Energy demand prediction for the implementation of an energy tariff emulator to trigger demand response in buildings

Buildings are key actors of the electrical gird. As such they have an important role to play in grid stabilization, especially in a context where renewable energies are mandated to become an increasingly important part of the energy mix. Demand response provides a mechanism to reduce or displace electrical demand to better match electrical production. Buildings can be a pool of flexibility for the grid to operate more efficiently. One of the ways to obtain flexibility from building managers and building users is the introduction of variable energy prices which evolve depending on the expected load and energy generation. In the proposed scenario, the wholesale energy price of electricity, a load prediction, and the elasticity of consumers are used by an energy tariff emulator to predict prices to trigger end user flexibility. In this paper, a cluster analysis to classify users is performed and an aggregated energy prediction is realised using Random Forest machine learning algorithm.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 768614. 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 therein

Improvements to previous algorithms to predict gene structure and isoform concentrations using Affymetrix Exon arrays

<p>Abstract</p> <p>Background</p> <p>Exon arrays provide a way to measure the expression of different isoforms of genes in an organism. Most of the procedures to deal with these arrays are focused on gene expression or on exon expression. Although the only biological analytes that can be properly assigned a concentration are transcripts, there are very few algorithms that focus on them. The reason is that previously developed summarization methods do not work well if applied to transcripts. In addition, gene structure prediction, i.e., the correspondence between probes and novel isoforms, is a field which is still unexplored.</p> <p>Results</p> <p>We have modified and adapted a previous algorithm to take advantage of the special characteristics of the Affymetrix exon arrays. The structure and concentration of transcripts -some of them possibly unknown- in microarray experiments were predicted using this algorithm. Simulations showed that the suggested modifications improved both specificity (SP) and sensitivity (ST) of the predictions. The algorithm was also applied to different real datasets showing its effectiveness and the concordance with PCR validated results.</p> <p>Conclusions</p> <p>The proposed algorithm shows a substantial improvement in the performance over the previous version. This improvement is mainly due to the exploitation of the redundancy of the Affymetrix exon arrays. An R-Package of SPACE with the updated algorithms have been developed and is freely available.</p

Taking advantage of an existing indoor climate monitorization for measuring occupancy

This paper describes a procedure to gain additional information from an already existing infrastructure primarily designed for other purposes. The deployed sensor network consists of wirelessly communicated indoor climate monitoring sensors, for which it is tried to extend its usage by determining occupancy in the room they are located, in that way the system provides a higher level aspect of the house usage. An elderly caring institution’s building has been monitored for one year obtaining data about temperature, relative humidity and CO2 levels from five different rooms. Such data shows some interesting patterns as the air flow between the rooms which should be considered in any real case scenario. The data has been used to train some machine learning models, which show acceptable quality overall suggesting to use this kind of sensing equipment to perform an occupancy monitoring non-intrusively. The acquired knowledge could bring additional opportunities in the care of the elderly, especially for specific diseases that are usually accompanied by changes in patterns of behaviour. By using the occupancy status it could be possible to determine changes in the daily patterns in that segment of the population which could be an indicative of the initial states of a disease or a worsening in it

An IoT sensor network to model occupancy profiles for energy usage simulation tools

The development of IoT devices has allowed to install large amounts of sensors in different environments. Consequently, monitoring small houses and entire buildings has become possible. In addition, buildings are one of the biggest energy consumers, so the monitoring of the energy waste, and its sources, is gaining attention. Human behaviour has been reported as being the main discrepancy source between energy usage simulations and real usage, thus being able to easily monitor such behaviour will bring greater insight in the building usage. In this paper, an IoT sensor network is proposed to model occupancy profiles at room level. Such measurement of users’ behaviour along with additional information such as temperature or humidity can be used to develop strategies to save energy, especially regarding heating, ventilating and airconditioning (HVAC) systems. The proposed equipment has been gathering data for some months in a workplace containing several meeting rooms. Four of those rooms were monitored and later analysed to test the validity of the proposed approach. The results show that it is possible to obtain occupancy profiles by using simple IoT equipment.Unai Saralegui is grateful to the Tecnalia Research & Innovation Foundation for funding through a PhD fellowship. Olatz Arbelaitz and Javier Muguerza’s research was partially supported by the Department of Education, Universities and Research of the Basque Government under Grant IT980- 16 and by the Ministry of Economy and Competitiveness of the Spanish Government and the European Regional Development fund- ERFD (PhysComp project, TIN2017- 85409-P)

Graph Based Learning for Building Prediction in Smart Cities

Anticipating pedestrians’ activity is a necessary task for providing a safe and energy efficient environment in an urban area. By locating strategically sensors throughout the city useful information could be obtained. By knowing the average activity of those throughout different days of the week we could identify the typology of the buildings neighboring those sensors. For these type of purposes, clustering methods show great capability forming groups of items that have great similarity intra clusters and dissimilarity inter cluster. Different approaches are made to classify sensors depending on the typology of buildings surrounding them and the mean pedestrians’ counts for different time intervals. By this way, sensors could be classified in different groups according to their activation patterns and the environment in which they are located through clustering processes and using graph convolutional networks. This study reveals that there is a close relationship between the activity pattern of the pedestrians’ and the type of environment sensors that collect pedestrians’ data are located. By this way, institutions could alleviate a great amount of effort needed to ensure safe and energy efficient urban areas, only knowing the typology of buildings of an urban zone

Premsa i poder polític.

