Overview of methods to analyse dynamic data

This book gives an overview of existing data analysis methods to analyse the dynamic data obtained from full scale testing, with their advantages and drawbacks. The overview of full scale testing and dynamic data analysis is limited to energy performance characterization of either building components or whole buildings. The methods range from averaging and regression methods to dynamic approaches based on system identification techniques. These methods are discussed in relation to their application in following in situ measurements: -measurement of thermal transmittance of building components based on heat flux meters; -measurement of thermal and solar transmittance of building components tested in outdoor calorimetric test cells; -measurement of heat transfer coefficient and solar aperture of whole buildings based on co-heating or transient heating tests; -characterisation of the energy performance of whole buildings based on energy use monitoring

Case studies of outdoor testing and analysis of building components

The construction and development of the PASSYS/PASLINK outdoor test cells were funded by the European Commission, with the objective of providing high-quality test environments for quantifying the performance of passive solar building components. Over the years since the original test cells were commissioned, the initial concept for outdoor testing has been extended to include other test cell types. Significant improvements have been made to the experimental procedures and analysis techniques, and a broad range of components has been tested. This paper describes representative experiments that have been conducted using these highly controlled outdoor test environments, indicates some of the related analysis, and shows the type of information that can be obtained from such tests. It demonstrates the way in which component performance can be ascertained in the realistic external environment. The case studies chosen range from building component tests within EC research projects to commercial tests, and from conventional building components to novel integrated facade systems. They also include a large range of passive and active components. Each case study summarises the test component, the purpose of the test, details of the test configuration (period of test, instrumentation, etc.), results and analysis, and associated modelling and monitoring where appropriate. The paper concludes with an appraisal of the advantages and limitations of the test cells for the various component types

Wireless sensors and IoT platform for intelligent HVAC control

Energy consumption of buildings (residential and non-residential) represents approximately 40% of total world electricity consumption, with half of this energy consumed by HVAC systems. Model-Based Predictive Control (MBPC) is perhaps the technique most often proposed for HVAC control, since it offers an enormous potential for energy savings. Despite the large number of papers on this topic during the last few years, there are only a few reported applications of the use of MBPC for existing buildings, under normal occupancy conditions and, to the best of our knowledge, no commercial solution yet. A marketable solution has been recently presented by the authors, coined the IMBPC HVAC system. This paper describes the design, prototyping and validation of two components of this integrated system, the Self-Powered Wireless Sensors and the IOT platform developed. Results for the use of IMBPC in a real building under normal occupation demonstrate savings in the electricity bill while maintaining thermal comfort during the whole occupation schedule.QREN SIDT [38798]; Portuguese Foundation for Science & Technology, through IDMEC, under LAETA [ID/EMS/50022/2013

Reduced-order modeling for energy performance contracting

Buildings subject to Energy Performance Contracts (EPCs) are usually quite complex public buildings, sometimes relatively old and usually barely documented from the technical standpoint. Gathering comprehensive and reliable technical information is a time consuming and expensive process that has to be carried out within the submission deadline. In these conditions, the standard approach to energy performance forecasting which uses detailed simulation is practically unfeasible. This paper proposes a reduced-order modeling approach that is tailored to the EPC tendering phase. The proposed methodology extends a third order building model, introducing explicit, albeit still abstract, representations of the heating/cooling system, of the weather influence and of the end-user gains. The extended parameter set reflects to a large degree the information that is readily available in practical on-site surveying, or that can be easily calculated from that information. As a consequence of the simplified physics, a knowledge driven, practical calibration procedure, which provides an effective way of reducing uncertainty, is proposed. The calibration procedure analyses the uncertainty present in the available knowledge and uses the constraints imposed by the implemented physics on the parameters’ dynamic to assess their value estimation. The modeling approach is exemplified through three case studies: the first one provides the comparison of the reduced-order model predictions with the outcomes of a detailed model of a small hospital, the second one is used to compare the reduced-order model predictions with the detailed measurements of energy consumption of a real building, and the third case study exemplifies the use in operational context with scarce information.Peer ReviewedPostprint (author's final draft

