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Documents published in Scipedia

• Development of a dynamic model for natural ventilated photovoltaic components and of a data driven approach to validate and identify the model parameters

  J. Cipriano, G. Houzeaux, G. Mor Martinez, U. Eicker, J. Carbonell, S. Danov

  Solar Energy (2016). Vol. 129, pp. 310-331

  
  

  Abstract

  In the development of dynamic models for the energy performance evaluation of building integrated natural ventilated PV components there are still many open questions regarding the uncertainty of the estimated parameters of the models. Traditionally, the dynamic models for these complex components are derived from the heat transfer balance equations, and the unknown heat transfer coefficients (convection and radiation), the solar properties of the materials or the pressure coefficients for the air mass flow rate balance, are assigned based on literature or on manufacturer prescriptions. However, there is a lack of systematic methods able to validate the simulation outputs with the measured data, taking into consideration the uncertainty of the parameters and their effect over the results. This research is focused on the development of a dynamic simulation model for a PV ventilated component, and on the application of a data-driven iterative approach to identify the unknown parameters, to evaluate their influence in the simulation outputs and finally, to determine the deviations of the simulations outputs against the measured data. During the identification process, 43 unknown parameters are detected and 13 of them are categorized as strong parameters. The implemented data driven approach is able to achieve high goodness of fit with the measured data and it is recommended to analyses which aim at evaluating the influence of some component parameters or the thermal and electrical energy produced by these natural ventilated PV components.

  Abstract
  In the development of dynamic models for the energy performance evaluation of building integrated natural ventilated PV components there are still many open questions regarding [...]

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• Influencing factors in energy use of housing blocks: a new methodology, based on clustering and energy simulations, for decision making in energy refurbishment projects

  X. Cipriano, A. Vellido, J. Cipriano, J. Martí-Herrero, S. Danov

  Energy Efficiency (2017). Vol. 10, pp. 359-382

  
  

  Abstract

  In recent years, big efforts have been dedicated to identify which are the factors with highest influence in the energy consumption of residential buildings. These factors include aspects such as weather dependence, user behaviour, socio-economic situation, type of the energy installations and typology of buildings. The high number of factors increases the complexity of analysis and leads to a lack of confidence in the results of the energy simulation analysis. This fact grows when we move one step up and perform global analysis of blocks of buildings. The aim of this study is to report a new methodology for the assessment of the energy performance of large groups of buildings when considering the real use of energy. We combine two clustering methods, Generative Topographic Mapping and k-means, to obtain reference dwellings that can be considered as representative of the different energy patterns and energy systems of the neighbourhood. Then, simulation of energy demand and indoor temperature against the monitored comfort conditions in a short period is performed to obtain end use load disaggregation. This methodology was applied in a district at Terrassa City (Spain), and six reference dwellings were selected. Results showed that the method was able to identify the main patterns and provide occupants with feasible recommendations so that they can make required decisions at neighbourhood level. Moreover, given that the proposed method is based on the comparison with similar buildings, it could motivate building occupants to implement community improvement actions, as well as to modify their behaviour.

  Abstract
  In recent years, big efforts have been dedicated to identify which are the factors with highest influence in the energy consumption of residential buildings. These factors [...]

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• Hidden Markov Models for indirect classification of occupant behaviour

  J. Liisberg, J. Moller, H. Bloem, J. Cipriano, G. Mor Martinez, H. Madsen

  Sustainable Cities and Society (2016). Vol. 27, pp. 83-98

  
  

  Abstract

  Even for similar residential buildings, a huge variability in the energy consumption can be observed. This variability is mainly due to the different behaviours of the occupants and this impacts the thermal (temperature setting, window opening, etc.) as well as the electrical (appliances, TV, computer, etc.) consumption. It is very seldom to find direct observations of occupant presence and behaviour in residential buildings. However, given the increasing use of smart metering, the opportunity and potential for indirect observation and classification of occupants’ behaviour is possible. This paper focuses on the use of Hidden Markov Models (HMMs) to create methods for indirect observations and characterisation of occupant behaviour. By applying homogeneous HMMs on the electricity consumption of fourteen apartments, three states describing the data were found suitable. The most likely sequence of states was determined (global decoding). From reconstruction of the states, dependencies like ambient air temperature were investigated. Combined with an occupant survey, this was used to classify/interpret the states as (1) absent or asleep, (2) home, medium consumption and (3) home, high consumption. From the global decoding, the average probability profiles with respect to time of day were investigated, and four distinct patterns of occupant behaviour were observed. Based on the initial results of the homogeneous HMMs and with the observed dependencies, time dependent HMMs (inhomogeneous HMMs) were developed, which improved forecasting. For both the homogeneous and inhomogeneous HMMs, indications of common parameters were observed, which suggests further development of the HMMs as population models.

  Abstract
  Even for similar residential buildings, a huge variability in the energy consumption can be observed. This variability is mainly due to the different behaviours of the occupants [...]

