85 research outputs found
From in-situ measurement to regression and time series models: An overview of trends and prospects for building performance modelling
No full textData analysis methodologies are crucial to learn insights from data and to create more trust in the assumptions used for energy performance assessment. Indeed, continuous performance monitoring should become a more diffuse practice in order to improve our design and operation strategies for the future. This is an essential step to reduce incrementally the gap between simulated and measured performance. In fact, assumptions in simulation represent a significant source of uncertainty when estimating the energy performance of buildings. This uncertainty affects decision-making processes in multiple ways, from design of new and refurbished buildings to policy making. The research presented aims to highlight potential links between experimental approaches for test-facilities and methods and tools used for continuous performance monitoring, at the state of the art. In particular, we start by exploring the relation between in-situ measurement of thermal transmittance (U) and regression-based monitoring approaches, such as co-heating test and energy signature, for heat load coefficient (HLC) and solar aperture (gA) estimation. After that, we highlight some recent developments in simplified dynamic energy modelling using lumped parameter models. In particular, we want to underline the scalability of these techniques, considering relevant issues in current integrated engineer design perspective. These issues include, among others, the necessity of limiting the number of a sensors to be installed in buildings, the possibility of employing both experimental and real operation data (and compare them with design data as well) and, finally, the possibility to automate performance monitoring at multiple scales, from single components, to individual buildings, to building stock and cities.Building Physic
Parametric performance analysis and energy model calibration workflow integration - A scalable approach for buildings
No full textHigh efficiency paradigms and rigorous normative standards for new and existing buildings are fundamental components of sustainability and energy transitions strategies today. However, optimistic assumptions and simplifications are often considered in the design phase and, even when detailed simulation tools are used, the validation of simulation results remains an issue. Further, empirical evidences indicate that the gap between predicted and measured performance can be quite large owing to different types of errors made in the building life cycle phases. Consequently, the discrepancy between a priori performance assessment and a posteriori measured performance can hinder the development and diffusion of energy efficiency practices, especially considering the investment risk. The approach proposed in the research is rooted on the integration of parametric simulation techniques, adopted in the design phase, and inverse modelling techniques applied in Measurement and Verification (M&V) practice, i.e., model calibration, in the operation phase. The research focuses on the analysis of these technical aspects for a Passive House case study, showing an efficient and transparent way to link design and operation performance analysis, reducing effort in modelling and monitoring. The approach can be used to detect and highlight the impact of critical assumptions in the design phase as well as to guarantee the robustness of energy performance management in the operational phase, providing parametric performance boundaries to ease monitoring process and identification of insights in a simple, robust and scalable way
Parametric performance analysis and energy model calibration workflow integration - a scalable approach for buildings
No full textHigh efficiency paradigms and rigorous normative standards for new and existing buildings are fundamental components of sustainability and energy transitions strategies today. However, optimistic assumptions and simplifications are often considered in the design phase and, even when detailed simulation tools are used, the validation of simulation results remains an issue. Further, empirical evidences indicate that the gap between predicted and measured performance can be quite large owing to different types of errors made in the building life cycle phases. Consequently, the discrepancy between a priori performance assessment and a posteriori measured performance can hinder the development and diffusion of energy efficiency practices, especially considering the investment risk. The approach proposed in the research is rooted on the integration of parametric simulation techniques, adopted in the design phase, and inverse modelling techniques applied in Measurement and Verification (M&V) practice, i.e., model calibration, in the operation phase. The research focuses on the analysis of these technical aspects for a Passive House case study, showing an efficient and transparent way to link design and operation performance analysis, reducing effort in modelling and monitoring. The approach can be used to detect and highlight the impact of critical assumptions in the design phase as well as to guarantee the robustness of energy performance management in the operational phase, providing parametric performance boundaries to ease monitoring process and identification of insights in a simple, robust and scalable way.</p
Multi-commodity network flow models for dynamic energy management – Mathematical formulation
No full textAbstractThe evolution of energy infrastructures towards a more distributed, adaptive, predictive and marketbased paradigm implies an effort on combining communication protocols and energy transmission and distribution systems in a common architecture. This architecture should allow decentralized control in order to be able to manage efficiently distributed generation, storage and exchange of energy between sources and sinks. Dynamic energy management models are a part of this “systems thinking” vision that aims to create a new field of applications that is at the intersection of computing science and energy technology. The broader implications associated with them are related with the possibility of creating communities that integrate energy supply and demand within a given region, in order to limit their impact. In order to push intelligence to the energy networks’ edges, up to individual sources and sinks, scalable and flexible distributed systems will have to be build. In this sense, data mining techniques and multicommodity network flow models can be combined for pattern detection, forecasting and optimization, which are essential features of dynamic energy management
Interpretable data-driven building load profiles modelling for Measurement and Verification 2.0
No full textAccelerating the decarbonisation of the built environment necessitates increasing electrification of end-uses, which in turn poses the issue of rethinking the role of energy efficiency in conjunction with flexibility in grid interaction. This requires a better understanding of the electricity load profiles at hourly or sub-hourly intervals using techniques that are simple, reliable, and interpretable. To this extent, this study proposes a reformulation of the Time Of Week and Temperature modelling approach. This approach is able to separate the energy consumption dependence on building operational characteristics (Time Of Week) and on weather (outdoor air temperature), through a highly automated modelling workflow, necessitating minimal effort for model tuning. These features, along with its intrinsic interpretability due to its formulation using multivariate regression and the availability of open-source software, makes it an ideal starting point for applied research. The case study selected for the research is a fully electrified public building in Southern Italy. The building has been monitored for 5 years, before, during and after the COVID-19 lockdown. The novel model formulation is calibrated using hourly interval data with a Coefficient of Variation of Root Mean Square Error in the range of 20.0-28.5% throughout the various monitoring periods. The counterfactual analysis of electricity consumption indicates a 10.7-26.7% decrease in electricity consumption due to operational adjustments following COVID-19 lockdown, highlighting the impact of behavioural change. Finally, the possibility of additional workflow automation and enhanced interpretability is discussed