Control of Residential Space Heating for Demand Response Using Grey-box Models

Certain advanced control schemes are capable of making a part of the thermostatic loads of space heating in buildings flexible, thereby enabling buildings to engage in so-called demand response. It has been suggested that this flexible consumption may be a valuable asset in future energy systems where conventional fossil fuel-based energy production have been partially replaced by intermittent energy production from renewable energy sources. Model predictive control (MPC) is a control scheme that relies on a model of the building to predict the future impact on the temperature conditions in the building of both control decisions (space heating) and phenomena outside the influence of the control scheme (e.g. weather conditions). MPC has become one of the most frequently used control schemes in studies investigating the potential for engaging buildings in demand response. While research has indicated MPC to have many useful applications in buildings, several challenges still inhibit its adoption in practice. A significant challenge related to MPC implementation lies in obtaining the required model of the building, which is often derived from measurements of the temperature and heating consumption. Furthermore, studies have indicated that, although demand response in buildings could contribute to the task of balancing supply and demand, suitable tariff structures that incentivize consumers to engage in DR are lacking. The main goal of this work is to contribute with research that addresses these issues. This thesis is divided into two parts.The first part of the thesis explores ways of simplifying the task of obtaining the building model that is required for implementation of MPC. Studies that explore practical ways of obtaining the measurement data needed for model identification are presented together with a study evaluating the suitedness of different low-order model structures that are suited for control-purposes.The second part of the thesis presents research on the potential of utilizing buildings for demand response. First, two studies explore and evaluate suitable incentive mechanisms for demand response by implementing an MPC scheme in a multi-apartment building block. These studies evaluate two proposed incentive mechanisms as well as the impact of building characteristics and MPC scheme implementation. Finally, a methodology for bottom-up modelling of entire urban areas is presented, and proved capable of predicting the aggregated energy demand of urban areas. The models resulting from the methodology are then applied in an analysis on demand response

Outdoor test cells for building envelope experimental characterisation - A literature review

partially_open4siThe present work has been partially developed within the framework of IEA EBC Annex 58In the past decades the construction sector experienced the diffusion of a wide variety of complex building envelope components and passive elements and strategies, characterized by a dynamic response to the climatic parameters. Many of these components have been claimed to contribute to reducing building energy use and improving occupants’ comfort. These kind of envelope elements need nevertheless to be tested under laboratory and real dynamic weather conditions in order to characterise, and possibly to model, their behaviour and their effectiveness both in terms of energy saving and indoor environmental quality. Both indoor laboratories and outdoor test cells have been developed in order to tackle the challenging issue of experimentally characterising innovative envelope elements. However, not always the experimental methodologies are fully and explicitly described in the available literature, and they are rarely compared to other types of experimental procedures. The aim of the present paper is to describe and review recent state of the art technologies for outdoor test cells. The paper starts with a short introduction on potentialities and limitations of outdoor facilities with respect to indoor laboratories and real buildings field tests, and it continues with a detailed classification and description of the most relevant outdoor test cells developed in recent years.openCattarin, Giulio; Causone, Francesco; Kindinis, Andrea; Pagliano, LorenzoCattarin, Giulio; Causone, Francesco; Kindinis, Andrea; Pagliano, Lorenz

A State-Space Modeling Approach and Subspace Identification Method for Predictive Control of Multi-Zone Buildings with Mixed-Mode Cooling

The paper presents a control-oriented modeling approach for multi-zone buildings with mixed-mode (MM) cooling that incorporates their mode switching behavior. A forward state-space representation with time-varying system matrices is presented and used for establishing a detailed prediction model of a multi-zone MM building. The linear time-variant state-space (LTV-SS) model, which is considered as a true representation of the building, is used for developing data-driven linear time-invariant state-space models based on the subspace identification algorithm. The simplified black-box model can successfully capture the switching behavior of the MM building with the RMSE of 0.64 ºC