Alfonso P. Ramallo-González

Adult sex ratio and operational sex ratio exhibit different temporal dynamics in the wild

Lay Summary Sex ratios vary within wild populations, but these variations and the relationship of different sex ratio indices are rarely explored. Using data from a well-monitored polygamous bird population, we show that adult sex ratio (ASR, proportion of males in adult population) and operational sex ratio (OSR, ratio of sexually active males to females) fluctuate over the breeding season and these fluctuations are not correlated, suggesting that OSR is a poor predictor of ASR under polygamy.

A Review of Current and Future Weather Data for Building Simulation

This article provides the first comprehensive assessment of methods for the creation of weather variables for use in building simulation. We undertake a critical analysis of the fundamental issues and limitations of each methodology and discusses new challenges, such as how to deal with uncertainty, the urban heat island, climate change and extreme events. Proposals for the next generation of weather files for building simulation are made based on this analysis. A seven-point list of requirements for weather files is introduced and the state-of-the-art compared to this via a mapping exercise. It is found that there are various issues with all current and suggested approaches, but the two areas most requiring attention are the production of weather files for the urban landscape and files specifically designed to test buildings against the criteria of morbidity, mortality and building services system failure. Practical application: Robust weather files are key to the design of sustainable, healthy and comfortable buildings. This article provides the first comprehensive assessment of their technical requirements to ensure buildings perform well in both current and future climates.

An update of the UK’s test reference year: The implications of a revised climate on building design

Average weather years have been used around the world for testing buildings to ascertain their likely energy use using thermal modelling software. In the UK, the Test Reference Years which are in current use were released in 2006 but generally consisted of data from 1983 to 2004. In this work, revised test reference years will be proposed which are based on a new climatic period from 1984 to 2013. The differences between the two years will be highlighted and the implications for building design will be discussed. Practical application : Test Reference years are integral to building design to assess the performance of buildings at design stage. Specifically, they are used to assess energy use in buildings as well as for compliance purposes with Part L of the Building Regulations.

An open IoT platform for the management and analysis of energy data

Abstract Buildings are key players when looking at end-use energy demand. It is for this reason that during the last few years, the Internet of Things (IoT) has been considered as a tool that could bring great opportunities for energy reduction via the accurate monitoring and control of a large variety of energy-related agents in buildings. However, there is a lack of IoT platforms specifically oriented towards the proper processing, management and analysis of such large and diverse data. In this context, we put forward in this paper the IoT Energy Platform (IoTEP) which attempts to provide the first holistic solution for the management of IoT energy data. The platform we show here (that has been based on FIWARE) is suitable to include several functionalities and features that are key when dealing with energy quality insurance and support for data analytics. As part of this work, we have tested the platform IoTEP with a real use case that includes data and information from three buildings totalizing hundreds of sensors. The platform has exceed expectations proving robust, plastic and versatile for the application at hand.

Overheating in vulnerable and non-vulnerable households

ABSTRACT As the 2003 European heatwave demonstrated, overheating in homes can cause wide-scale fatalities. With temperatures and heatwave frequency predicted to increase due to climate change, such events can be expected to become more common. Thus, investigating the risk of overheating in buildings is key to understanding the scale of the problem and in designing solutions. Most work on this topic has been theoretical and based on lightweight dwellings that might be expected to overheat. By contrast, this study collects temperature and air quality data over two years for vulnerable and non-vulnerable UK homes where overheating would not be expected to be common. Overheating was found to occur, particularly and disproportionately in households with vulnerable occupants. As the summers in question were not extreme and contained no prolonged heatwaves, this is a significant and worrying finding. The vulnerable homes were also found to have worse indoor air quality. This suggests that some of the problem might be solved by enhancing indoor ventilation. The collected thermal comfort survey data were also validated against the European adaptive model. Results suggest that the model underestimates discomfort in warm conditions, having implications for both vulnerable and non-vulnerable homes.

The reliability of inverse modelling for the wide scale characterization of the thermal properties of buildings

The reduction of energy use in buildings is a major component of greenhouse gas mitigation policy and requires knowledge of the fabric and the occupant behaviour. Hence there has been a longstanding desire to use automatic means to identify these. Smart metres and the internet-of-things have the potential to do this. This paper describes a study where the ability of inverse modelling to identify building parameters is evaluated for 6 monitored real and 1000 simulated buildings. It was found that low-order models provide good estimates of heat transfer coefficients and internal temperatures if heating, electricity use and CO2 concentration are measured during the winter period. This implies that the method could be used with a small number of cheap sensors and enable the accurate assessment of buildings’ thermal properties, and therefore the impact of any suggested retrofit. This has the potential to be transformative for the energy efficiency industry.

Towards a Cognitive Agent-based Model for Air Conditioners Purchasing Prediction

Abstract Climate change as a result of human activities is a problem of a paramount importance. The global temperature on Earth is gradually increasing and it may lead to substantially hotter summers in a moderate belt of Europe, which in turn is likely to influence the air conditioning penetration in this region. The current work is an attempt to predict air conditioning penetration in different residential areas in the UK between 2030-2090 using an integration of calibrated building models, future weather predictions and an agent-based model. Simulation results suggest that up to 12% of homes would install an air conditioner in 75 years’ time assuming an average purchasing ability of the households. The performed simulations provide more insight into the influence of overheating intensity along with households’ purchasing ability and social norms upon households’ decisions to purchase an air conditioner.