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.
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.
Deze bijdrage aan "Smart Humanity" (red. W. Bronsgeest en S. de Waart 2020) schetst een beeld van modellen die richting geven aan processen, organisatorische inrichting, en informatievoorziening. AI-toepassingen worden hierbij gezien als onderdeel van de informatievoorziening.
Due to societal developments, like the introduction of the ‘civil society’, policy stimulating longer living at home and the separation of housing and care, the housing situation of older citizens is a relevant and pressing issue for housing-, governance- and care organizations. The current situation of living with care already benefits from technological advancement. The wide application of technology especially in care homes brings the emergence of a new source of information that becomes invaluable in order to understand how the smart urban environment affects the health of older people. The goal of this proposal is to develop an approach for designing smart neighborhoods, in order to assist and engage older adults living there. This approach will be applied to a neighborhood in Aalst-Waalre which will be developed into a living lab. The research will involve: (1) Insight into social-spatial factors underlying a smart neighborhood; (2) Identifying governance and organizational context; (3) Identifying needs and preferences of the (future) inhabitant; (4) Matching needs & preferences to potential socio-techno-spatial solutions. A mixed methods approach fusing quantitative and qualitative methods towards understanding the impacts of smart environment will be investigated. After 12 months, employing several concepts of urban computing, such as pattern recognition and predictive modelling , using the focus groups from the different organizations as well as primary end-users, and exploring how physiological data can be embedded in data-driven strategies for the enhancement of active ageing in this neighborhood will result in design solutions and strategies for a more care-friendly neighborhood.
The developments of digitalization and automation in freight transport and logistics are expected to speed-up the realization of an adaptive, seamless, connected and sustainable logistics system. CATALYST determines the potential and impact of Connected Automated Transport (CAT) by testing and implementing solutions in a real-world environment. We experiment on smart yards and connected corridors, to answer research questions regarding supply chain integration, users, infrastructure, data and policy. Results are translated to overarching lessons on CAT implementations, and shared with potential users and related communities. This way, CATALYST helps logistic partners throughout the supply chain prepare for CAT and accelerates innovation.
The developments of digitalization and automation in freight transport and logistics are expected to speed-up the realization of an adaptive, seamless, connected and sustainable logistics system. CATALYST determines the potential and impact of Connected Automated Transport (CAT) by testing and implementing solutions in a real-world environment. We experiment on smart yards and connected corridors, to answer research questions regarding supply chain integration, users, infrastructure, data and policy. Results are translated to overarching lessons on CAT implementations, and shared with potential users and related communities. This way, CATALYST helps logistic partners throughout the supply chain prepare for CAT and accelerates innovation.
