Dealing with and maintaining high-quality standards in the design and construction phases is challenging, especially for on-site construction. Issues like improper implementation of building components and poor communication can widen the gap between design specifications and actual conditions. To prevent this, particularly for energy-efficient buildings, it is vital to develop resilient, sustainable strategies. These should optimize resource use, minimize environmental impact, and enhance livability, contributing to carbon neutrality by 2050 and climate change mitigation. Traditional post-occupancy evaluations, which identify defects after construction, are impractical for addressing energy performance gaps. A new, real-time inspection approach is necessary throughout the construction process. This paper suggests an innovative guideline for prefabricated buildings, emphasizing digital ‘self-instruction’ and ‘self-inspection’. These procedures ensure activities impacting quality adhere to specific instructions, drawings, and 3D models, incorporating the relevant acceptance criteria to verify completion. This methodology, promoting alignment with planned energy-efficient features, is supported by BIM-based software and Augmented Reality (AR) tools, embodying Industry 4.0 principles. BIM (Building Information Modeling) and AR bridge the gap between virtual design and actual construction, improving stakeholder communication and enabling real-time monitoring and adjustments. This integration fosters accuracy and efficiency, which are key for energy-efficient and nearly zero-energy buildings, marking a shift towards a more precise, collaborative, and environmentally sensible construction industry.
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Machine learning models have proven to be reliable methods in classification tasks. However, little research has been conducted on the classification of dwelling characteristics based on smart meter and weather data before. Gaining insights into dwelling characteristics, which comprise of the type of heating system used, the number of inhabitants, and the number of solar panels installed, can be helpful in creating or improving the policies to create new dwellings at nearly zero-energy standard. This paper compares different supervised machine learning algorithms, namely Logistic Regression, Support Vector Machine, K-Nearest Neighbor, and Long-short term memory, and methods used to correctly implement these algorithms. These methods include data pre-processing, model validation, and evaluation. Smart meter data, which was used to train several machine learning algorithms, was provided by Groene Mient. The models that were generated by the algorithms were compared on their performance. The results showed that the Long-short term memory performed the best with 96% accuracy. Cross Validation was used to validate the models, where 80% of the data was used for training purposes and 20% was used for testing purposes. Evaluation metrics were used to produce classification reports, which indicates that the Long-short term memory outperforms the compared models on the evaluation metrics for this specific problem.
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One of the most complex and urgent challenges in the energy transition is the large‐scale refurbishment of the existing housing stock in the built environment. In order to comply with the goals of the Paris convention, the aim is to live “energy‐neutral,’’ that is, a dwelling should produce as much sustainable energy as it consumes on a yearly basis. This means that millions of existing houses need to undergo a radical energy retrofit. In the next 30 years, all dwellings should be upgraded to nearly zero‐energy buildings, which is a challenge to accomplish for a reasonable price. Across the EU, many projects have developed successful approaches to the improvement of building technologies and processes, as well a better involvement of citizens. It is important to compare and contrast such approaches and disseminate lessons learned.In practice, it is crucial to raise the level of participation of inhabitants in neighborhood renovation activities. Therefore,the central question of this issue is: How can we increase the involvement of tenants and homeowners into this radicalenergy renovation?
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The Utrecht SBE16 Conference. From the introduction: "The conference is part of the Sustainable Built conference series and is as such considered to be part of the pre-eminent international conference series on sustainable building and construction endorsed by iiSBE, UNEP-SBO and FIDIC. The Utrecht SBE16 conference is hosted by the Centre of Expertise Smart Sustainable Cities of HU University of Applied Sciences Utrecht, in partnership with six Dutch Universities of Applied Sciences (Avans, Saxion, Rotterdam, The Hague, Zuyd, InHolland) and the Utrecht Sustainability Institute (USI). The Transition Zero conference provides us with a unique opportunity to meet transition professionals in urban sustainability from all over Europe and beyond and to learn about the latest developments and best (inter)national practices in urban sustainability. The rich interest in the conference, made it possible to offer research as well as practitioner-driven tracks on topics related to the conference title. The conference brought together excellent future-minded practitioners, researchers and thought leaders from the R&I community, specialists and professionals on zero energy homes and transition of the built environment."
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One of the most complex and urgent challenges in the energy transition is the large-scale refurbishment of the existing housing stock in the built environment. In order to comply with the goals of the Paris convention, the aim is to live “energy-neutral,” that is, a dwelling should produce as much sustainable energy as it consumes on a yearly basis. This means that millions of existing houses need to undergo a radical energy retrofit. In the next 30 years, all dwellings should be upgraded to nearly zero-energy buildings, which is a challenge to accomplish for a reasonable price. Across the EU, many projects have developed successful approaches to the improvement of building technologies and processes, as well a better involvement of citizens. It is important to compare and contrast such approaches and disseminate lessons learned. In practice, it is crucial to raise the level of participation of inhabitants in neighborhood renovation activities. Therefore, the central question of this issue is: How can we increase the involvement of tenants and homeowners into this radical energy renovation?