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• Comparing time-series clustering approaches for individual electrical load patterns

  Z. Greve, F. Lecron, F. Vallée, G. Mor Martinez, D. Pérez

  CIRED-Open Acces Proceedings Journal (2017). Vol. 2017 (1), pp. 2165-2168

  
  

  Abstract

  This work positions the task of grouping electricity load time series among the vast field of clustering, and highlights corresponding research issues. A selection of the most performant time-series clustering approaches from the signal processing community are compared on the same dataset, composed by domestic electricity load profiles from Spain. The cross-correlation-based distance of Paparrizos and Gravano (2015) is shown to provide the best trade-off between clustering accuracy and CPU times

  Abstract
  This work positions the task of grouping electricity load time series among the vast field of clustering, and highlights corresponding research issues. A selection of the [...]

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• Using matrix factorisation for the prediction of electrical quantities

  F. Lecron, Z. Grève, F. Vallée, G. Mor Martinez, D. Pérez, S. Danov, J. Cipriano

  CIRED-Open Acces Proceedings Journal (2017). Vol. 2017 (1), pp. 2568-2571

  
  

  Abstract

  The prediction task is attracting more and more attention among the power system community. Accurate predictions of electrical quantities up to a few hours ahead (e.g. renewable production, electrical load etc.) are for instance crucial for distribution system operators to operate their network in the presence of a high share of renewables, or for energy producers to maximise their profits by optimising their portfolio management. In the literature, statistical approaches are usually proposed to predict electrical quantities. In the present study, the authors present a novel method based on matrix factorisation. The authors' approach is inspired by the literature on data mining and knowledge discovery and the methodologies involved in recommender systems. The idea is to transpose the problem of predicting ratings in a recommender system to a problem of forecasting electrical quantities in a power system. Preliminary results on a real wind speed dataset tend to show that the matrix factorisation model provides similar results than auto regressive integrated models in terms of accuracy (MAE and RMSE). The authors' approach is nevertheless highly scalable and can deal with noisy data (e.g. missing data).

  Abstract
  The prediction task is attracting more and more attention among the power system community. Accurate predictions of electrical quantities up to a few hours ahead (e.g. renewable [...]

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• Developing indicators to improve energy action plans in municipalities: An accounting framework based on the fund-flow model

  X. Cipriano, G. Gamboa, S. Danov, G. Mor Martinez, J. Cipriano

  Sustainable Cities and Society (2017). Vol. 32, pp. 236-276

  
  

  Abstract

  The main instrument used to implement the EU 2020 objectives at local level is the Covenant of Mayors, which fosters the development of Sustainable Energy Action Plans (SEAPs); these plans aim to reduce CO2 emissions by at least 20% by 2020. These action plans are linked to different municipal promotion tools, and a set of indicators has also been proposed to define and monitor the actions. In general, the adequacy of the main indicators used by the SEAPs is analysed, taking into consideration the representation of the urban system across all scales (city, neighbourhood and building) as well as whether the chosen indicators enable the analyst to consider the influence of human activities (i.e. hours of use) on energy consumption, within the system. In contrast, this paper proposes to apply the fund-flow model as an accounting framework that can be used to develop indicators for energy-efficient urban planning. The energy model behind these indicators was obtained using an innovative procedure for energy simulation which considers the isolated characteristics of the buildings (type of construction, occupation, interior comfort, type of energy system) as well as the interaction with the surrounding buildings (shadows, streets, geometry).The indicators proposed have been developed within the SEMANCO UE project, and we have taken into account their potential as criteria for identifying the most effective actions for energy planning on multilevel scales. An example in Manresa (Spain) is used to test the usability. Our results demonstrate that these indicators are suitable for use in decision-making processes in the field of urban planning and for performing coherent comparisons of actions between urban areas, cities, countries or regions.

  Abstract
  The main instrument used to implement the EU 2020 objectives at local level is the Covenant of Mayors, which fosters the development of Sustainable Energy Action Plans (SEAPs); [...]

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• EMPOWERING, a smart Big Data framework for sustainable electricity suppliers

  G. Mor Martinez, J. Vilaplana, S. Danov, J. Cipriano, F. Solsona, D. Chemisana

  IEEE Access (2018). Vol. 6, pp. 71132-71142

  
  

  Abstract

  This paper presents the EMPOWERING project, a Big Data environment aimed at helping domestic customers to save electricity by managing their consumption positively. This is achieved by improving the information received about energy bills and offering online tools. The main contributions of EMPOWERING are the creation of a novel workflow in the electricity utility sector regarding the implementation of data analytics for their customers and the fast implementation of data-mining techniques in massive datasets within a Big Data platform to achieve scalability. The results obtained show that EMPOWERING can be of use for customers of electrical suppliers by changing their energy habits to decrease consumption and so increase environmental sustainability.

  Abstract
  This paper presents the EMPOWERING project, a Big Data environment aimed at helping domestic customers to save electricity by managing their consumption positively. This is [...]

