This paper presents a case study where a model predictive control (MPC) logic is developed for energy flexible operation of a space heating system in an educational building. A Long Short-Term Memory Neural Network (LSTM) surrogate model is trained on the output of an EnergyPlus building simulation model. This LSTM model is used within an MPC framework where a genetic algorithm is used to optimize setpoint sequences. The EnergyPlus model is used to validate the performance of the control logic. The MPC approach leads to a substantial reduction in energy consumption (7%) and energy costs (13%) with improved comfort performance. Additional energy costs savings are possible (7–16%) if a sacrifice in indoor thermal comfort is accepted. The presented method is useful for developing MPC systems in the design stages where measured data is typically not available. Additionally, this study illustrates that LSTM models are promising for MPC for buildings.
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The pervasive use of media at current-day festivals thoroughly impacts how these live events are experienced, anticipated, and remembered. This empirical study examined event-goers’ live media practices – taking photos, making videos, and in-the-moment sharing of content on social media platforms – at three large cultural events in the Netherlands. Taking a practice approach (Ahva 2017; Couldry 2004), the author studied online and offline event environments through extensive ethnographic fieldwork: online and offline observations, and interviews with 379 eventgoers. Analysis of this research material shows that through their live media practices eventgoers are continuously involved in mediated memory work (Lohmeier and Pentzold 2014; Van Dijck 2007), a form of live storytelling thatrevolves around how they want to remember the event. The article focuses on the impact of mediated memory work on the live experience in the present. It distinguishes two types of mediatised experience of live events: live as future memory and the experiential live. The author argues that memory is increasingly incorporated into the live experience in the present, so much so that, for many eventgoers, mediated memory-making is crucial to having a full live event experience. The article shows how empirical research in media studies can shed new light on key questions within memory studies.
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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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