1,721,005 research outputs found
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
Full text linkAccelerating 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
Parametric performance analysis and energy model calibration workflow integration - A scalable approach for buildings
Full text linkHigh 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
SUSTAINABILITY INDICATORS FOR BUILDINGS: NETWORK ANALYSIS AND VISUALIZATION
Full text linkNowadays 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
Linking design and operation phase energy performance analysis through regression-based approaches
Full text linkThe reduction of energy usage and environmental impact of the built environment and construction industry is crucial for sustainability on a global scale. We are working towards an increased commitment towards resource efficiency in the built environment and to the growth of innovative businesses following circular economy principles. The conceptualization of change is a relevant part of energy and sustainability transitions research, which is aimed at enabling radical shifts compatible with societal functions. In this framework, building performance has to be considered in a whole life cycle perspective because buildings are long-term assets. In a life cycle perspective, both operational and embodied energy and carbon emissions have to be considered for appropriate comparability and decision-making. The application of sustainability assessments of products and practices in the built environment is itself a critical and debatable issue. For this reason, the way energy consumption data are measured, processed, and reported has to be progressively standardized in order to enable transparency and consistency of methods at multiple scales (from single buildings up to building stock) and levels of analysis (from individual components up to systems), ideally complementing ongoing research initiatives that use open science principles in energy research. In this paper, we analyse the topic of linking design and operation phase’s energy performance analysis through regression-based approaches in buildings, highlighting the hierarchical nature of building energy modelling data. The goal of this research is to review the current state of the art of in order to orient future efforts towards integrated data analysis workflows, from design to operation. In this sense, we show how data analysis techniques can be used to evaluate the impact of both technical and human factors. Finally, we indicate how approximated physical interpretation of regression models can help in developing data-driven models that could enhance the possibility of learning from feedback and reconstructing building stock data at multiple levels
Open data and models for energy and environment
Full text linkAn increasing number of data sources and models to handle them call for transparency and openness in assessing their goodness and practical use for people. The simplest and most robust tools to collect, process, and analyse data to offer solid data-based evidence for future projections in building and district and regional system planning are of interest. For this purpose, and following the success of the first Special Issue “Open Data and Energy Analytics”, the Special Issue “Open Data and Models for Energy and Environment” has been launched, intended for energy engineers and planners. Among a very high number of submissions, 10 articles were selected for acceptance and published
A psycho-acoustical experiment using a stereo dipole for spatial impression of music signals
No full textAcoustic performance of concert halls and opera houses is usually assessed by measuring the BIRs (Binaural Impulse Responses). Anechoic music convoluted with BIRs constitutes the virtual sound in the way it is played in the sound field, i.e. the room. From BIRs, the IACC (Inter-Aural Cross Correlation) can be computed. This parameter makes it possible to evaluate the spaciousness of the hall. However, the calculation of the IACC value is affected by the convolution technique used as well as the kind of musical motif. For example, in the same concert hall, the BIR provides three different IACC values in the case of three different motifs played in it. This study has conducted a psycho-acoustic experiment by using a virtual sound field representation produced by the stereo dipole technique in a listening room. In the experimental set-up there were two or four loudspeakers, corresponding to the single stereo-dipole or the dual stereo-dipole, respectively. By cancelling the cross-talk pathways (i.e. from left loudspeaker to right ear), the parallel sound presentation creates a 3D sound field for listeners sitting in the target point. The invert Kirkeby method was adopted to determine the inverse filters. Finally, the auralization technique with measured BIRs in theatres was utilized and the virtual sound field was generated in the Arlecchino listening room (Bologna, Italy), a low reverberation room equipped with an Ambisonic system. In the virtual sound field, the BIR was recorded again by the same dummy head used during the measurement in the theatres. The similarity between real and virtual sound fields was evaluated by comparing some acoustic parameters. The stereo-dipole technique demonstrates a good degree of accuracy of the sound field appearance. Moreover, the accuracy of the sound field appearance was analysed using two musical motifs and three musical instruments, comparing the values of the IACC calculated by echoic music with the virtual echoic music
Open data and energy analytics
Full text linkThis pioneering Special Issue aims at providing the state-of-the-art on open energy data analytics; its availability in the different contexts, i.e., country peculiarities; and at different scales, i.e., building, district, and regional for data-aware planning and policy-making. Ten high-quality papers were published after a demanding peer review process and are commented on in this Editorial
Validation and application of three-dimensional auralisation during concert hall renovation
Full text linkDuring the renovation of auditoria and concert halls, the acoustic quality is normally evaluated from measurements of impulse responses. One possibility for evaluating the acoustic quality from the measurements (the simulations) consists of convolving anechoic music with the measured (or simulated) impulse responses. In this way, a psycho-acoustic test is achieved using a virtual sound field representation. The listening room ‘Arlecchino’ at the University of Bologna includes ambisonics (up to fifth order) and stereo-dipole playback for virtual reproduction of sound in rooms. In this article, the effectiveness of the listening room ‘Arlecchino’ is first analysed, comparing acoustic parameters obtained from binaural impulse responses measured in some opera houses (in Italy) and auditorium (in Japan) with those virtually measured after the virtual reconstruction obtained in the listening rooms. The similarity between real and virtual sound fields, has been evaluated by comparing different acoustic parameters calculated by real and virtual sound fields, in four halls in different configurations, by means of the stereo-dipole method. In the second part of the article, the listening room was used to analyse the variation in interaural cross-correlation measurements in rooms obtained considering different anechoic sound signals convolved with the binaural impulse responses, to quantify the variation of the interaural cross correlation with different motifs. For this purpose, two different musical instrument digital interface musical motifs, very different from each other for their music characteristics, have been considered. Moreover, for each musical motif, different sound characteristics (i.e. different musical instruments) were considered, to consider both the rhythmic and timbre aspect
Data-Driven Building Energy Modelling – Generalisation Potential of Energy Signatures Through Interpretable Machine Learning
No full textBuilding energy modeling based on data-driven techniques has been demonstrated to be effective in a variety of situations. However, the question about its limits in terms of generalization is still open. The ability of a machine-learning model to adapt to previously unseen data and function satisfactorily is known as generalization. Apart from that, while machine-learning techniques are incredibly effective, interpretability is required for a "human-in-the-loop" approach to be successful. This study develops and tests a flexible regression-based approach applied to monitored energy data on a Passive House building. The formulation employs dummy (binary) variables as a piecewise linearization method, with the procedures for producing them explicitly stated to ensure interpretability. The results are described using statistical indicators and a graphic technique that allows for comparison across levels in the building systems. Finally, suggestions are provided for further steps toward generalization in data-driven techniques for energy in buildings
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