Nowadays, radiant ceiling systems can be considered among the technologies capable of meeting sustainable heating and cooling requirements. In order to adequately address design and simulation issues concerning these systems, correct evaluation of the heat transfer process is needed. The aim of this research is to present further evidence on the cooling capacity and heat transfer coefficients for a cooled radiant ceiling, assuring adequate thermal comfort levels in those possible different operation conditions. An experimental setup into a climate test room was developed and used to derive convenient results. The obtained values revealed that heat transfer evaluations on the basis of operative temperature as the unique reference temperature and corresponding total coefficient are not appropriate in real situations, but considering radiant and convective phenomena separately is strongly recommended.
A review on climate parameters affecting applicability of passive and low energy heating and cooling techniques is presented. The study has been developed from existing research work results, and aims to serve as a first-stage assessment tool of the viability of these solutions at a particular location, depending on outdoor conditions to be faced. This contribution starts with a justification of comprehensive climate analysis as the first step to evaluate whether a specific passive or low energy solution would be efficient, or on the contrary, it would incur in higher energy consumption. Comfort requirements indoors as well as building typology and use are then briefly tackled as they would determine actual applicability. It continues gathering the weather variables affecting passive solar, natural ventilation, free cooling and evaporative cooling technologies. Key climatic information is provided for the city of Valladolid (Spain), as an example. Finally it ends with an overview of existing tools for representing climate information in bioclimatic design. Thus, the main target of this paper is to serve as a guide for an adequate preselection of the optimal passive energy solutions in buildings at a specific site, from existing research on climate analysis.
Peak shaving, demand response, fast fault detection, emissions and costs reduction are some of the main objectives to meet in advanced district heating and cooling (DHC) systems. In order to enhance the operation of infrastructures, challenges such as supply temperature reduction and load uncertainty with the development of algorithms and technologies are growing. Therefore, traditional control strategies and diagnosis approaches cannot achieve these goals. Accordingly, to address these shortcomings, researchers have developed plenty of innovative methods based on their applications and features. The main purpose of this paper is to review recent publications that include both hard and soft computing implementations such as model predictive control and machine learning algorithms with applications also on both fourth and fifth generation district heating and cooling networks. After introducing traditional approaches, the innovative techniques, accomplished results and overview of the main strengths and weaknesses have been discussed together with a description of the main capabilities of some commercial platforms.
In recent years, new technologies, such as Artificial Intelligence, are emerging to improve decision making based on learning. Their use applied to the Architectural, Engineering and Construction (AEC) sector, together with the increased use of Building Information Modeling (BIM) methodology in all phases of a building’s life cycle, is opening up a wide range of opportunities in the sector. At the same time, the need to reduce CO2 emissions in cities is focusing on the energy renovation of existing buildings, thus tackling one of the main causes of these emissions. This paper shows the potentials, constraints and viable solutions of the use of Machine Learning/Artificial Intelligence approaches at the design stage of deep renovation building projects using As-Built BIM models as input to improve the decision-making process towards the uptake of energy efficiency measures. First, existing databases on buildings pathologies have been studied. Second, a Machine Learning based algorithm has been designed as a prototype diagnosis tool. It determines the critical areas to be solved through deep renovation projects by analysing BIM data according to the Industry Foundation Classes (IFC4) standard and proposing the most convenient renovation alternative (based on a catalogue of Energy Conservation Measures). Finally, the proposed diagnosis tool has been applied to a reference test building for different locations. The comparison shows how significant differences appear in the results depending on the situation of the building and the regulatory requirements to which it must be subjected.
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