Analysis of building energy upgrade technologies for implementing the dual energy efficiency and demand response scheme for non-residential buildings

The continuous growth of renewable energy and the transition to a more de-centralised electricity generation adds significant complexity to balance power supply and demand in the grid. These imbalances are partially compensated by demand response programs, which represent a new business opportunity in the building sector, especially for ESCOs. Including demand response to their traditional energy efficiency-based business model adds an additional revenue stream that could potentially shorten payback periods of energy renovation projects. This paper introduces this new dual-services business model, and evaluates the potential suitability of HVAC, generation and storage technologies to ensure proposed energy efficiency and flexibility goals.This paper is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 745594. This paper reflects only the author´s views and neither the Agency nor the Commission are responsible for any use that may be made of the information contained therei

Heritability maps of human face morphology through large-scale automated three-dimensional phenotyping

The human face is a complex trait under strong genetic control, as evidenced by the striking visual similarity between twins. Nevertheless, heritability estimates of facial traits have often been surprisingly low or difficult to replicate. Furthermore, the construction of facial phenotypes that correspond to naturally perceived facial features remains largely a mystery. We present here a large-scale heritability study of face geometry that aims to address these issues. High-resolution, three-dimensional facial models have been acquired on a cohort of 952 twins recruited from the TwinsUK registry, and processed through a novel landmarking workflow, GESSA (Geodesic Ensemble Surface Sampling Algorithm). The algorithm places thousands of landmarks throughout the facial surface and automatically establishes point-wise correspondence across faces. These landmarks enabled us to intuitively characterize facial geometry at a fine level of detail through curvature measurements, yielding accurate heritability maps of the human face (www.heritabilitymaps.info)

Demand flexibility enabled by virtual energy storage to improve renewable energy penetration

The increasing resort to renewable energy distributed generation, which is needed to mitigate anthropogenic CO2 emissions, leads to challenges concerning the proper operation of electric distribution systems. As a result of the intrinsic nature of Renewable Energy Sources (RESs), this generation shows a high volatility and a low predictability that make the balancing of energy production and consumption difficult. At the same time, the electrification of new energy‐intensive sectors (such as heating) is expected. This complex scenario paves the way for new sources of flexibility that will have more and more relevance in the coming years. This paper analyses how the electrification of the heating system, combined with an electric flexibility utilisation module, can be used to mitigate the problems related to the fluctuating production of RES. By using Power‐to‐Heat (P2H) technologies, buildings are able to store the overproduction of RES in the form of thermal energy for end‐use according to the principle of the so‐called Virtual Energy Storage (VES). A context‐aware demand flexibility extraction based on the VES model and the flexibility upscale and utilisation on district‐level through grid simulation and energy flow optimisation is presented in the paper. The involved modules have been developed within the PLANET (PLAnning and operational tools for optimising energy flows and synergies between energy NETworks) H2020 European project and interact under a unified co‐simulation framework with the PLANET Decision Support System (DSS) for the analysis of multi‐energy scenarios. DSS has been used to simulate a realistic future energy scenario, according to which the imbalance problems triggered by RES overproduction are mitigated with the optimal exploitation of the demand flexibility enabled by VES

Optimization module for filtering and ranking alternative energy replacement systems, in an online ICT design tool for building retrofits

This paper describes the development of an innovative optimizer component as part of a calculation tool for evaluating and comparing a set of retrofitting options for domestic heating and cooling systems. At the initial stage of the process, a filtering sub-module has been developed to pre-process the information introduced by the user and generate a limited set of simulations, thus speeding up the calculation process. At a later stage, the optimizer collects and post-processes outputs from the simulation core before displaying them as a result. In this later stage, a series of performance indicators are calculated and an analytical hierarchical process (AHP) is performed to rank the results based on the user's prioritization weighting for each key performance indicator. As the main outcome of this contribution, the benefits of implementing this optimizer are evaluated in increasing the efficiency of the rest of the components of the tool and, consequently, of the overall calculation process.This study has been developed within the HEAT4COOL research project. This project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 723925. This document reflects only the authors’ view and the Commission is not responsible for any use that may be made of the information it contains. The work has also been supported by the Swiss State Secretariat for Education, Research and Innovation (SERI) under Contract No. 16.0082

Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker.

Machine learning analysis of neuroimaging data can accurately predict chronological age in healthy people. Deviations from healthy brain ageing have been associated with cognitive impairment and disease. Here we sought to further establish the credentials of 'brain-predicted age' as a biomarker of individual differences in the brain ageing process, using a predictive modelling approach based on deep learning, and specifically convolutional neural networks (CNN), and applied to both pre-processed and raw T1-weighted MRI data. Firstly, we aimed to demonstrate the accuracy of CNN brain-predicted age using a large dataset of healthy adults (N = 2001). Next, we sought to establish the heritability of brain-predicted age using a sample of monozygotic and dizygotic female twins (N = 62). Thirdly, we examined the test-retest and multi-centre reliability of brain-predicted age using two samples (within-scanner N = 20; between-scanner N = 11). CNN brain-predicted ages were generated and compared to a Gaussian Process Regression (GPR) approach, on all datasets. Input data were grey matter (GM) or white matter (WM) volumetric maps generated by Statistical Parametric Mapping (SPM) or raw data. CNN accurately predicted chronological age using GM (correlation between brain-predicted age and chronological age r = 0.96, mean absolute error [MAE] = 4.16 years) and raw (r = 0.94, MAE = 4.65 years) data. This was comparable to GPR brain-predicted age using GM data (r = 0.95, MAE = 4.66 years). Brain-predicted age was a heritable phenotype for all models and input data (h(2) ≥ 0.5). Brain-predicted age showed high test-retest reliability (intraclass correlation coefficient [ICC] = 0.90-0.99). Multi-centre reliability was more variable within high ICCs for GM (0.83-0.96) and poor-moderate levels for WM and raw data (0.51-0.77). Brain-predicted age represents an accurate, highly reliable and genetically-influenced phenotype, that has potential to be used as a biomarker of brain ageing. Moreover, age predictions can be accurately generated on raw T1-MRI data, substantially reducing computation time for novel data, bringing the process closer to giving real-time information on brain health in clinical settings

Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker

Machine learning analysis of neuroimaging data can accurately predict chronological age in healthy people. Deviations from healthy brain ageing have been associated with cognitive impairment and disease. Here we sought to further establish the credentials of ‘brain-predicted age’ as a biomarker of individual differences in the brain ageing process, using a predictive modelling approach based on deep learning, and specifically convolutional neural networks (CNN), and applied to both pre-processed and raw T1-weighted MRI data. Firstly, we aimed to demonstrate the accuracy of CNN brain-predicted age using a large dataset of healthy adults (N = 2001). Next, we sought to establish the heritability of brain-predicted age using a sample of monozygotic and dizygotic female twins (N = 62). Thirdly, we examined the test-retest and multi-centre reliability of brain-predicted age using two samples (within-scanner N = 20; between-scanner N = 11). CNN brain-predicted ages were generated and compared to a Gaussian Process Regression (GPR) approach, on all datasets. Input data were grey matter (GM) or white matter (WM) volumetric maps generated by Statistical Parametric Mapping (SPM) or raw data. CNN accurately predicted chronological age using GM (correlation between brain-predicted age and chronological age r = 0.96, mean absolute error [MAE] = 4.16 years) and raw (r = 0.94, MAE = 4.65 years) data. This was comparable to GPR brain-predicted age using GM data (r = 0.95, MAE = 4.66 years). Brain-predicted age was a heritable phenotype for all models and input data (h2 ≥ 0.5). Brain-predicted age showed high test-retest reliability (intraclass correlation coefficient [ICC] = 0.90–0.99). Multi-centre reliability was more variable within high ICCs for GM (0.83–0.96) and poor-moderate levels for WM and raw data (0.51–0.77). Brain-predicted age represents an accurate, highly reliable and genetically-influenced phenotype, that has potential to be used as a biomarker of brain ageing. Moreover, age predictions can be accurately generated on raw T1-MRI data, substantially reducing computation time for novel data, bringing the process closer to giving real-time information on brain health in clinical settings

'Brukin2D' : a 2D visualization and comparison tool for LC-MS data

Peer reviewe

Correspondence sampling and predictive methodologies on manifolds and polyhedral surfaces

In recent years, a significant increase of interest can be observed regarding the analysis of objects with inherently complex morphological structures. This is especially true in the fields of biomedical imaging and computer vision, where the advent of new three-dimensional (3D) data capturing technologies has enabled the relatively low-cost acquisition of detailed models representing the boundaries of physical and biological structures, such as cardiac, brain and facial surfaces. Within this framework, of large importance is the development of methodologies tailored for statistical analysis and predictive modeling with complex objects being either the input or the response variables. In this thesis, we are concerned with aspects pertaining both the manipulation of complex surface objects, so as to enable their statistical analysis, as well as the construction of predictive methodologies that are suited to such data, and take advantage of the additional morphological structure to improve prediction accuracy. In more detail, we first concentrate on the problem of establishing dense point correspondence on collections of similar 3D polyhedral surfaces. This annotation is a prerequisite for any type of further data analysis. By considering these polyhedral objects as approximations of two-dimensional non-linear surfaces, a.k.a. manifolds, we are able to present a methodology for the automatic and dense point annotation, that can be accurately and efficiently employed on large datasets. We apply our algorithm on a detailed heritability and genome association study of the human face shape. Subsequently, we deal with the problem of using morphological data, extracted from surface objects, as inputs for regression analysis. In particular, we extend our previous algorithm for point correspondence to the regression setting, by incorporating an adaptive procedure that identifies and annotates more densely subregions within the surfaces that are highly predictive of a related response variable. This procedure leads to significant improvements in prediction accuracy, as evaluated on an application of age prediction from facial surfaces. Finally, we consider the opposite scenario of predictive modeling with complex objects constituting the responses. We identify as main problem the fact that the response space can no longer be considered Euclidean. To solve this, we construct a non-parametric regression methodology for manifold-valued objects. The method is versatile and can be applied in cases where the response space is a well-defined manifold, but also when such knowledge is not available. Model fitting and prediction phases only require the definition of a suitable distance function on the response space. We apply our method in a variety of image completion problems, as well as the prediction of human faces from genotypic data. Construction of suitable regression algorithms is necessary here, since most methodologies cannot deal with the issue of non-linearity in the response space. Existing manifold regression methods either require the rigorous definition of the underlying manifold or make use of kernel functions to implicitly project the responses on a very high-dimensional vectorial space, where standard regression methods can be applied. In the first case though, applicability is restricted to only very limited types of data, while in the second case, the issue of predicting on the response space can not be tackled easily or efficiently. We present a non-parametric predictive methodology for manifold-valued objects, based on our previous work on distance-based Random Forest \cite{Sim2013}. Predictions are made through a two-step approach, where we first find a point estimate on a Euclidean embedding of the responses, and then project that point back to the original space to acquire the manifold-valued prediction. The methodology can readily handle various types of data objects, since its only requirement is the definition of a meaningful distance function for the responses. Our predictive method was shown to outperform a number of different regression methods on various problems of image completion.Open Acces