over thousands) are published every year on different or similar buildings and systems, presenting similar or fully distinct (sometime opposite) conclusions. Although benchmarks are established for some typical cases, disparities are often observed due to the uncertainties in inputs, assumptions, and user skills.
Replication studies are rarely published because of the lack of a “breakthrough,” therefore drawing little recognition for the work in the form of citations, which tend to go to the original papers. There is also a perception that editors are not interested in replication studies, particularly those that confirm previous results.
Energy and Buildings, as one of the journals on Replication Studies Pilot effort initiated by Elsevier, will support valuing the reproducibility study and help build public trust in building science. As a result, Energy and Buildings will launch its first special issue with a focus on Replication in Building Simulation.
In this special issue, we will particularly welcome papers that address the following questions, without excluding other forms of replication. This applies all aspects of building simulation, such as energy, lighting, acoustics, material, air quality, thermal comfort, life cycle, power generation, etc.
Do existing theories (including analytical and empirical formula), models, methods, assumptions still stand? To what extent? Any possible revision, improvement, adjustment with today’s knowledge and technology?
Are simulation parameters and inputs correct, accurate and adequate for replication? What extra information will be desired to conduct a systematical replication and evaluation of a rigorous simulation?
To answer these two questions requires a thorough investigation and modeling of cases that are either prominent in the field with significant citations or with diverse (and even district) findings. New modeling methods and techniques are welcome to simulate these “old” cases, as well as using new data from experiment for model development and prediction validation.
The criteria for a successful replication and evaluation study may include good understanding of previous studies, necessary and adequate details of replication work, and thorough comparison of new results with published ones (both simulation and experiment if available). Explanations are highly sought to articulate the found disparities (if any) between new and old simulations and among different simulations. It is also valuable to confirm the existing findings, with particular interest to highlight the key scientific core that makes the case last. Critical review articles on contradict results for the same modeling problem is also of great interest.