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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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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This study explores how households interact with smart systems for energy usage, providing insights into the field's trends, themes and evolution through a bibliometric analysis of 547 relevant literature from 2015 to 2025. Our findings discover: (1) Research activity has grown over the past decade, with leading journals recognizing several productive authors. Increased collaboration and interdisciplinary work are expected to expand; (2) Key research hotspots, identified through keyword co-occurrence, with two (exploration and development) stages, highlighting the interplay between technological, economic, environmental, and behavioral factors within the field; (3) Future research should place greater emphasis on understanding how emerging technologies interact with human, with a deeper understanding of users. Beyond the individual perspective, social dimensions also demand investigation. Finally, research should also aim to support policy development. To conclude, this study contributes to a broader perspective of this topic and highlights directions for future research development.
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B4B is a multi-year, multi-stakeholder project focused on developing methods to harness big data from smart meters, building management systems and the Internet of Things devices, to reduce energy consumption, increase comfort, respond flexibly to user behaviour and local energy supply and demand, and save on installation maintenance costs. This will be done through the development of faster and more efficient Machine Learning and Artificial Intelligence models and algorithms. The project is geared to existing utility buildings such as commercial and institutional buildings.
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Uit voorwoord Anton Franken, lid CvB `Smart Sustainable Cities is een platform voor het bedrijfsleven, kennisinstellingen en Hogeschool Utrecht waar gezamenlijk vernieuwende producten en diensten worden ontwikkeld die de realisatie van slimme, duurzame en gezonde steden dichterbij brengt. Startende en ervaren professionals hebben hiermee de mogelijkheid om via het onderwijs of via bij- en nascholing de nieuwste toepasbare kennis en inzichten op dit gebied op te doen. Tevens verricht het platform onderzoek. In projecten werken studenten, bedrijven, docenten en onderzoekers samen om nieuwe kennis en inzichten tot toepassing te brengen. Drie inhoudelijke thema’s staan centraal: ‘Stedelijke gebieden energieneutraal’, ‘Gezonde gebieden gezond gebouwd’ en ‘Duurzaam gedrag: mens en organisatie’ .`
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Installing photovoltaic panels (PV) on household rooftops can significantly contribute to mitigating anthropogenic climate change. The mitigation potential will be much higher when households would use PVs in a sustainable way, that is, if they match their electricity demand to their PVs electricity production, as to avoid using electricity from the grid. Whilst some have argued that owning PVs motivate households to use their PV in a sustainable way, others have argued that owning a PV does not result in load shifting, or that PV owners may even use more energy when their PV production is low. This paper addresses this critical issue, by examining to what extent PV owners are likely to shift their electricity demand to reduce the use of electricity from the grid. Extending previous studies, we analyse actual high frequency electricity use from the grid using smart meter data of households with and without PVs. Specifically, we employ generalized additive models to examine whether hourly net electricity use (i.e., the difference between electricity consumed from the grid and supplied back to the grid) of households with PVs is not only lower during times when PV production is high, but also when PV production low, compared to households without PVs. Results indicate that during times when PV production is high, net electricity use of households with PV is negative, suggesting they sent back excess electricity to the power grid. However, we found no difference in net electricity use during times when PV production is low. This suggests that installing PV does not promote sustainable PV use, and that the mitigation potential of PV installment can be enhanced by encouraging sustainable PV use
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Dit document geeft een overzicht van de bevindingen over het Factory-as-a service concept. Gedurende het SMITZH project heeft het lectoraat Smart Sustainable Manufacturing gezocht naar antwoorden op een aantal vragen: Welke initiatieven bestaan er, waar ondernemers elkaar helpen via het beschikbaar stellen en delen van productiecapaciteit? Wat zijn de randvoorwaarden om zo’n initiatief te laten slagen? Wat kan bijdragen om belemmeringen voor de toekomst weg te nemen? De voordelen zijn zeker aanwezig, maar obstakels ook. Met name dat laatste kan de voortgang en innovatief denken over de inrichting van flexibele en ‘Smart Manufacturing’ in de weg zitten. Het verhogen van de flexibiliteit om de maakindustrie concurrerender en veerkrachtiger te maken is een van de doelstellingen van het Smart Industry Programma, SMITZH en het lectoraat.
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In recent years, a step change has been seen in the rate of adoption of Industry 4.0 technologies by manufacturers and industrial organizations alike. This article discusses the current state of the art in the adoption of Industry 4.0 technologies within the construction industry. Increasing complexity in onsite construction projects coupled with the need for higher productivity is leading to increased interest in the potential use of Industry 4.0 technologies. This article discusses the relevance of the following key Industry 4.0 technologies to construction: data analytics and artificial intelligence, robotics and automation, building information management, sensors and wearables, digital twin, and industrial connectivity. Industrial connectivity is a key aspect as it ensures that all Industry 4.0 technologies are interconnected allowing the full benefits to be realized. This article also presents a research agenda for the adoption of Industry 4.0 technologies within the construction sector, a three-phase use of intelligent assets from the point of manufacture up to after build, and a four-staged R&D process for the implementation of smart wearables in a digital enhanced construction site.
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Background: With increasing knowledge on the adverse health efects of certain constituents of PM (particulate matter), such as silica, metals, insoluble ions, and black carbon, PM has been under the attention of work safety experts. Previously, we investigated the perceptions of blue-collar workers in highly exposed areas of work. Subsequently, we developed an instruction folder highlighting the most important aspects of PM risk and mitigation, and tested this folder in a digital experiment. The digital experiment yielded positive results with regards to acquired knowledge about PM, but did not on risk perception or safety behavior. Methods: In this study, we investigate the efects of the folder when combined with a practical assignment involving a PM exposimeter, showing the amount of particulate matter in microgram per cubic meter in real time on its display for various activities. We tested this at six workplaces of four companies in the roadwork and construction branch. Results: The results indicate that the folder itself yields an increased knowledge base in employees about PM, but the efects of the practical assignment are more contentious. Nevertheless, there is an indication that using the assignment may lead to a higher threat appraisal among employees for high exposure activities. Conclusion: We recommend implementing our folder in companies with high PM exposure and focusing further research on appropriate methods of implementation.
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Human behaviour change is necessary to meet targets set by the Paris Agreement to mitigate climate change. Restrictions and regulations put in place globally to mitigate the spread of COVID-19 during 2020 have had a substantial impact on everyday life, including many carbon-intensive behaviours such as transportation. Changes to transportation behaviour may reduce carbon emissions. Behaviour change theory can offer perspective on the drivers and influences of behaviour and shape recommendations for how policy-makers can capitalise on any observed behaviour changes that may mitigate climate change. For this commentary, we aimed to describe changes in data relating to transportation behaviours concerning working from home during the COVID-19 pandemic across the Netherlands, Sweden and the UK. We display these identified changes in a concept map, suggesting links between the changes in behaviour and levels of carbon emissions. We consider these changes in relation to a comprehensive and easy to understand model of behaviour, the Opportunity, Motivation Behaviour (COM-B) model, to understand the capabilities, opportunities and behaviours related to the observed behaviour changes and potential policy to mitigate climate change. There is now an opportunity for policy-makers to increase the likelihood of maintaining pro-environmental behaviour changes by providing opportunities, improving capabilities and maintaining motivation for these behaviours.
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