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.
Online platforms for collecting local memories are often claimed to be a driving force of empowerment for individuals, groups and the community as a whole. Long term online participation especially plays a key role in the claims for empowerment on group and community level. However, the present research on local memory websites lacks empirical data to substantiate these claims and leaves aside questions about their wider presence, the way they are organized and how their particular structure and affordances enable online participation. To address these issues, we develop six analytical dimensions in order to analyse a comprehensive number of such sites, examining in particular their organizational and online participatory features. On the basis of a cross-sectional design including 80 cases from the Netherlands, the United Kingdom and various other countries, we show three types of websites can be distinguished, namely residential, institutional and associational. In addition, we find that the expectancy of online participation is maximized not only by organizational aspects that fosterautonomy, but also by characteristics that enlarge the sense of authenticity. Our findings also show a limited number of cases with a considerable level of online participation, which offers the empirical data for analysis in terms of empowerment on group and community level.Nevertheless, we conclude that in most cases the organizational characteristics and participatory affordances of the websites are not sufficient to produce empowerment on all levels.
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%.
