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.
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The climate crisis is an urgent and complex global challenge, requiring transformative action from diverse stakeholders, including governments, civil society, and grassroots movements. Conventional top-down approaches to climate governance have proven insufficient (e.g. UNFCCC, COP events), necessitating a shift towards more inclusive and polycentric models that incorporate the perspectives and needs of diverse communities (Bliznetskaya, 2023; Dorsch & Flachsland, 2017). The independent, multidisciplinary approach of citizen-led activist groups can provide new insights and redefine challenges and opportunities for climate governance and regulation. Despite their important role in developing effective climate action, these citizen-led groups often face significant barriers to decision-making participation, including structural, practical, and legal challenges (Berry et al., 2019; Colli, 2021; Marquardt et al., 2022; Tayler & Schulte, 2019).
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The period leading to and immediately after the release of the IPCC's fifth series of climate change assessments saw substantial efforts by climate change denial interests to portray anthropogenic climate change (ACC) as either unproven theory or a negligible contribution to natural climate variability, including the relationship between tourism and climate change. This paper responds to those claims by stressing that the extent of scientific consensus suggests that human-induced warming of the climate system is unequivocal. Second, it responds in the context of tourism research and ACC, highlighting tourism's significant contribution to greenhouse gas emissions, as well as climate change's potential impacts on tourism at different scales. The paper exposes the tactics used in ACC denial papers to question climate change science by referring to non-peer-reviewed literature, outlier studies, and misinterpretation of research, as well as potential links to think tanks and interest groups. The paper concludes that climate change science does need to improve its communication strategies but that the world-view of some individuals and interests likely precludes acceptance. The connection between ACC and sustainability illustrates the need for debate on adaptation and mitigation strategies, but that debate needs to be grounded in scientific principles not unsupported skepticism.
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This booklet presents sixteen 'practice briefs' which are popular publications based on 12 Master and one Bachelor theses of Van Hall Larenstein University of Applied Sciences (VHL). All theses were commissioned through the research project entitled 'Inclusive and climate smart business models in Ethiopian and Kenyan dairy value chains (CSDEK)'. The objective of this research is to identify scalable, climate smart dairy business models in the context of the ongoing transformation from informal to formal dairy chains in Kenya and Ethiopia.
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This paper introduces and contextualises Climate Futures, an experiment in which AI was repurposed as a ‘co-author’ of climate stories and a co-designer of climate-related images that facilitate reflections on present and future(s) of living with climate change. It converses with histories of writing and computation, including surrealistic ‘algorithmic writing’, recombinatory poems and ‘electronic literature’. At the core lies a reflection about how machine learning’s associative, predictive and regenerative capacities can be employed in playful, critical and contemplative goals. Our goal is not automating writing (as in product-oriented applications of AI). Instead, as poet Charles Hartman argues, ‘the question isn’t exactly whether a poet or a computer writes the poem, but what kinds of collaboration might be interesting’ (1996, p. 5). STS scholars critique labs as future-making sites and machine learning modelling practices and, for example, describe them also as fictions. Building on these critiques and in line with ‘critical technical practice’ (Agre, 1997), we embed our critique of ‘making the future’ in how we employ machine learning to design a tool for looking ahead and telling stories on life with climate change. This has involved engaging with climate narratives and machine learning from the critical and practical perspectives of artistic research. We trained machine learning algorithms (i.e. GPT-2 and AttnGAN) using climate fiction novels (as a dataset of cultural imaginaries of the future). We prompted them to produce new climate fiction stories and images, which we edited to create a tarot-like deck and a story-book, thus also playfully engaging with machine learning’s predictive associations. The tarot deck is designed to facilitate conversations about climate change. How to imagine the future beyond scenarios of resilience and the dystopian? How to aid our transition into different ways of caring for the planet and each other?
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This magazine presents the highlights of the applied research project “Inclusive and climate-smart business models in Ethiopian and Kenyan dairy valuechains (CSDEK)”. The CSDEK applied research project was conducted in six case study areas, three in Ethiopia and three in Kenya. At the time of publishing this magazine, research was still ongoing in some of the study areas. The projectteam and researchers hope to contribute to creating awareness of climatesmartdairy practices and development of the dairy sector in Ethiopia and Kenya. In two of the study areas, collaboration between VHL and dairy stakeholders will continue, preferably through local networks in a Living Lab approach.
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We summarize what we assess as the past year's most important findings within climate change research: limits to adaptation, vulnerability hotspots, new threats coming from the climate–health nexus, climate (im)mobility and security, sustainable practices for land use and finance, losses and damages, inclusive societal climate decisions and ways to overcome structural barriers to accelerate mitigation and limit global warming to below 2°C.
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Both climate change and human activity are the important drivers that can change hydrological cycle routs and affect the features of hydrological drought in river basins. The current study selects the Zayandeh Rud river Basin as a case study region in which to evaluate the influences of climate alteration and human activity on meteorological and hydrological drought based on the Standardized Precipitation Index (SPI) and Standardized Runoff Index (SRI) on different time scales. The generated local climatic data of future years (2006–2040), (2041–2075) and (2076–2100) under the severest scenario (RCP 8.5) from the CMIP5 climate model are selected and used for the hydrology model and water allocation model of WEAP to construct hydrological drought which also consider human activities. The results indicate that significant meteorological drought is expected to occur in the winter and spring months of January to June. However, the driest month for hydrological drought is in the summer and autumn (July to December) (e.g. no changes in seasonality of droughts compared to historic period). It is concluded that, in the results of this work, the human influences on projected hydrological drought have been outlined; they had been missed in many projections for future hydrological drought. However, this study confirms the previous study (Bierkens et al. 2012) which mentioned that human influences can account for future hydrological drought in areas of Asia, the Middle East and the Mediterranean. The results attained in this study are beneficial for examining how hydrological drought characterizations respond to climate alteration and human activity on several time scales, thereby providing scientific information for drought predicting and water resources management over various time scales under non-stationary circumstances.
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Purpose As a step toward more firmly establishing factors to promote retention among younger employees in the hospitality industry, this study aims to focuses on fun in the workplace (fun activities, manager support for fun and coworker socializing) and training climate (organizational support, manager support and job support) as potential antecedents of turnover in a European context. Design/methodology/approach Logistic regression was used to analyze the impact of fun and training climate on turnover with a sample of 902 employees from Belgium, Germany and The Netherlands. Data on fun and training climate were obtained through surveys, which were paired with turnover data from organizational records. Findings With respect to fun in the workplace, group-level manager support for fun and coworker socializing were significantly related to turnover, but not fun activities. With respect to training climate, individual-level job support was significantly related to turnover, but not organizational support and manager support. Research limitations/implications As the data were obtained from employees from one organization, further research would be valuable with additional samples to substantiate the generalizability of the results. Practical implications Given the challenge of turnover, organizations should foster informal aspects of fun in the workplace and learning opportunities to promote retention. Originality/value The study examined the fun–turnover relationship in a context outside of the USA where previous fun–turnover research has been conducted, and it examined fun relative to training climate, which has not been studied heretofore. This study also investigated group- and individual-level effects of both fun and training climate on turnover.
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This report describes the Utrecht regio with regard to sustainability and circular business models.
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