Zoekresultaten

Final report - Leicester City Hall Operational Pilot

A large council building in Leicester, its central HQ called City Hall, aims to link on site renewable energy (PV) generation to electric vehicles (EVs) used by the Council staff. Leicester City Hall based staff are utilising four EVs for their work and charging these, when possible, from local renewable energy (PV) generation. This study presents the analysis of the use of four such EVs and their charging profiles that take place at the City Hall.

Increasing the attractiveness of district heating networks to consumers

This report has been established within the Flexiheat project. Flexiheat has focused on increasing flexibility in district heating systems. The intelligent district heating network is a dynamic network: an open network where different waste heat and renewable energy sources are connected, that has multiple producers and groups of consumers and facilitates the connection between different energy infrastructures (gas, heat and electricity). Eventually this will lead to an optimal deployment of the available heat sources and an increased cost-efficiency of district heating. Flexiheat aims to develop new concepts for these intelligent, flexible district heating networks. One of the strategies is to allow third party access to the network. A smart control system is developed to manage the heat flows across the network. This system makes use of dynamic pricing. In this exploration the concept of third party access in relation to the Flexiheat project will be discussed. The development of new business and price models based on the Flexiheat approach has led to an analysis of possible alternative price models for consumers.

Prediction of Medical Outcomes with Modern Modelling Techniques

Het doel van dit onderzoek is te onderzoeken onder welke omstandigheden en onder welke condities relatief moderne modelleringstechnieken zoals support vector machines, neural networks en random forests voordelen zouden kunnen hebben in medisch-wetenschappelijk onderzoek en in de medische praktijk in vergelijking met meer traditionele modelleringstechnieken, zoals lineaire regressie, logistische regressie en Cox regressie.

Charging infrastructure roll-out strategies for large scale introduction of electric vehicles in urban areas

On the eve of the large-scale introduction of electric vehicles, policy makers have to decide on how to organise a significant growth in charging infrastructure to meet demand. There is uncertainty about which charging deployment tactic to follow. The main issue is how many of charging stations, of which type, should be installed and where. Early roll-out has been successful in many places, but knowledge on how to plan a large-scale charging network in urban areas is missing. Little is known about return to scale effects, reciprocal effects of charger availability on sales, and the impact of fast charging or more clustered charging hubs on charging preferences of EV owners. This paper explores the effects of various roll-out strategies for charging infrastructure that facilitate the large-scale introduction of EVs, using agent-based simulation. In contrast to previously proposed models, our model is rooted in empirically observed charging patterns from EVs instead of travel patterns of fossil fuelled cars. In addition, the simulation incorporates different user types (inhabitants, visitors, taxis and shared vehicles) to model the diversity of charging behaviours in an urban environment. Different scenarios are explored along the lines of the type of charging infrastructure (level 2, clustered level 2, fast charging) and the intensity of rollout (EV to charging point ratio). The simulation predicts both the success rate of charging attempts and the additional discomfort when searching for a charging station. Results suggest that return to scale and reciprocal effects in charging infrastructure are considerable, resulting in a lower EV to charging station ratio on the longer term.

EIT Urban Mobility Knowledge base of innovative solutions in urban mobility and living labs : final report

Capturing the well-being exposome in poly-environmental scores.

In this study we use aggregated weighted scores of environmental effects to study environmental influences on well-being and happiness. To this end, we split a sample of Netherlands Twin Register (NTR) participants into a training (N =4857) and test (N =2077) sample. In the training sample, we use elastic net regression to estimate effect sizes for associations between life satisfaction and two sets of environmental variables: one based on self- report socioenvironmental data, and one based on objective physical environmental data. Based on these effect sizes, we create two poly-environmental scores (PES-S and PES-O, for self-reports and objective data respectively). In the test sample, we perform association analyses between different measures of well-being and the two PESs. We find that the PES-S explains ~36% of the variance in well-being, while the PES-O does not significantly contribute to the model. Variance in other well-being measures (i.e., different life satisfaction domains, subjective happiness, quality of life, flourishing, psychological well-being, self-rated health, depressive problems, and loneliness) are explained to varying extents by the PESs, ranging from 6.36% (self-rated health) to 36.66% (loneliness). These predictive values did not change during the COVID-19 pandemic (N =3214). Validating the PES-S in the UK biobank (N =40,614), we find that the UK biobank PES-S explains about ~12% of the variance in happiness. Lastly, we examine if there is any indication for gene-environment correlation (rGE), the phenomenon where one’s genetic predisposition influences exposure to the environment, by associating the PESs with polygenic scores (PGS) in a sample of Netherlands Twin Register (NTR) and UK Biobank participants. While the PES and PGS were not correlated in the NTR sample, they were correlated in the larger UK biobank sample, indicating the potential presence of rGE. We discuss several limitations pertaining to our dataset, such as a potential influence of common method bias, and reflect on how PESs might be used in future research.