Reconstruyendo la red de lazos personales : metodología egocéntrica para investigación sociocéntrica

Este artículo presenta los avances de investigación en un modelo computacional que permite describir una red social observada por medio de encuestas muestrales representativas de sus nodos. El modelo tiene también el objetivo de servir a estimar la fiabilidad de la encuesta así como estimar medidas globales de la red. De esta forma, el trabajo propone un camino alternativo al análisis egocéntrico de datos representativos de redes personales, obteniendo con un modelo de multiagente de simulación un análisis sociocéntrico de la información parcial de una red. La medición que se utiliza como marco de referencia empírico del modelo es la producida por la Encuesta de la Deuda Social Argentina en el año 2006. Esta encuesta se aplica anualmente en 1500 hogares del país sobre una muestra polietápica representativa de adultos en Gran Buenos Aires, Córdoba, Mendoza, Neuquén, Salta, Resistencia y Bahía Blanca.This article introduces the preliminary results of a computational network model research approach. The aim of this model is to help estimating sampling errors of an ongoing personal network survey, as well as to describe the network observed. In this way, it presents an alternative path to the egocentric analysis of representative data of personal networks, using a simulation multiagent model in order to make a sociocentric analysis on partial information of a network. The survey used as empirical reference frame for the model is the Encuesta de la Deuda Social Argentina (2006), which is applied annually to 1500 household along the country, using a multistage representative sample of adults in 7 large Argentinean cities (Gran Buenos Aires, Córdoba, Mendoza, Neuquén, Salta, Resistencia and Bahía Blanca)

Stochastic Coordinated Management of Electrical–Gas–Thermal Networks in Flexible Energy Hubs Considering Day-Ahead Energy and Ancillary Markets

This paper presents an optimal operation framework for electrical, gas, and thermal networks in the presence of energy hubs (EHs), so that EHs can benefit from day-ahead ancillary and energy markets. Therefore, to consider the goals of network operators (optimal operation of networks) and EHs (optimal operation in markets), the proposed model is developed in the form of a bi-level optimization. Its upper-level formulation minimizes the expected energy loss in the proposed networks based on the optimal power flow constraints and technical limits. At the lower-level problem, maximizing the expected profit of EHs in day-ahead energy and ancillary markets (including reactive and reserve regulation) is formulated based on the operational model of resources, storage devices, and responsive load in the EH framework, and the flexible constraints of EHs. This scheme includes the uncertainties of load, market price, renewable energy resources, and mobile storage energy demand, which uses the point estimation method to model them. Karush–Kuhn–Tucker is then used to extract the single-level model. Finally, by implementing the proposed scheme on a standard system, the obtained numerical results confirm the capability of the proposed model in improving the network’s operation and economic status of EHs. As a result, the proposed scheme is able to decrease operation indices such as energy losses, voltage drop, and temperature drop by approximately 28.5%, 39%, and 27.8%, respectively, compared to load flow analysis. This scheme can improve the flexibility of EHs, including non-controllable sources such as renewable resources, by nearly 100% and it obtains considerable profits for hubs.This research was funded by the “Basque Government (GISEL research group IT1522-22)

Vocal caricatures reveal signatures of speaker identity

What are the features that impersonators select to elicit a speaker’s identity? We built a voice database of public figures (targets) and imitations produced by professional impersonators. They produced one imitation based on their memory of the target (caricature) and another one after listening to the target audio (replica). A set of naive participants then judged identity and similarity of pairs of voices. Identity was better evoked by the caricatures and replicas were perceived to be closer to the targets in terms of voice similarity. We used this data to map relevant acoustic dimensions for each task. Our results indicate that speaker identity is mainly associated with vocal tract features, while perception of voice similarity is related to vocal folds parameters.Wetherefore show the way in which acoustic caricatures emphasize identity features at the cost of loosing similarity, which allows drawing an analogy with caricatures in the visual space.Fil: López, Sabrina. Dynamical Systems Lab, IFIBA-Physics dept, University of Buenos Aires, Pabellón 1, Ciudad Universitaria, CABA 1428EGA, ArgentinaFil: Riera, Pablo. Acoustics and Sound Perception Lab, Universidad of Quilmes, Roque Saénz Peña 352, Bernal, Buenos Aires B1876BXD, ArgentinaFil: Assaneo, María Florencia. Dynamical Systems Lab, IFIBA-Physics dept, University of Buenos Aires, Pabellón 1, Ciudad Universitaria, CABA 1428EGA, ArgentinaFil: Eguía, Manuel. Acoustics and Sound Perception Lab, Universidad of Quilmes, Roque Saénz Peña 352, Bernal, Buenos Aires B1876BXD, Argentin

