In the present work, we analyze the influence of the designer’s choice of values for the human metabolic index (met) and insulation by clothing (clo) that can be selected within the ISO 7730 for the calculation of the energy demand of buildings. To this aim, we first numerically modeled, using TRNSYS, two buildings in different countries and climatologies. Then, we consistently validated our simulations by predicting indoor temperatures and comparing them with measured data. After that, the energy demand of both buildings was obtained. Subsequently, the variability of the set-point temperature concerning the choice of clo and met, within limits prescribed in ISO 7730, was analyzed using a Monte Carlo method. This variability of the interior comfort conditions has been finally used in the numerical model previously validated, to calculate the changes in the energy demand of the two buildings. Therefore, this work demonstrated that the diversity of possibilities offered by ISO 7730 for the choice of clo and met results, depending on the values chosen by the designer, in significant differences in indoor comfort conditions, leading to non-negligible changes in the calculations of energy consumption, especially in the case of big buildings.
We present a statistical analysis of correlations between carbon dioxide concentration and energy consumption in workplaces, with the goal of enabling its eventual programming in energy platforms In Cloud for improving management and enhancing automation and control in the service sector industry. A low-cost system is used to measure and transmit the data of buildings and the information can be analyzed by any remote device. The study includes data measured during one year in two offices located at Sada (A Coruña, Spain) and Clayton (Panama). We present linear and nonlinear regression curves, show that there are significant positive correlations and discuss seasonal and climate-related aspects. We argue that this kind of analysis can provide useful information for the design and operation in the service industry, through web-based energy platforms for control and automation.
Keywords: Industry 4.0, intelligent energy platforms, energy consumption, statistical analysis, automation of facilities, control of facilities.
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