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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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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The main objective of this study was to influence implicit learning through two different classical manipulations and to inspect whether working memory capacity (WMC) and personality were related to the different measures of learning. With that purpose, in Experiment 1 we asked 172 undergraduate students of psychology to perform a serial reaction time (SRT) task under single- or dual-task conditions and to complete a WMC task and a personality test. In Experiment 2, 164 students performed the SRT task under incidental or intentional conditions and also filled a WMC task and a personality test. In both experiments, WMC influenced learning, but this relation was found only when attention was not loaded (Experiment 1) or when intentional instructions were given (Experiment 2). The pattern of relations with personality, although more varied, also showed a commonality between both experiments: learning under the most implicit conditions correlated positively with extraversion.
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Memory forms the input for future behavior. Therefore, how individuals remember a certain experience may be just as important as the experience itself. The peak-and-end-rule (PE-rule) postulates that remembered experiences are best predicted by the peak emotional valence and the emotional valence at the end of an experience in the here and now. The PE-rule, however, has mostly been assessed in experimental paradigms that induce relatively simple, one-dimensional experiences (e.g. experienced pain in a clinical setting). This hampers generalizations of the PE-rule to the experiences in everyday life. This paper evaluates the generalizability of the PE-rule to more complex and heterogeneous experiences by examining the PE-rule in a virtual reality (VR) experience, as VR combines improved ecological validity with rigorous experimental control. Findings indicate that for more complex and heterogeneous experiences, peak and end emotional valence are inferior to other measures (such as averaged valence and arousal ratings over the entire experiential episode) in predicting remembered experience. These findings suggest that the PE-rule cannot be generalized to ecologically more valid experiential episodes.
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There is emerging evidence that the performance of risk assessment instruments is weaker when used for clinical decision‐making than for research purposes. For instance, research has found lower agreement between evaluators when the risk assessments are conducted during routine practice. We examined the field interrater reliability of the Short‐Term Assessment of Risk and Treatability: Adolescent Version (START:AV). Clinicians in a Dutch secure youth care facility completed START:AV assessments as part of the treatment routine. Consistent with previous literature, interrater reliability of the items and total scores was lower than previously reported in non‐field studies. Nevertheless, moderate to good interrater reliability was found for final risk judgments on most adverse outcomes. Field studies provide insights into the actual performance of structured risk assessment in real‐world settings, exposing factors that affect reliability. This information is relevant for those who wish to implement structured risk assessment with a level of reliability that is defensible considering the high stakes.
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Mixing examples of different categories (interleaving) has been shown to promote inductive learning as compared with presenting examples of the same category together (massing). In three studies, we tested whether the advantage of interleaving is exclusively due to the mixing of examples from different categories or to the temporal gap introduced between presentations. In addition, we also tested the role of working memory capacity (WMC). Results showed that the mixing of examples might be the key component that determines improved induction. WMC might also be involved in the interleaving effect: participants with high spans seemed to profit more than participants with low spans from interleaved presentations. Our findings have relevant implications for education. Practice schedules should be individually customised so society as a whole can profit from differences between learners. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
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A considerable amount of literature has been published on Corporate Reputation, Branding and Brand Image. These studies are extensive and focus particularly on questionnaires and statistical analysis. Although extensive research has been carried out, no single study was found which attempted to predict corporate reputation performance based on data collected from media sources. To perform this task, a biLSTM Neural Network extended with attention mechanism was utilized. The advantages of this architecture are that it obtains excellent performance for NLP tasks. The state-of-the-art designed model achieves highly competitive results, F1 scores around 72%, accuracy of 92% and loss around 20%.
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This study explored what contributes to successful family foster care from the perspective of young people by asking them about their most positive memory of family foster care. Forty-four Dutch adolescents and young adults (aged 16–28) participated in this study and shared their most positive memory in a short interview. Their answers were qualitatively analyzed using reflexive thematic analysis, supplemented with an analysis of the structure of their memories. The thematic analysis resulted in the themes Belongingness, Receiving support, Normal family life, It is better than before, and Seeing yourself grow. The structural analysis showed that young people both shared memories related to specific events, as well as memories that portrayed how they felt for a prolonged period of time. In addition, young people were inclined to share negative memories alongside the positive memories. These results highlight that, in order to build a sense of belonging, it is important that of foster parents create a normal family environment for foster children and provide continuous support. Moreover, the negative memories shared by participants are discussed in light of a bias resulting from earlier traumatic experiences. Accepted Version. Published Version Article at Sage: https://doi.org/10.1177%2F1359104520978691
MULTIFILE
A short paper on the whats and the hows of learning technology standardization
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Passenger flow management is an important issue at many airports around the world. There are high concentrations of passengers arriving and leaving the airport in waves of large volumes in short periods, particularly in big hubs. This might cause congestion in some locations depending on the layout of the terminal building. With a combination of real airport data, as well as synthetic data obtained through an airport simulator, a Long Short-Term Memory Recurrent Neural Network has been implemented to predict the possible trajectories that passengers may travel within the airport depending on user-defined passenger profiles. The aim of this research is to improve passenger flow predictability and situational awareness to make a more efficient use of the airport, that could also positively impact communication with public and private land transport operators.
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