Intelligent energy storage management trade-off system applied to Deep Learning predictions

The control of the electrical power supply is one of the key bases to reach the sustainable development goals set by United Nations. The achievement of these objectives encourages a dual strategy of creation and diffusion of renewable energies and other technologies of zero emission. Thus, meet the emerging necessities require, inevitably, a significant transformation of the building sector to improve the design of the electrical infrastructure. This improvement should be linked to advanced techniques that allows the identification of complex patterns in large amount of data, such as Deep Learning ones, in order to mitigate potential uncertainties. Accurate electricity and energy supply prediction models, in combination with storage systems will be reflected directly in efficiency improvements in buildings. In this paper, a branch of Deep Learning models, known as Standard Neural Networks, are used to predict electricity consumption and photovoltaic generation with the purpose of reduce the energy wasted, by managing the storage system using Reinforcement Learning technique. Specifically, Deep Reinforcement Learning is applied using the Deep Q-Learning agent. Furthermore, the accuracy of the predicted variables is measured by means of normalized Mean Bias Error (nMBE), and normalized Root Mean Squared Error (nRMSE). The methodologies developed are validated in an existing building, the School of Mining and Energy Engineering located on the Campus of the University of Vigo.Agencia Estatal de Investigación | Ref. TED2021-130677B-I00Financiado para publicación en acceso aberto: Universidade de Vigo/CISU

Feasibility of different weather data sources applied to building indoor temperature estimation using LSTM neural networks

The use of Machine Learning models is becoming increasingly widespread to assess energy performance of a building. In these models, the accuracy of the results depends largely on outdoor conditions. However, getting these data on-site is not always feasible. This article compares the temperature results obtained for an LSTM neural network model, using four types of meteorological data sources. The first is the monitoring carried out in the building; the second is a meteorological station near the site of the building; the third is a table of meteorological data obtained through a kriging process and the fourth is a dataset obtained using GFS. The results are analyzed using the CV(RSME) and NMBE indices. Based on these indices, in the four series, a CV(RSME) slightly higher than 3% is obtained, while the NMBE is below 1%, so it can be deduced that the sources used are interchangeable.Ministerio de Ciencia, Innovación y Universidades | Ref. RTI2018-096296-B-C

Machine learning and deep learning models applied to photovoltaic production forecasting

The increasing trend in energy demand is higher than the one from renewable generation, in the coming years. One of the greatest sources of consumption are buildings. The energy management of a building by means of the production of photovoltaic energy in situ is a common alternative to improve sustainability in this sector. An efficient trade-off of the photovoltaic source in the fields of Zero Energy Buildings (ZEB), nearly Zero Energy Buildings (nZEB) or MicroGrids (MG) requires an accurate forecast of photovoltaic production. These systems constantly generate data that are not used. Artificial Intelligence methods can take advantage of this missing information and provide accurate forecasts in real time. Thus, in this manuscript a comparative analysis is carried out to determine the most appropriate Artificial Intelligence methods to forecast photovoltaic production in buildings. On the one hand, the Machine Learning methods considered are Random Forest (RF), Extreme Gradient Boost (XGBoost), and Support Vector Regressor (SVR). On the other hand, Deep Learning techniques used are Standard Neural Network (SNN), Recurrent Neural Network (RNN), and Convolutional Neural Network (CNN). The models are checked with data from a real building. The models are validated using normalized Mean Bias Error (nMBE), normalized Root Mean Squared Error (nRMSE), and the coefficient of variation (R2). Standard deviation is also used in conjunction with these metrics. The results show that the models forecast the test set with errors of less than 2.00% (nMBE) and 7.50% (nRMSE) in the case of considering nights, and 4.00% (nMBE) and 11.50% (nRMSE) if nights are not considered. In both situations, the R2 is greater than 0.85 in all models.Universidade de Vigo | Ref. 00VI 131H 641021

Use of Optimised LSTM Neural Networks Pre-Trained With Synthetic Data to Estimate PV Generation

Optimising the use of the photovoltaic (PV) energy is essential to reduce fossil fuel emissions by increasing the use of solar power generation. In recent years, research has focused on physical simulations or artifical intelligence models attempting to increase the accuracy of PV generation predictions. The use of simulated data as pre-training for deep learning models has increased in different fields. The reasons are the higher efficiency in the subsequent training with real data and the possibility of not having real data available. This work presents a methodology, based on an deep learning model optimised with specific techniques and pre-trained with synthetic data, to estimate the generation of a PV system. A case study of a photovoltaic installation with 296 PV panels located in northwest Spain is presented. The results show that the model with proper pre-training trains six to seven times faster than a model without pre-training and three to four times faster than a model pre-trained with non-accurate simulated data. In terms of accuracy and considering a homogeneous training process, all models obtained average relative errors around 12%, except the model with incorrect pre-training which performs worse

