Lumped parameter models for building thermal modelling: an analytic approach to simplifying complex multi-layered constructions

PublishedJournal ArticleThere 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. © 2013 Elsevier B.V

Chapter Quality of Information within Internet of Things Data

Due to the increasing number of IoT devices, the amount of data gathered nowadays is rather large and continuously growing. The availability of new sensors presented in IoT devices and open data platforms provides new possibilities for innovative applications and use-cases. However, the dependence on data for the provision of services creates the necessity of assuring the quality of data to ensure the viability of the services. In order to support the evaluation of the valuable information, this chapter shows the development of a series of metrics that have been defined as indicators of the quality of data in a quantifiable, fast, reliable, and human-understandable way. The metrics are based on sound statistical indicators. Statistical analysis, machine learning algorithms, and contextual information are some of the methods to create quality indicators. The developed framework is also suitable for deciding between different datasets that hold similar information, since until now with no way of rapidly discovering which one is best in terms of quality had been developed. These metrics have been applied to real scenarios which have been smart parking and environmental sensing for smart buildings, and in both cases, the methods have been representative for the quality of the data

An Analytical Heat Wave Definition Based on the Impact on Buildings and Occupants

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.Alongside a mean global rise in temperature, climate change predictions point to an increase in heat waves and an associated rise in heat-related mortality. This suggests a growing need to ensure buildings are resilient to such events. Unfortunately, there is no agreed way of doing this, and no standard set of heatwaves for scientists or engineers to use. In addition, in all cases, heat waves are defined in terms of external conditions, yet, as the Paris heat wave of 2003 showed, people die in the industrialised world from the conditions inside buildings, not those outside. In this work, we reverse engineer external temperature time series from monitored conditions within a representative set of buildings during a heat wave. This generates a general probabilistic analytical relationship between internal and external heatwaves and thereby a standard set of events for testing resilience. These heat waves are by their simplicity ideal for discussions between clients and designers, or for the setting of national building codes. In addition, they provide a new framework for the declaration of a health emergency.Engineering and Physical Sciences Research Council (EPSRC)Zero Peak Energy Building Design for IndiaActive Building CentreFundación Séneca-Agencia de Ciencia y Tecnología de la Región de MurciaSpanish Ministry of Economy and Competitivenes

A unified probabilistic model for predicting occupancy, domestic hot water use and electricity use in residential buildings

A strategy to combine separate probabilistic models into a unified model for predicting schedules of active occupancy, domestic hot water (DHW) use, and non-HVAC electricity use in multiple residences at 10-minute resolution for every day of the year is described. In addition to combining the models, a variety of new model functions are introduced in order to to generate stochastic predictions for each of numerous residences at once, to enforce appropriate variability of behaviors from a dwelling to another and to ensure that domestic hot water and electricity use predictions are coincident with occupancy. The original separate models were developed for the US and the UK; several scaling factors were added in the model to adjust the predictions so as to better agree with national aggregated data for Canada since the model developed from the described strategy was validated with measured data from a social housing building in Quebec City, Canada. This validation was made by comparing predictions from the unified model to measurements of domestic hot water use and electricity consumption from the 40 residential units of the monitored building. The validation showed that the tool can produce realistic profiles since it is mostly in agreement with consumption patterns found in the monitored building. However, there remain discrepancies which suggest potential research ideas for future work in occupant behavior modelling

The reliability of inverse modelling for the wide scale characterization of the thermal properties of buildings

The reduction of energy use in buildings is a major component of greenhouse gas mitigation policy and requires knowledge of the fabric and the occupant behaviour. Hence there has been a longstanding desire to use automatic means to identify these. Smart metres and the internet-of-things have the potential to do this. This paper describes a study where the ability of inverse modelling to identify building parameters is evaluated for 6 monitored real and 1000 simulated buildings. It was found that low-order models provide good estimates of heat transfer coefficients and internal temperatures if heating, electricity use and CO2 concentration are measured during the winter period. This implies that the method could be used with a small number of cheap sensors and enable the accurate assessment of buildings’ thermal properties, and therefore the impact of any suggested retrofit. This has the potential to be transformative for the energy efficiency industry.</p

Characterization of vertical wind speed profiles based on Ward's agglomerative clustering algorithm

Wind turbine blades have been constantly increasing since wind energy becomes a popular renewable energy source to generate electricity. Therefore, the wind sector requires a more efficient and representative characterization of vertical wind speed profiles to assess the potential for a wind power plant site. This paper proposes an alternative characterization of vertical wind speed profiles based on Ward's agglomerative clustering algorithm, including both wind speed module and direction data. This approach gives a more accurate incoming wind speed variation around the rotor swept area, and subsequently, provides a more realistic and complete wind speed vector characterization for vertical profiles. Real wind database collected for 2018 in the Forschungsplattformen in Nordund Ostsee (FINO) research platform is used to assess the methodology. A preliminary pre-processing stage is proposed to select the appropriated number of heights and remove missing or incomplete data. Finally, two locations and four heights are selected, and 561588 wind data are characterized. Results and discussion are also included in this paper. The methodology can be applied to other wind database and locations to characterize vertical wind speed profiles and identify the most likely wind data vector patterns.This work was supported in part by the Ministry of Science and Innovation (Spain) (No. PID2021-126082OB-C22)

Quality of Information within Internet of Things Data

Due to the increasing number of IoT devices, the amount of data gathered nowadays is rather large and continuously growing. The availability of new sensors presented in IoT devices and open data platforms provides new possibilities for innovative applications and use-cases. However, the dependence on data for the provision of services creates the necessity of assuring the quality of data to ensure the viability of the services. In order to support the evaluation of the valuable information, this chapter shows the development of a series of metrics that have been defined as indicators of the quality of data in a quantifiable, fast, reliable, and human-understandable way. The metrics are based on sound statistical indicators. Statistical analysis, machine learning algorithms, and contextual information are some of the methods to create quality indicators. The developed framework is also suitable for deciding between different datasets that hold similar information, since until now with no way of rapidly discovering which one is best in terms of quality had been developed. These metrics have been applied to real scenarios which have been smart parking and environmental sensing for smart buildings, and in both cases, the methods have been representative for the quality of the data

Perception of Spanish Professors of different educational stages regarding gender equality issues in the educational field

[EN] En el presente trabajo, se pretende analizar la percepción del profesorado respecto a la introducción de la perspectiva de género en el ámbito educativo. Para ello, se presentan los resultados de un sondeo de opinión que se llevó a cabo entre más de doscientos docentes que acudieron a una jornada de formación, en el que se plantearon diferentes cuestiones relativas a la igualdad de género en el campo de la educación. El análisis de estos resultados muestra que, aunque se atisban mejoras −a tenor de la percepción del profesorado− con respecto a ciertas cuestiones concretas, aún queda un largo camino por recorrer para lograr la igualdad real entre mujeres y hombres en el sector docente.[EN] In the present work, an analysis of the perception of professors regarding the introduction of the gender perspective in the educational field is carried out. For this purpose, the results of an opinion questionnaire that was carried out among more than two hundred teachers who attended a training session are presented, in which different questions related to gender equality in the field of education were raised. The analysis of these results shows that, although some improvements can be noticed –according to the perception of the professors– with regard to certain specific issues, there is still a long way to go to achieve real equality between women and men within the educational field