The Carabattola—Vibroacoustical Analysis and Intensity of Acoustic Radiation (IAR)
No full textAmong the studies of musical instruments, one important, sometime underestimated discipline, is represented by ethnomusicology. The acoustic analyses on ethnic musical instruments (M.I.) are much more infrequent if compared to those on classical M.I. This article deals with the vibro-acoustic analysis on one of the most unknown ethnic, Italian M.I., i.e., the carabattola (also called battola), which used to be played in Italy until the late 1960s during the Holy Thursday before Easter. The study includes modal analysis and Intensity of Acoustic Radiation measured on an original carabattola, which was played in the Romagna area until the early twentieth century. After a brief overview about the theory of acoustic and vibrational analysis on musical instruments, the Intensity of acoustic radiation and its correlation with modal analysis are recalled, based on previous studies. In the experimental part of the article, the measurements conducted on the carabattola are described. Afterwards, the results obtained both from modal analysis and IAR measurements are analyzed and compared with other measurements previously conducted on musical (particularly percussion) instruments and commente
Energy analytics for supporting built environment decarbonisation
No full textThe identification of techno-economically feasible decarbonisation paths and sustainability transitions for the built environment is a necessary task for research today and building stock renovation processes can act in synergy with innovative economic and technological development paradigms to achieve different types of benefits such as economic growth and employment, together with resource efficiency and sustainability for the whole sector. The research presented aims at selecting the most relevant data analysis processes and techniques to respond to practical technical questions and to support decision-making in the built environment, at multiple scales of analysis, from individual buildings, to building stock and urban environment. The research aims to indicate in this way the possibility to join the micro-scale view, involving technological and behavioral issues in buildings, and the macro-scale view, involving strategic problems at market and policy levels for energy and sustainability planning. Further, the combined use of modelling techniques with large scale data acquisition and processing could guarantee multiple feed-backs from measured data, useful for the evolution, first of all, of design and operation practices in building but also, more in general, of the whole value chain of the sector. A synthesis and integration of modelling methodologies is presented through case studies, showing a path to improve transparency of performance assessment across building life cycle phases. Finally, multivariate data visualization techniques are presented to ease wider applicability of the described numerical techniques.</p
SUSTAINABILITY INDICATORS FOR BUILDINGS: NETWORK ANALYSIS AND VISUALIZATION
No full textNowadays rating systems to assess the sustainability of the built environment are available worldwide. The idea that a rating system based on indicators and a sustainability score can guarantee architectural quality, reliability, energy efficiency, economic convenience and finally a sustainability label, produces an increased value of the building on the real estate market giving an "aura" of advanced product to the building itself. It is well known that different rating systems can give a different sustainability score because similar areas of evaluation in different rating systems are not equal in term of indicators' weight. Moreover, the continuous updating of the rating systems tries to include in the assessment procedures a tailored vision coming from field experience. The building rating systems were born in the last 15 years (i. e. 1998-2004), while rating systems for urban districts are more recent (2009-2012). The paper provides a survey on the more influential and worldwide diffused rating systems, highlighting the differences in terms of organization and relationship between evaluation areas and comparing existing rating schemes with recent EU research projects and initiatives such as the "Common European framework for Sustainable Building Assessment" (CESBA) framework. The paper aims to report the preliminary analysis on the similarities and differences among rating systems, towards a harmonization of sustainability practices to be applied to new and existing buildings. A network analysis and visualization tool has been applied to show the structural analogies among rating systems through an innovative methodological approach which aims to enable a further development in this field by linking more directly these tools with computational tools used in the building lifecycle
Building performance monitoring: From in-situ measurement to regression-based approaches
No full textSimple and robust data analysis methodologies are crucial to learn insights from measured data and reduce the performance gap in building stock. For this reason, continuous performance monitoring should become a more diffuse practice in order to improve our design and operation strategies for the future. The research presented aims to highlight potential links between experimental approaches for test-facilities and methods and tools used for continuous performance monitoring, at the state of the art. In particular, we explore the relation between ISO 9869:2014 method for in-situ measurement of thermal transmittance (U) and regression-based monitoring approaches, such as co-heating test and energy signature, for heat load coefficient (HLC) and solar aperture (gA) estimation. In particular, we highlight the robustness and scalability of these monitoring techniques, considering relevant issues in current integrated engineer design perspective. These issues include, among others, the necessity of limiting the number of a sensors to be installed in buildings, the possibility of employing both experimental and real operation data and, finally, the possibility to automate and perform monitoring at multiple scales, from single components, to individual buildings, to building stock and cities.</p
Energy network modelling approaches for multi-scale building performance optimization
No full textEnergy dynamics in buildings can be described by means of different modelling approaches, depending of the specific purpose of the analysis, ranging from design phase simulation to energy management, optimal control, fault detection and diagnosis, etc. Network modelling formalism can help addressing energy related issues by simplifying physical representation. Further, the integrated use of robust computational techniques such as state-space models, transfer functions and time series models is crucial for the introduction of smart building technologies, conceived within the Internet of Things (IoT) paradigm. This technological paradigm can become a key enabler for the development of innovative and cost-effective solutions in building energy management and automation systems, aimed at high energy efficiency, low cost, flexibility and optimal interaction with infrastructures. However, the problem of modelling integration should be necessarily addressed to ensure the consistency of the proposed solutions. The research aims to present an analysis of the motivations to pursue a research in this direction, highlighting relevant features, opportunities and limitations.</p
- …