Improving the calibration of building simulation with interpolated weather datasets

Manuscrito aceptado[Abstract]: The building sector offers huge potential for energy savings, which helps to achieve environmental objectives and social benefits. A good approach to determine both the energy consumption of new buildings and the energetic refurbishment of existing buildings is through thermal simulation. This paper studies how building energy simulation calibration can be improved using interpolated weather data to determine on-site meteorological parameters at the building location. The lack of precise meteorological data in the exact location of buildings means that data from nearby stations is generally used, not knowing how far the error spreads in the results of heating demands and loads. The novelty of this paper lies in the analysis of error propagation to the results of demands and loads of thermal simulation, as well as in the method used to reduce these errors by TPS interpolation. As an interesting conclusion, the average (CV(RMSE)) obtained in the simulation of the studied building, placed successively in each one of the 70 meteorological station locations, decreases from 74% when using the nearest neighborhood to each site to 26% using the TPS interpolation technique. The error in the building simulations is almost three times lower using the studied method.We would like to thank for the meteorological database to Spanish State Meteorological Agency (AEMET). This investigation article was partially supported by the Spanish Government (Project: ENE2015-65999-C2-1-R). This investigation article was partially supported by the Spanish Government (Economy and Competitiveness Spanish Ministry), through the CDTI center (Industrial Technology Development Centre), and European FEDER 2007 - 2013 Technological Fund (European Regional Development Fund) (Project: IDI-20150503)

Generation of BIM data based on the automatic detection, identification and localization of lamps in buildings

In this paper we introduce a method that supports the detection, identification and localization of lamps in a building, with the main goal of automatically feeding its energy model by means of Building Information Modeling (BIM) methods. The proposed method, thus, provides useful information to apply energy-saving strategies to reduce energy consumption in the building sector through the correct management of the lighting infrastructure. Based on the unique geometry and brightness of lamps and the use of only greyscale images, our methodology is able to obtain accurate results despite its low computational needs, resulting in near-real-time processing. The main novelty is that the focus of the candidate search is not over the entire image but instead only on a limited region that summarizes the specific characteristics of the lamp. The information obtained from our approach was used on the Green Building XML Schema to illustrate the automatic generation of BIM data from the results of the algorithm.Comment: 12 pages, 19 figures, journa

Use of a numerical weather prediction model as a meteorological source for building thermal simulations

This version of the article has been accepted for publication, after peer review, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1016/j.scs.2020.102403[Abstract]: Thermal simulations are a commonly used tool for energy efficiency analysis of buildings. Regional meteorological station networks are a prime source of weather data inputs, required for building thermal simulations. However, local measurements from weather stations are not always available, and when available, access to data may be expensive. This paper analysed a novel use of a numerical weather prediction mesoscale model, the Global Forecast System (GFS) sflux model, as a source of input data for transient thermal simulations. Two interpolation techniques (nearest neighbour and universal kriging) were used to generate local weather datasets from GFS outputs at 27 locations spread over an area of 29,574 km2 in Galicia (northwest Spain). The performance of the GFS estimations was tested against weather measurements obtained from a government weather agency. A representative building with the most common features was selected for running thermal simulations in the TRNSYS environment, focused on heating demands, with estimated weather data as the input. The results highlighted that GFS-interpolated datasets consistently performed better than using measured data from the nearest weather station. GFS was found to be an appropriate weather source for building simulations and was able to provide good-quality, free and global-scale local weather inputs.Xunta de Galicia; TOPACIO IN852A 2018/37Universidade de Vigo; 00VI 131H 641.02Xunta de Galicia; TOPACIO IN852A 2018/37This investigation article was partially supported by the University of Vigo through the grant Convocatoria de Axudas á Investigación 2018: Axudas Predoutorais UVigo 2018 (grant number 00VI 131H 641.02). This investigation article was also partially supported by the Galician Government by means of the Xunta de Galicia CONECTA PEME 2018 (Project: TOPACIO IN852A 2018/37). This paper was carried out in the framework of the GIS-Based Infrastructure Management System for Optimized Response to Extreme Events of Terrestrial Transport Networks (SAFEWAY) project, which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 769255. Neither the Innovation and Networks Executive Agency (INEA) nor the European Commission is in any way responsible for any use that may be made of the information it contain