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• Experimental and numerical assessment of local resonance phenomena in 3D-printed acoustic metamaterials

  D. Roca, T. Pàmies, J. Cante, O. Lloberas-Valls, J. Oliver

  Journal of vibration and acoustics. (2020). Vol. 142 (2), pp. 1-19

  
  

  Abstract

  The so called Locally Resonant Acoustic Metamaterials (LRAM) are a new kind of artificially engineered materials capable of attenuating acoustic waves. As the name suggests, this phenomenon occurs in the vicinity of internal frequencies of the material structure, and can give rise to acoustic bandgaps. One possible way to achieve this is by considering periodic arrangements of a certain topology (unit cell), smaller in size than the characteristic wavelength. In this context, a computational model based on a homogenization framework has been developed from which one can obtain the aforementioned resonance frequencies for a given LRAM unit cell design in the sub-wavelength regime, which is suitable for low-frequency applications. Aiming at validating both the proposed numerical model and the local resonance phenomena responsible for the attenuation capabilities of such materials, a 3D-printed prototype consisting of a plate with a well selected LRAM unit cell design has been built and its acoustic response to normal incident waves in the range between 500 and 2000 Hz has been tested in an impedance tube. The results demonstrate the attenuating capabilities of the proposed design in the targeted frequency range for normal incident sound pressure waves and also establish the proposed formulation as the fundamental base for the computational design of 3D-printed LRAM-based structures

  Abstract
  The so called Locally Resonant Acoustic Metamaterials (LRAM) are a new kind of artificially engineered materials capable of attenuating acoustic waves. As the name suggests, [...]

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• Variational approach to relaxed topological optimization: closed form solutions for structural problems in a sequential pseudo-time framework (preprint)

  J. Oliver, D. Yago, J. Cante, O. Lloberas-Valls

  Computer Methods in Applied Mechanics and Engineering (2019). Vol. 355, pp. 779-819

  
  

  Abstract

  The work explores a specific scenario for structural computational optimization based on the following elements: (a) a relaxed optimization setting considering the ersatz (bi-material) approximation, (b) a treatment based on a non-smoothed characteristic function field as a topological design variable, (c) the consistent derivation of a relaxed topological derivative whose determination is simple, general and efficient, (d) formulation of the overall increasing cost function topological sensitivity as a suitable optimality criterion, and (e) consideration of a pseudo-time framework for the problem solution, ruled by the problem constraint evolution. In this setting, it is shown that the optimization problem can be analytically solved in a variational framework, leading to, nonlinear, closed-form algebraic solutions for the characteristic function, which are then solved, in every time-step, via fixed point methods based on a pseudo-energy cutting algorithm combined with the exact fulfillment of the constraint, at every iteration of the non-linear algorithm, via a bisection method. The issue of the ill-posedness (mesh dependency) of the topological solution, is then easily solved via a Laplacian smoothing of that pseudo-energy. In the aforementioned context, a number of (3D) topological structural optimization benchmarks are solved, and the solutions obtained with the explored closed-form solution method, are analyzed, and compared, with their solution through an alternative level set method. Although the obtained results, in terms of the cost function and topology designs, are very similar in both methods, the associated computational cost is about five times smaller in the closed-form solution method this possibly being one of its advantages. Some comments, about the possible application of the method to other topological optimization problems, as well as envisaged modifications of the explored method to improve its performance close the work

  Abstract
  The work explores a specific scenario for structural computational optimization based on the following elements: (a) a relaxed optimization setting considering the ersatz [...]

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• Computational design of locally resonant acoustic metamaterials

  D. Roca, D. Yago, J. Cante, O. Lloberas-Valls, J. Oliver

  Computer Methods in Applied Mechanics and Engineering (2019). Vol. 345, pp. 161-182

  
  

  Abstract

  The so-called Locally Resonant Acoustic Metamaterials (LRAM) are considered for the design of specifically engineered devices capable of stopping waves from propagating in certain frequency regions (bandgaps), this making them applicable for acoustic insulation purposes. This fact has inspired the design of a new kind of lightweight acoustic insulation panels with the ability to attenuate noise sources in the low frequency range (below 5000 Hz) without requiring thick pieces of very dense materials. A design procedure based on different computational mechanics tools, namely, (1) a multiscale homogenization framework, (2) model order reduction strategies and (3) topological optimization procedures, is proposed. It aims at attenuating sound waves through the panel for a target set of resonance frequencies as well as maximizing the associated bandgaps. The resulting design’s performance is later studied by introducing viscoelastic properties in the coating phase, in order to both analyse their effects on the overall design and account for more realistic behaviour. The study displays the emerging field of Computational Material Design (CMD) as a computational mechanics area with enormous potential for the design of metamaterial-based industrial acoustic parts

  Abstract
  The so-called Locally Resonant Acoustic Metamaterials (LRAM) are considered for the design of specifically engineered devices capable of stopping waves from propagating in [...]

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