The energy management systems industry in the built environment is currently an important topic. Buildings use about 40% of the total global energy worldwide. Therefore, the energy management system’s sector is one of the most influential sectors to realize changes and transformation of energy use. New data science technologies used in building energy management systems might not only bring many technical challenges, but also they raise significant educational challenges for professionals who work in the field of energy management systems. Learning and educational issues are mainly due to the transformation of professional practices and networks, emerging technologies, and a big shift in how people work, communicate, and share their knowledge across the professional and academic sectors. In this study, we have investigated three different companies active in the building services sector to identify the main motivation and barriers to knowledge adoption, transfer, and exchange between different professionals in the energy management sector and explore the technologies that have been used in this field using the boundary-crossing framework. The results of our study show the importance of understanding professional learning networks in the building services sector. Additionally, the role of learning culture, incentive structure, and technologies behind the educational system of each organization are explained. Boundary-crossing helps to analyze the barriers and challenges in the educational setting and how new educational technologies can be embedded. Based on our results, future studies with a bigger sample and deeper analysis of technologies are needed to have a better understanding of current educational problems.
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Completeness of data is vital for the decision making and forecasting on Building Management Systems (BMS) as missing data can result in biased decision making down the line. This study creates a guideline for imputing the gaps in BMS datasets by comparing four methods: K Nearest Neighbour algorithm (KNN), Recurrent Neural Network (RNN), Hot Deck (HD) and Last Observation Carried Forward (LOCF). The guideline contains the best method per gap size and scales of measurement. The four selected methods are from various backgrounds and are tested on a real BMS and metereological dataset. The focus of this paper is not to impute every cell as accurately as possible but to impute trends back into the missing data. The performance is characterised by a set of criteria in order to allow the user to choose the imputation method best suited for its needs. The criteria are: Variance Error (VE) and Root Mean Squared Error (RMSE). VE has been given more weight as its ability to evaluate the imputed trend is better than RMSE. From preliminary results, it was concluded that the best K‐values for KNN are 5 for the smallest gap and 100 for the larger gaps. Using a genetic algorithm the best RNN architecture for the purpose of this paper was determined to be GatedRecurrent Units (GRU). The comparison was performed using a different training dataset than the imputation dataset. The results show no consistent link between the difference in Kurtosis or Skewness and imputation performance. The results of the experiment concluded that RNN is best for interval data and HD is best for both nominal and ratio data. There was no single method that was best for all gap sizes as it was dependent on the data to be imputed.
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Completeness of data is vital for the decision making and forecasting on Building Management Systems (BMS) as missing data can result in biased decision making down the line. This study creates a guideline for imputing the gaps in BMS datasets by comparing four methods: K Nearest Neighbour algorithm (KNN), Recurrent Neural Network (RNN), Hot Deck (HD) and Last Observation Carried Forward (LOCF). The guideline contains the best method per gap size and scales of measurement. The four selected methods are from various backgrounds and are tested on a real BMS and meteorological dataset. The focus of this paper is not to impute every cell as accurately as possible but to impute trends back into the missing data. The performance is characterised by a set of criteria in order to allow the user to choose the imputation method best suited for its needs. The criteria are: Variance Error (VE) and Root Mean Squared Error (RMSE). VE has been given more weight as its ability to evaluate the imputed trend is better than RMSE. From preliminary results, it was concluded that the best K‐values for KNN are 5 for the smallest gap and 100 for the larger gaps. Using a genetic algorithm the best RNN architecture for the purpose of this paper was determined to be Gated Recurrent Units (GRU). The comparison was performed using a different training dataset than the imputation dataset. The results show no consistent link between the difference in Kurtosis or Skewness and imputation performance. The results of the experiment concluded that RNN is best for interval data and HD is best for both nominal and ratio data. There was no single method that was best for all gap sizes as it was dependent on the data to be imputed.
