Modeling the Searching Behavior of Social Monkeys

We discuss various features of the trajectories of spider monkeys looking for food in a tropical forest, as observed recently in an extensive {\it in situ} study. Some of the features observed can be interpreted as the result of social interactions. In addition, a simple model of deterministic walk in a random environment reproduces the observed angular correlations between successive steps, and in some cases, the emergence of L\'evy distributions for the length of the steps.Comment: 7 pages, 3 figure

Validation of calibrated energy models: Common errors

Nowadays, there is growing interest in all the smart technologies that provide us with information and knowledge about the human environment. In the energy ¿eld, thanks to the amount of data received from smart meters and devices and the progress made in both energy software and computers, the quality of energy models is gradually improving and, hence, also the suitability of Energy Conservation Measures (ECMs). For this reason, the measurement of the accuracy of building energy models is an important task, because once the model is validated through a calibration procedure, it can be used, for example, to apply and study different strategies to reduce its energy consumption in maintaining human comfort. There are several agencies that have developed guidelines and methodologies to establish a measure of the accuracy of these models, and the most widely recognized are: ASHRAE Guideline 14-2014, the International Performance Measurement and Veri¿cation Protocol (IPMVP) and the Federal Energy Management Program (FEMP). This article intends to shed light on these validation measurements (uncertainty indices) by focusing on the typical mistakes made, as these errors could produce a false belief that the models used are calibrated

Towards a new generation of building envelope calibration

Building energy performance (BEP) is an ongoing point of reflection among researchers and practitioners. The importance of buildings as one of the largest activators in climate change mitigation was illustrated recently at the United Nations Framework Convention on Climate Change 21st Conference of the Parties (COP21). Continuous technological improvements make it necessary to revise the methodology for energy calculations in buildings, as has recently happened with the new international standard ISO 52016-1 on Energy Performance of Buildings. In this area, there is a growing need for advanced tools like building energy models (BEMs). BEMs should play an important role in this process, but until now there has no been international consensus on how these models should reconcile the gap between measurement and simulated data in order to make them more reliable and affordable. Our proposal is a new generation of models that reconcile the traditional data-driven (inverse) modelling and law-driven (forward) modelling in a single type that we have called law-data-driven models. This achievement has greatly simpli¿ed past methodologies, and is a step forward in the search for a standard in the process of calibrating a building energy model

Lasso Estimation of an Interval-Valued Multiple Regression Model

A multiple interval-valued linear regression model considering all the cross-relationships between the mids and spreads of the intervals has been introduced recently. A least-squares estimation of the regression parameters has been carried out by transforming a quadratic optimization problem with inequality constraints into a linear complementary problem and using Lemke's algorithm to solve it. Due to the irrelevance of certain cross-relationships, an alternative estimation process, the LASSO (Least Absolut Shrinkage and Selection Operator), is developed. A comparative study showing the differences between the proposed estimators is provided

Are Migrants Selected on Motivational Orientations? Selectivity Patterns amongst International Migrants in Europe

Migration scholars often assume migrants are the most ambitious and motivated individuals of their home countries. Yet research on motivational selectivity is scant. We present the first systematic cross-national analysis of migrants' selectivity on achievement-related motivational orientations (ARMOs). We measure ARMOs using a validated scale that combines orientations towards socio-economic success, risk, and money. Matching the European Social Survey and the World Value Survey cumulative data sets, we examine whether international migrants recently arrived in Europe are more achievement-oriented than those observational equivalents that do not migrate. We focus on migrants from nine different origins (France, Germany, the United Kingdom, Poland, Romania, Turkey, Morocco, Brazil, and Andean countries) sampled at different European destinations varying in gross domestic product, type of welfare state, and linguistic distance. Our findings seem to contradict the arguments about a common migrant personality put forward by social psychologists, as well as most of the predictions of standard economic models. We do find, however, some support for the welfare magnet hypothesis, as well as for the expectation that gender traditionalism favours negative selectivity of migrant women. We show that reported estimates are not driven by educational selectivity and are unlikely to be biased by destination effects.This study received financial support from the following two projects: Growth, Equal Opportunities, Migration, and Markets, GEMM, funded by the European Commission Horizon 2020 programme (ID 649255), and New Approaches to Immigration Research, NewAIR, funded by the Spanish Ministry of Economy and Competitiveness (CSO2016-78452)