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Machine learning models have proven to be reliable methods in classification tasks. However, little research has been done on classifying dwelling characteristics based on smart meter & weather data before. Gaining insights into dwelling characteristics can be helpful to create/improve the policies for creating new dwellings at NZEB standard. This paper compares the different machine learning algorithms and the methods used to correctly implement the models. These methods include the data pre-processing, model validation and evaluation. Smart meter data was provided by Groene Mient, which was used to train several machine learning algorithms. The models that were generated by the algorithms were compared on their performance. The results showed that Recurrent Neural Network (RNN) 2performed the best with 96% of accuracy. Cross Validation was used to validate the models, where 80% of the data was used for training purposes and 20% was used for testing purposes. Evaluation metrices were used to produce classification reports, which can indicate which of the models work the best for this specific problem. The models were programmed in Python.
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Electrification of transportation, communication, working and living continues worldwide. Televisions, telephones, servers are an important part of everyday life. These loads and most sustainable sources as well, have one thing in common: Direct Current. The Dutch research and educational programme ‘DC – road to its full potential’ studies the impact of feeding these appliances from a DC grid. An improvement in energy efficiency is expected, other benefits are unknown and practical considerations are needed to come to a proper comparison with an AC grid. This paper starts with a brief introduction of the programme and its first stages. These stages encompass firstly the commissioning, selection and implementation of a safe and user friendly testing facility, to compare performance of domestic appliances when powered with AC and DC. Secondly, the relationship between the DC-testing facility and existing modeling and simulation assignments is explained. Thirdly, first results are discussed in a broad sense. An improved energy efficiency of 3% to 5% is already demonstrated for domestic appliances. That opens up questions for the performance of a domestic DC system as a whole. The paper then ends with proposed minor changes in the programme and guidelines for future projects. These changes encompass further studying of domestic appliances for product-development purposes, leaving less means for new and costly high-power testing facilities. Possible gains are 1) material and component savings 2) simpler and cheaper exteriors 3) stable and safe in-house infrastructure 4) whilst combined with local sustainable generation. That is the road ahead. 10.1109/DUE.2014.6827758
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A replacement of cars with conventional internal combustion engines (ICEs) by electric vehicles (EVs) is seen by many as a means to improve local air quality, reduce dependence on fossil fuels and CO2 emissions. The market for EV is slowly developing with a growing number of (subsidized) manufacturers offering EV models in different market segments to (subsidized) car owners. The number of EVs is still small in most countries, but policymakers and manufacturers see partial or even full replacement of ICEs by EVs as realistic in the coming decade. EV engines are powered by rechargeable lithium-ion batteries. Li-ion is produced from precursors, either liquid (brine metal salt) or solid (hard rocks). Lithium mining is still concentrated in a few countries. Lithium is used for batteries, ceramics, grease and medicine. This reliance comes at a cost, as conventional lithium mining creates several externalities. The following main question will be addressed: How to source a required volume of lithium in a way that reduces the environmental and social-economic impact of mining this resource? To address this question, we will use a combination of relevant literature and a local case study supported by a model-based estimation. The focus is on the Netherlands, an EV user country, but the approach is generic.
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With the increasing global population, urbanization, the current unsustainable and expansive agricultural practices would be expected to further elevate the risk of food and nutritional insecurity of the global population, which is recognized as a global threat for the 21st century. This paper reviews the demographic changes, urbanization, sustainability of the conventional agricultural systems, the environmental and resource implications and presents possible sustainable alternatives.
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tract Micro wind turbines can be structurally integrated on top of the solid base of noise barriers near highways. A number of performance factors were assessed with holistic experiments in wind tunnel and in the field. The wind turbines underperformed when exposed in yawed flow conditions. The theoretical cosθ theories for yaw misalignment did not always predict power correctly. Inverter losses turned out to be crucial especially in standby mode. Combination of standby losses with yawed flow losses and low wind speed regime may even result in a net power consuming turbine. The micro wind turbine control system for maintaining optimal power production underperformed in the field when comparing tip speed ratios and performance coefficients with the values recorded in the wind tunnel. The turbine was idling between 20%–30% of time as it was assessed for sites with annual average wind speeds of three to five meters per second without any power production. Finally, the field test analysis showed that inadequate yaw response could potentially lead to 18% of the losses, the inverter related losses to 8%, and control related losses to 33%. The totalized loss led to a 48% efficiency drop when compared with the ideal power production measured before the inverter. Micro wind turbine’s performance has room for optimization for application in turbulent wind conditions on top of noise barriers. https://doi.org/10.3390/en14051288
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