This article provides the first comprehensive assessment of methods for the creation of weather variables for use in building simulation. We undertake a critical analysis of the fundamental issues and limitations of each methodology and discusses new challenges, such as how to deal with uncertainty, the urban heat island, climate change and extreme events. Proposals for the next generation of weather files for building simulation are made based on this analysis. A seven-point list of requirements for weather files is introduced and the state-of-the-art compared to this via a mapping exercise. It is found that there are various issues with all current and suggested approaches, but the two areas most requiring attention are the production of weather files for the urban landscape and files specifically designed to test buildings against the criteria of morbidity, mortality and building services system failure. Practical applications: Robust weather files are key to the design of sustainable, healthy and comfortable buildings. This article provides the first comprehensive assessment of their technical requirements to ensure buildings perform well in both current and future climates.

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Average weather years have been used around the world for testing buildings to ascertain their likely energy use using thermal modelling software. In the UK, the Test Reference Years which are in current use were released in 2006 but generally consisted of data from 1983 to 2004. In this work, revised test reference years will be proposed which are based on a new climatic period from 1984 to 2013. The differences between the two years will be highlighted and the implications for building design will be discussed. Practical ApplicationTest Reference years are integral to building design to assess the performance of buildings at design stage. Specifically they are used to assess energy use in buildings as well as for compliance purposes with Part L of the Building Regulations.

There are many sophisticated building simulators capable of accurately modelling the thermal performance of buildings. Lumped Parameter Models (LPMs) are an alternative which, due to their shorter computational time, can be used where many runs are needed, for example when completing computer-based optimisation. In this paper, a new, more accurate, analytic method is presented for creating the parameters of a second order LPM, consisting of three resistors and two capacitors, that can be used to represent multi-layered constructions. The method to create this LPM is more intuitive than the alternatives in the literature and has been named the Dominant Layer Model. This new method does not require complex numerical operations, but is obtained using a simple analysis of the relative influence of the different layers within a construction on its overall dynamic behaviour. The method has been used to compare the dynamic response of four different typical constructions of varying thickness and materials as well as two more complex constructions as a proof of concept. When compared with a model that truthfully represents all layers in the construction, the new method is largely accurate and outperforms the only other model in the literature obtained with an analytical method.

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The digitization of manufacturing industry has led to leaner and more efficient production, under the Industry 4.0 concept. Nowadays, datasets collected from shop floor assets and information technology (IT) systems are used in data-driven analytics efforts to support more informed business intelligence decisions. However, these results are currently only used in isolated and dispersed parts of the production process. At the same time, full integration of artificial intelligence (AI) in all parts of manufacturing systems is currently lacking. In this context, the goal of this manuscript is to present a more holistic integration of AI by promoting collaboration. To this end, collaboration is understood as a multi-dimensional conceptual term that covers all important enablers for AI adoption in manufacturing contexts and is promoted in terms of business intelligence optimization, human-in-the-loop and secure federation across manufacturing sites. To address these challenges, the proposed architectural approach builds on three technical pillars: (1) components that extend the functionality of the existing layers in the Reference Architectural Model for Industry 4.0; (2) definition of new layers for collaboration by means of human-in-the-loop and federation; (3) security concerns with AI-powered mechanisms. In addition, system implementation aspects are discussed and potential applications in industrial environments, as well as business impacts, are presented.