El patrimonio cultural como oferta complementaria al turismo de sol y playa. El caso del sudeste Bonaerense. Argentina

El presente artículo plantea la necesidad de complementar el turismo tradicional de sol y playa con el turismo cultural, generando nuevos productos a partir del patrimonio cultural. Esto puede permitir desestacionalizar al primero y potenciar el crecimiento del segundo, ampliando regionalmente los beneficios del turismo. Este marco general es aplicado a la región sudeste de la Provincia de Buenos Aires, en la República Argentina, siendo una problemática a resolver en muchos otros espacios turísticos del mundo.The present article raises the need to complement the traditional tourism of the sun and beach with the cultural tourism, generating new products from the cultural heritage. This can allow desestacionalizar the first one and to promote the growth of the second one, extending regionally the benefits of the tourism. This general frame is applied to the region southeast of the Province of Buenos Aires, in the Republic Argentina, being a problematics to resolving in many other tourist spaces of the world

El crecimiento urbano de Tandil: ¿modelo territorial de la ciudad difusa?

Tandil, es una ciudad intermedia de la Provincia de Buenos Aires que ha crecido de forma continua y heterogénea. Este crecimiento de los últimos 50 años refleja la expansión sobre distintas áreas. En las últimas dos décadas se observa un mayor crecimiento sobre los faldeos serranos de forma dispersa o en fajas. Estos elementos permiten considerar la presencia de un modelo Urban Sprawl, de ciudad difusa o dispersa. Patrones territoriales, de conectividad, densidad o concentración pueden indicar, a priori, un tipo de crecimiento que consume mucho espacio. Es el producto de un uso espontáneo del territorio a partir de una fuerte especulación inmobiliaria. Pero también es el resultado de una planificación que permite la diferenciación espacial entre el Norte y el Sur de la ciudad.

Probabilistic load forecasting for building energy models

In the current energy context of intelligent buildings and smart grids, the use of load forecasting to predict future building energy performance is becoming increasingly relevant. The prediction accuracy is directly influenced by input uncertainties such as the weather forecast, and its impact must be considered. Traditional load forecasting provides a single expected value for the predicted load and cannot properly incorporate the effect of these uncertainties. This research presents a methodology that calculates the probabilistic load forecast while accounting for the inherent uncertainty in forecast weather data. In the recent years, the probabilistic load forecasting approach has increased in importance in the literature but it is mostly focused on black-box models which do not allow performance evaluation of specific components of envelope, HVAC systems, etc. This research fills this gap using a white-box model, a building energy model (BEM) developed in EnergyPlus, to provide the probabilistic load forecast. Through a Gaussian kernel density estimation (KDE), the procedure converts the point load forecast provided by the BEM into a probabilistic load forecast based on historical data, which is provided by the building’s indoor and outdoor monitoring system. An hourly map of the uncertainty of the load forecast due to the weather forecast is generated with different prediction intervals. The map provides an overview of different prediction intervals for each hour, along with the probability that the load forecast error is less than a certain value. This map can then be applied to the forecast load that is provided by the BEM by applying the prediction intervals with their associated probabilities to its outputs. The methodology was implemented and evaluated in a real school building in Denmark. The results show that the percentage of the real values that are covered by the prediction intervals for the testing month is greater than the confidence level (80%), even when a small amount of data are used for the creation of the uncertainty map; therefore, the proposed method is appropriate for predicting the probabilistic expected error in load forecasting due to the use of weather forecast data

Ground characterization of building energy models

The calibration of building energy models is crucial for their use in some applications that depend on their accuracy for adequate performance, such as demand response and model predictive control (MPC). In general, energy models offer many possibilities/strategies when characterizing a construction system, and such a characterization is key when analyzing both its thermal behavior and its energy impact. This research analyzes the different ways to characterize the thermal interaction of the building energy model (BEM) with the ground, comparing conventional approaches with new approaches based on both optimization of the former and dynamic ground characterizations. Using a model adjusted to a real case study, each of the existing options are analyzed, in which a different control of the ground temperature both in terms of its temporal oscillation and its location in the building (based on thermal zones) is taken into account. Exhaustive monitoring of a real building and measuring the ground and ground floor surface temperatures have made establishing which EnergyPlus components/objects best characterize the ground-slab interaction possible, both in terms of the simplicity of modeling and the cost (economic and technical) required for each of them. As will be seen, there are objects with an excellent cost/effectiveness ratio when characterizing the groun