Psychologists, psycholinguists, and other researchers using language stimuli have been struggling for more than 30 years with the problem of how to analyze experimental data that contain two crossed random effects (items and participants). The classical analysis of variance does not apply; alternatives have been proposed but have failed to catch on, and a statistically unsatisfactory procedure of using two approximations (known as F 1 and F 2) has become the standard. A simple and elegant solution using mixed model analysis has been available for 15 years, and recent improvements in statistical software have made mixed models analysis widely available. The aim of this article is to increase the use of mixed models by giving a concise practical introduction and by giving clear directions for undertaking the analysis in the most popular statistical packages. The article also introduces the djmixed add-on package for SPSS, which makes entering the models and reporting their results as straightforward as possible.
MULTIFILE
The built environment requires energy-flexible buildings to reduce energy peak loads and to maximize the use of (decentralized) renewable energy sources. The challenge is to arrive at smart control strategies that respond to the increasing variations in both the energy demand as well as the variable energy supply. This enables grid integration in existing energy networks with limited capacity and maximises use of decentralized sustainable generation. Buildings can play a key role in the optimization of the grid capacity by applying demand-side management control. To adjust the grid energy demand profile of a building without compromising the user requirements, the building should acquire some energy flexibility capacity. The main ambition of the Brains for Buildings Work Package 2 is to develop smart control strategies that use the operational flexibility of non-residential buildings to minimize energy costs, reduce emissions and avoid spikes in power network load, without compromising comfort levels. To realise this ambition the following key components will be developed within the B4B WP2: (A) Development of open-source HVAC and electric services models, (B) development of energy demand prediction models and (C) development of flexibility management control models. This report describes the developed first two key components, (A) and (B). This report presents different prediction models covering various building components. The models are from three different types: white box models, grey-box models, and black-box models. Each model developed is presented in a different chapter. The chapters start with the goal of the prediction model, followed by the description of the model and the results obtained when applied to a case study. The models developed are two approaches based on white box models (1) White box models based on Modelica libraries for energy prediction of a building and its components and (2) Hybrid predictive digital twin based on white box building models to predict the dynamic energy response of the building and its components. (3) Using CO₂ monitoring data to derive either ventilation flow rate or occupancy. (4) Prediction of the heating demand of a building. (5) Feedforward neural network model to predict the building energy usage and its uncertainty. (6) Prediction of PV solar production. The first model aims to predict the energy use and energy production pattern of different building configurations with open-source software, OpenModelica, and open-source libraries, IBPSA libraries. The white-box model simulation results are used to produce design and control advice for increasing the building energy flexibility. The use of the libraries for making a model has first been tested in a simple residential unit, and now is being tested in a non-residential unit, the Haagse Hogeschool building. The lessons learned show that it is possible to model a building by making use of a combination of libraries, however the development of the model is very time consuming. The test also highlighted the need for defining standard scenarios to test the energy flexibility and the need for a practical visualization if the simulation results are to be used to give advice about potential increase of the energy flexibility. The goal of the hybrid model, which is based on a white based model for the building and systems and a data driven model for user behaviour, is to predict the energy demand and energy supply of a building. The model's application focuses on the use case of the TNO building at Stieltjesweg in Delft during a summer period, with a specific emphasis on cooling demand. Preliminary analysis shows that the monitoring results of the building behaviour is in line with the simulation results. Currently, development is in progress to improve the model predictions by including the solar shading from surrounding buildings, models of automatic shading devices, and model calibration including the energy use of the chiller. The goal of the third model is to derive recent and current ventilation flow rate over time based on monitoring data on CO₂ concentration and occupancy, as well as deriving recent and current occupancy over time, based on monitoring data on CO₂ concentration and ventilation flow rate. The grey-box model used is based on the GEKKO python tool. The model was tested with the data of 6 Windesheim University of Applied Sciences office rooms. The model had low precision deriving the ventilation flow rate, especially at low CO2 concentration rates. The model had a good precision deriving occupancy from CO₂ concentration and ventilation flow rate. Further research is needed to determine if these findings apply in different situations, such as meeting spaces and classrooms. The goal of the fourth chapter is to compare the working of a simplified white box model and black-box model to predict the heating energy use of a building. The aim is to integrate these prediction models in the energy management system of SME buildings. The two models have been tested with data from a residential unit since at the time of the analysis the data of a SME building was not available. The prediction models developed have a low accuracy and in their current form cannot be integrated in an energy management system. In general, black-box model prediction obtained a higher accuracy than the white box model. The goal of the fifth model is to predict the energy use in a building using a black-box model and measure the uncertainty in the prediction. The black-box model is based on a feed-forward neural network. The model has been tested with the data of two buildings: educational and commercial buildings. The strength of the model is in the ensemble prediction and the realization that uncertainty is intrinsically present in the data as an absolute deviation. Using a rolling window technique, the model can predict energy use and uncertainty, incorporating possible building-use changes. The testing in two different cases demonstrates the applicability of the model for different types of buildings. The goal of the sixth and last model developed is to predict the energy production of PV panels in a building with the use of a black-box model. The choice for developing the model of the PV panels is based on the analysis of the main contributors of the peak energy demand and peak energy delivery in the case of the DWA office building. On a fault free test set, the model meets the requirements for a calibrated model according to the FEMP and ASHRAE criteria for the error metrics. According to the IPMVP criteria the model should be improved further. The results of the performance metrics agree in range with values as found in literature. For accurate peak prediction a year of training data is recommended in the given approach without lagged variables. This report presents the results and lessons learned from implementing white-box, grey-box and black-box models to predict energy use and energy production of buildings or of variables directly related to them. Each of the models has its advantages and disadvantages. Further research in this line is needed to develop the potential of this approach.
