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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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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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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Climate change adaptation has influenced river management through an anticipatory governance paradigm. As such, futures and the power of knowing the future has become increasingly influential in water management. Yet, multiple future imaginaries co-exist, where some are more dominant that others. In this PhD research, I focus on deconstructing the future making process in climate change adaptation by asking ‘What river imaginaries exist and what future imaginaries dominate climate change adaptation in riverine infrastructure projects of the Meuse and Magdalena river?’. I firstly explore existing river imaginaries in a case study of the river Meuse. Secondly, I explore imaginaries as materialised in numerical models for the Meuse and Magdalena river. Thirdly, I explore the integration and negotiation of imaginaries in participatory modelling practices in the Magdalena river. Fourthly, I explore contesting and alternative imaginaries and look at how these are mobilised in climate change adaptation for the Magdalena and Meuse river. Multiple concepts stemming from Science and Technology Studies and Political Ecology will guide me to theorise the case study findings. Finally, I reflect on my own positionality in action-research which will be an iterative process of learning and unlearning while navigating between the natural and social sciences.
Climate change is one of the most critical global challenges nowadays. Increasing atmospheric CO2 concentration brought by anthropogenic emissions has been recognized as the primary driver of global warming. Therefore, currently, there is a strong demand within the chemical and chemical technology industry for systems that can covert, capture and reuse/recover CO2. Few examples can be seen in the literature: Hamelers et al (2013) presented systems that can use CO2 aqueous solutions to produce energy using electrochemical cells with porous electrodes; Legrand et al (2018) has proven that CDI can be used to capture CO2 without solvents; Shu et al (2020) have used electrochemical systems to desorb (recover) CO2 from an alkaline absorbent with low energy demand. Even though many efforts have been done, there is still demand for efficient and market-ready systems, especially related to solvent-free CO2 capturing systems. This project intends to assess a relatively efficient technology, with low-energy costs which can change the CO2 capturing market. This technology is called whorlpipe. The whorlpipe, developed by Viktor Schauberger, has shown already promising results in reducing the energy and CO2 emissions for water pumping. Recently, studies conducted by Wetsus and NHL Stenden (under submission), in combination with different companies (also members in this proposal) have shown that vortices like systems, like the Schauberger funnel, and thus “whorlpipe”, can be fluid dynamically represented using Taylor-Couette flows. This means that such systems have a strong tendency to form vortices like fluid-patterns close to their air-water interface. Such flow system drastically increase advection. Combined with their higher area to volume ratio, which increases diffusion, these systems can greatly enhance gas capturing (in liquids), and are, thus, a unique opportunity for CO2 uptake from the air, i.e. competing with systems like conventional scrubbers or bubble-based aeration.
Family Dairy Tech Sustainable and affordable stable management systems for family dairy farms in India. An example of Dutch technology that is useful to an ?emerging economy?. Summary Problem The demand for dairy products in India is increasing. Small and medium-sized family farmers want to capitalize on this development and the Indian government wants to support them. Dutch companies offer knowledge and a wide range of products and services to improve dairy housing systems and better milk quality, in which India is interested. However, the Dutch technology is sophisticated and expensive. For a successful entry into this market, entrepreneurs have to develop affordable and robust (?frugal?) systems and products adapted to the Indian climate and market conditions. The external question is therefore: ?How can Dutch companies specialised on dairy housing systems adapt their products and offer these on the Indian market to contribute to sustainable and profitable local dairy farming??. Goal Since 2011, VHL University of Applied Sciences (VHL) is collaborating with a college and an agricultural information center Krishi Vigyan Kendra (KVK), Baramati, Pune district, Maharashtra State India. In this region many small-scale dairy farmers are active. Within this project, KVK wants to support farmers to scale up their farm form one or a few cows up to 15 to 100 cows, with a better milk quality. In this innovative project, VHL and Saxion Universities of Applied Sciences, in collaboration with KVK and several Dutch companies want to develop integrated solutions for the growing number of dairy farms in the State of Maharashtra, India. The research questions are: 1. "How can, by smart combinations of existing and new technologies, the cow-varieties and milk- and stable-management systems in Baramati, India, for family farmers be optimized in an affordable and sustainable way?" 2. "What are potential markets in India for Dutch companies in the field of stable management and which innovative business models can support entering this market?" Results The intended results are: 1. A design of an integral stable management system for small and medium-sized dairy farms in India, composed of modified Dutch technologies. 2. A cattle improvement programme for robust cows that are adapted to the conditions of Maharashtra. 3. An advice to Dutch entrepreneurs how to develop their market position in India for their technologies. 4. An advice to Indian family farmers how they can increase their margins in a sustainable way by employing innovative technologies.
