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

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

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

Forced Laminar Flow in Pipes Subjected to Asymmetric External Conditions: The HEATT© Platform for Online Simulations

This chapter studies the fluid flow within pipes subjected to thermal asymmetrical boundary conditions. The phenomenon at hand takes place in many real-world industrial situations, such as solar thermal devices, aerial pipelines. A steady-state analysis of laminar forced-convection heat transfer for an incompressible Newtonian fluid is studied. The fluid is considered to flow through a straight round pipe provided with straight fins. For the case studied, axial heat conduction in the fluid has been considered and the effects of the forced convection have been considered to be dominant. A known uniform temperature field is applied at the upper external surface of the assembly. The 3D assembly has been created combining cylindrical and Cartesian coordinates. The governing differential equation system is solved numerically through suitable discretization in a set of different finite volume elements. The results are shown through the thermal profiles in respect of longitudinal and radial-azimuthal coordinates and the problem characteristic length. To facilitate the resolution of this phenomenon, an open computing platform called HEATT©, based on this model, has been developed, and it is also shown here. The platform is now being built and is expected to be freely available at the end of year 2022

Design and implementation of an interoperable architecture for integrating building legacy systems into scalable energy management systems

The building sector is responsible for a significant amount of energy consumption and greenhouse gas (GHG) emissions. Thus, the monitoring, control and optimization of energy consumption in buildings will play a critical role in the coming years in improving energy efficiency in the building sector and in reducing greenhouse gas emissions. However, while there are a significant number of studies on how to make buildings smarter and manage energy through smart devices, there is a need for more research on integrating buildings with legacy equipment and systems. It is therefore vital to define mechanisms to improve the use of energy efficiency in existing buildings. This study proposes a new architecture (PHOENIX architecture) for integrating legacy building systems into scalable energy management systems with focus also on user comfort in the concept of interoperability layers. This interoperable and intelligent architecture relies on Artificial Intelligence/Machine Learning (AI/ML) and Internet of Things (IoT) technologies to increase building efficiency, grid flexibility and occupant well-being. To validate the architecture and demonstrate the impact and replication potential of the proposed solution, five demonstration pilots have been utilized across Europe. As a result, by implementing the proposed architecture in the pilot sites, 30 apartments and four commercial buildings with more than 400 devices have been integrated into the architecture and have been communicating successfully. In addition, six Trials were performed in a commercial building and five key performance indicators (KPIs) were measured in order to evaluate the robust operation of the architecture. Work is still ongoing for the trials and the KPIs’ analysis after the implementation of PHOENIX architecture at the rest of the pilot sites

CIoTVID: Towards an Open IoT-Platform for Infective Pandemic Diseases such as COVID-19

The factors affecting the penetration of certain diseases such as COVID-19 in society are still unknown. Internet of Things (IoT) technologies can play a crucial role during the time of crisis and they can provide a more holistic view of the reasons that govern the outbreak of a contagious disease. The understanding of COVID-19 will be enriched by the analysis of data related to the phenomena, and this data can be collected using IoT sensors. In this paper, we show an integrated solution based on IoT technologies that can serve as opportunistic health data acquisition agents for combating the pandemic of COVID-19, named CIoTVID. The platform is composed of four layers&mdash;data acquisition, data aggregation, machine intelligence and services, within the solution. To demonstrate its validity, the solution has been tested with a use case based on creating a classifier of medical conditions using real data of voice, performing successfully. The layer of data aggregation is particularly relevant in this kind of solution as the data coming from medical devices has a very different nature to that coming from electronic sensors. Due to the adaptability of the platform to heterogeneous data and volumes of data; individuals, policymakers, and clinics could benefit from it to fight the propagation of the pandemic

A Semantically Data-Driven Classification Framework for Energy Consumption in Buildings

Encouraged by the European Union, all European countries need to enforce solutions to reduce non-renewable energy consumption in buildings. The reduction of energy (heating, domestic hot water, and appliances consumption) aims for the vision of near-zero energy consumption as a requirement goal for constructing buildings. In this paper, we review the available standards, tools and frameworks on the energy performance of buildings. Additionally, this work investigates if energy performance ratings can be obtained with energy consumption data from IoT devices and if the floor size and energy consumption values are enough to determine a dwellings&rsquo; energy performance rating. The essential outcome of this work is a data-driven prediction tool for energy performance labels that can run automatically. The tool is based on the cutting edge kNN classification algorithm and trained on open datasets with actual building data such as those coming from the IoT paradigm. Additionally, it assesses the results of the prediction by analysing its accuracy values. Furthermore, an approach to semantic annotations for energy performance certification data with currently available ontologies is presented. Use cases for an extension of this work are also discussed in the end