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INXCES will use and enhance innovative 3D terrain analysis and visualization technology coupled with state-of-the-art satellite remote sensing to develop cost-effective risk assessment tools for urban flooding, aquifer recharge, ground stability and subsidence. INXCES will develop quick scan tools that will help decision makers and other actors to improve the understanding of urban and peri-urban terrains and identify options for cost effective implementation of water management solutions that reduce the negative impacts of extreme events, maximize beneficial uses of rainwater and stormwater for small to intermediate events and provide long-term resilience in light of future climate changes. The INXCES approach optimizes the multiple benefits of urban ecosystems, thereby stimulating widespread implementation of nature-based solutions on the urban catchment scale.INXCES will develop new innovative technological methods for risk assessment and mitigation of extreme hydroclimatic events and optimization of urban water-dependent ecosystem services at the catchment level, for a spectrum of rainfall events. It is widely acknowledged that extreme events such as floods and droughts are an increasing challenge, particularly in urban areas. The frequency and intensity of floods and droughts pose challenges for economic and social development, negatively affecting the quality of life of urban populations. Prevention and mitigation of the consequences of hydroclimatic extreme events are dependent on the time scale. Floods are typically a consequence of intense rainfall events with short duration. In relation to prolonged droughts however, a much slower timescale needs to be considered, connected to groundwater level reductions, desiccation and negative consequences for growing conditions and potential ground – and building stability.INXCES will take a holistic spatial and temporal approach to the urban water balance at a catchment scale and perform technical-scientific research to assess, mitigate and build resilience in cities against extreme hydroclimatic events with nature-based solutions.INXCES will use and enhance innovative 3D terrain analysis and visualization technology coupled with state-of-the-art satellite remote sensing to develop cost-effective risk assessment tools for urban flooding, aquifer recharge, ground stability and subsidence. INXCES will develop quick scan tools that will help decision makers and other actors to improve the understanding of urban and peri-urban terrains and identify options for cost effective implementation of water management solutions that reduce the negative impacts of extreme events, maximize beneficial uses of rainwater and stormwater for small to intermediate events and provide long-term resilience in light of future climate changes. The INXCES approach optimizes the multiple benefits of urban ecosystems, thereby stimulating widespread implementation of nature-based solutions on the urban catchment scale.
Polycotton textiles are fabrics made from cotton and polyester. It is used in many textile applications such as sporting cloths, nursery uniforms and bed sheets. As cotton and polyester are quite different in their polymer nature, polycotton textiles are hard to recycle and therefore mostly incinerated. Incineration of discarded polycotton, and substitution by virgin polycotton, create a significant environmental impact. However, textile manufacturers and brand owners will become obliged to apply recycled content in clothing from 2023 onwards. Therefore, the development of more sustainable recycling alternatives for the separation and purification of polycotton into its monomers and cellulose is vital. In a recently approved GoChem project, it has been shown that cotton can be separated from polyester successfully, using a chemical recycling process. The generated solution is a mixture of suspended and partially decolorized cotton (cellulose) and a liquid fraction produced from the depolymerization of the polyester (monomers). A necessary further step of this work is the investigation of possible separation methods to recover the cotton and purify the obtained polyester monomers into polymer-grade pure products suitable for repolymerization. Repolymerize is a new consortium, composed of the first project members, plus a separation and purification process group, to investigate efficient and high yield purification steps to recover these products. The project will focus on possible steps to separate the suspended fraction (cotton) and further recover of high purity ethylene glycol from the rest fraction (polyester depolymerization solution). The main objective is to create essential knowledge so the private partners can evaluate whether such process is technologically and economically feasible.
Making buildings smarter will save energy and make energy systems more flexible to address grid congestion. This is done by adding smart functionalities (such as machine learning and AI) to existing building management systems and by making full use of building data. Applied research and innovation on smart buildings is urgently needed to evaluate the best smart solutions for buildings applicable to different types of buildings across different contexts, and to assess their costs and benefits. Research on smart buildings, therefore, plays a large role in European, national and regional R&I agenda’s on energy, climate and digitalisation. Amsterdam University of Amsterdam (AUAS) has a growing research group on building energy management and smart buildings, supporting the sustainable transition of its own campus and the Amsterdam region. However, to date, AUAS has not been able to engage in international research projects in this area. Recently, AUAS became a partner in an European University Alliance (U!REKA European University), U!REKA comprises of six universities of applied sciences across Europe with its mission focusing on climate neutral communities and cities. Several partners with U!REKA are also conducting research on smart buildings and smart campuses, but, like AUAS, still in relative isolation. U!REKA will provide the collaboration framework for future joint research to be kick-started by the proposed SIA pilot project. In this research project, AUAS will cooperate with the Technical University Eindhoven, Metropolia University of Applied Sciences (Helsinki) and Politecnico de Lisboa (Lisbon) as consortium partners. Supporting partners are Frankfurt University of Applied Sciences, KTH Royal Institute of Technology (Stockholm) and TVVL (Dutch knowledge platform and association of professionals in the installation sector). The research is based on smart building case studies on the campuses of the project partners.