Abstract: Existing frailty models have enhanced research and practice; however, none of the models accounts for the perspective of older adults upon defining and operationalizing frailty. We aim to propose a mixed conceptual model that builds on the integral model while accounting for older adults’ perceptions and lived experiences of frailty. We conducted a traditional literature review to address frailty attributes, risk factors, consequences, perceptions, and lived experiences of older adults with frailty. Frailty attributes are vulnerability/susceptibility, aging, dynamic, complex, physical, psychological, and social. Frailty perceptions and lived experience themes/subthemes are refusing frailty labeling, being labeled “by others” as compared to “self-labeling”, from the perception of being frail towards acting as being frail, positive self-image, skepticism about frailty screening, communicating the term “frail”, and negative and positive impacts and experiences of frailty. Frailty risk factors are classified into socio-demographic, biological, physical, psychological/cognitive, behavioral, and situational/environmental factors. The consequences of frailty affect the individual, the caregiver/family, the healthcare sector, and society. The mixed conceptual model of frailty consists of interacting risk factors, interacting attributes surrounded by the older adult’s perception and lived experience, and interacting consequences at multiple levels. The mixed conceptual model provides a lens to qualify frailty in addition to quantifying it.
Positive Energy Districts (PEDs) can play an important part in the energy transition by providing a year-round net positive energy balance in urban areas. In creating PEDs, new challenges emerge for decision-makers in government, businesses and for the public. This proposal aims to provide replicable strategies for improving the process of creating PEDs with a particular emphasis on stakeholder engagement, and to create replicable innovative business models for flexible energy production, consumption and storage. The project will involve stakeholders from different backgrounds by collaborating with the province, municipalities, network operators, housing associations, businesses and academia to ensure covering all necessary interests and mobilise support for the PED agenda. Two demo sites are part of the consortium to implement the lessons learnt and to bring new insights from practice to the findings of the project work packages. These are 1), Zwette VI, part of the city of Leeuwarden (NL), where local electricity congestion causes delays in building homes and small industries. And 2) Aalborg East (DK), a mixed-use neighbourhood with well-established partnerships between local stakeholders, seeking to implement green energy solutions with ambitions of moving towards net-zero emissions.
The question we have chosen – and been invited – to answer is “What is Europe: Past, Present, and Future.” This sits within the resilient societies theme of the NWA call. The reason for our choice of the ‘resilience’ theme is based on the many disciplines working on the project, which stretch beyond the historic (living history theme) into the societal.It has a deeper conceptual basis, however. It springs from an assumption that a shared sense of belonging and inclusion is one foundation for and aspect of resilience – just as a rope braided together from many strands is stronger than one where the strands are fraying apart. Positive and inclusive expressions of belonging and affiliation are present in education, sports, and music – highly visible sites of representation that have profound reach and impact in society. Racialisation, othering, and selective or stereotypical representations, however, work against resilience. They are circulated widely and generate exclusion and hurt. In these linked work packages, then, we take up the question’s invitation to expand and disrupt, what the NWA’s call itself defines as a normative prior understanding of Europe. In the words of the question, this definition emphasizes Europe’s nature as white, Christian-secular, bounded by the geographic limits of Western Europe, shaped by Greco-Roman heritage and tradition, democratic, and home of the enlightenment. Our consortium seeks to analyze this representation, research and present more expansive and accurate ones in consultative reflective and co-creative processes. Through the process, the new knowledge, and our highly participatory research and dissemination models we will change societal understandings of the bounds of Dutch, and European identities. This will forge a greater sense of belonging across all of the communities, including academia, involved in our project.This project is vital for building resilience through tackling sources of fragmentation and alienation in past and present. It is much needed as we look forward to an increasingly diverse and mixed demographic future.
Logistics companies struggle to keep their supply chain cost-effective, reliable and sustainable, due to changing demand, increasing competition and growing service requirements. To remain competitive, processes must be efficient with low costs. Of the entire supply chain, the first and last mile logistics may be the most difficult aspect due to low volumes, high waiting and shipping times and complex schedules. These inefficiencies account for up to 40% of total transport costs. Connected Automated Transport (CAT) is a technological development that allows for safer, more efficient and cleaner transport, especially for the first- and last-mile. The Connected Automated Driving Roadmap (ERTRAC) states that CAT can revolutionize the way fleets operate. The CATALYST Project (NWO) already shows the advantages of CAT. SAVED builds on several projects and transforms the challenges and solutions that were identified on a strategic level to a tactical and operational (company) level. Despite the high-tech readiness of CAT, commercial acceptance is lacking due to issues regarding profitable integration into existing logistics processes and infrastructures. In-depth research on automated hub-to-hub freight transport is needed, focusing on ideal vehicle characteristics, logistic control of the vehicles (planning, routing, positioning, battery management), control modes (central, decentralized, hybrid), communication modes (vehicle-to-vehicle, vehicle-to-infrastructure) and automation of loading and unloading, followed by the translation of this knowledge into valid business models. Therefore, SAVED focuses on the following question: “How can automated and collaborative hub-to-hub transport be designed, and what is the impact in terms of People, Planet and Profit (PPP) on the logistics value chain of industrial estates of different sizes, layouts and different traffic situations (mixed/unmixed infrastructure)?“ SAVED results in knowledge of the applicability of CAT and the impact on the logistics value chain of various industrial estates, illustrated by two case studies.