Premsa i poder polític

Molecular gas in the immediate vicinity of Sgr A* seen with ALMA

We report serendipitous detections of line emission with ALMA in band 3, 6, and 7 in the central parsec of the Galactic center at an up to now highest resolution (<0.7''). Among the highlights are the very first and highly resolved images of sub-mm molecular emission of CS, H13CO+, HC3N, SiO, SO, C2H, and CH3OH in the immediate vicinity (~1'' in projection) of Sgr A* and in the circumnuclear disk (CND). The central association (CA) of molecular clouds shows three times higher CS/X (X: any other observed molecule) luminosity ratios than the CND suggesting a combination of higher excitation - by a temperature gradient and/or IR-pumping - and abundance enhancement due to UV- and/or X-ray emission. We conclude that the CA is closer to the center than the CND is and could be an infalling clump consisting of denser cloud cores embedded in diffuse gas. Moreover, we identified further regions in and outside the CND that are ideally suited for future studies in the scope of hot/cold core and extreme PDR/XDR chemistry and consequent star formation in the central few parsecs

An open prospective study on the efficacy of Navina Smart, an electronic system for transanal irrigation, in neurogenic bowel dysfunction

Esdeveniments adversos; Aplicacions; IncontinènciaEventos adversos; Aplicaciones; IncontinenciaAdverse events; Apps; IncontinenceBackground Transanal irrigation (TAI) has emerged as a key option when more conservative bowel management does not help spinal cord injured (SCI) individuals with neurogenic bowel dysfunction (NBD). Aim To investigate the short-term efficacy and safety of an electronic TAI system (Navina Smart) in subjects with NBD. Design We present an open, prospective efficacy study on Navina Smart, in individuals with NBD secondary to SCI, studied at three months. Population Eighty-nine consecutive consenting established SCI individuals (61 male; mean age 48, range 18–77) naïve to TAI treatment were recruited from ten centres in seven countries. Subjects had confirmed NBD of at least moderate severity (NBD score ≥10). Methods Subjects were taught how to use the device at baseline assisted by the Navina Smart app, and treatment was tailored during phone calls until optimal TAI regime was achieved. The NBD score was measured at baseline and at three months follow up (mean 98 days). Safety analysis was performed on the complete population while per protocol (PP) analysis was performed on 52 subjects. Results PP analysis showed a significant decrease in mean NBD score (17.8 to 10, p<0.00001). In subjects with severe symptoms (defined as NBD score ≥14), mean NBD scores decreased (19.4 to 10.9, p<0.0001). The number of subjects with severe symptoms decreased from 41 (79%) subjects at baseline to 16 (31%) at three months follow-up. Device failure accounted for the commonest cause for loss of data. Side effects possibly related to the device developed in 11 subjects (12%). Discontinuation due to failure of therapy to relieve symptoms was reported by 5 subjects (6%). Conclusion Navina Smart is effective for individuals with NBD, even those with severe symptoms; long-term data will follow. Whilst there were some device problems (addressed by the later stages of subject recruitment) the treatment was generally safe.The clinical study was full sponsored by Wellspect Healthcare. The funders initiated the study, monitored the conduct phase, and closed the study

Development of a novel splice array platform and its application in the identification of alternative splice variants in lung cancer

<p>Abstract</p> <p>Background</p> <p>Microarrays strategies, which allow for the characterization of thousands of alternative splice forms in a single test, can be applied to identify differential alternative splicing events. In this study, a novel splice array approach was developed, including the design of a high-density oligonucleotide array, a labeling procedure, and an algorithm to identify splice events.</p> <p>Results</p> <p>The array consisted of exon probes and thermodynamically balanced junction probes. Suboptimal probes were tagged and considered in the final analysis. An unbiased labeling protocol was developed using random primers. The algorithm used to distinguish changes in expression from changes in splicing was calibrated using internal non-spliced control sequences. The performance of this splice array was validated with artificial constructs for <it>CDC6</it>, <it>VEGF</it>, and <it>PCBP4 </it>isoforms. The platform was then applied to the analysis of differential splice forms in lung cancer samples compared to matched normal lung tissue. Overexpression of splice isoforms was identified for genes encoding <it>CEACAM1</it>, <it>FHL-1</it>, <it>MLPH</it>, and <it>SUSD2. </it>None of these splicing isoforms had been previously associated with lung cancer.</p> <p>Conclusions</p> <p>This methodology enables the detection of alternative splicing events in complex biological samples, providing a powerful tool to identify novel diagnostic and prognostic biomarkers for cancer and other pathologies.</p

Systems Biology in ELIXIR: modelling in the spotlight

info:eu-repo/semantics/publishedVersio