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.
The term crowdsourcing was introduced by Jeff Howe (2006). It is the act of a company or organisation to take a function once performed by employees and outsourcing it to an undefined, and usually large, network of people in the form of an open call. As communication tools to organize work have become widely available, and a well-educated global work force has come online, crowdsourcing has become an increasingly important mechanism to organize work. We discuss a categorisation of crowdsourcing, its costs and benefits and several examples. The use of crowdsourcing begins with the question which strategic goal an organisation wants to achieve, and whether the benefits outweigh the costs. We give some recommendations for adopting crowdsourcing. This usually requires a certain amount of restructuring of existing workflows and a willingness to become more open which may or may not be a welcome side effect.
This study examines how the contemporary European policy debate addresses the further development of the quality of teacher educators. A classification framework based on the literature on professionalism was used to compare European and Member State policy actions and measures on the quality of teacher educators through an analysis of seven European policy documents and a questionnaire completed by key policy-makers in 16 European countries. The findings show that European Union policy documents pay limited attention to the quality of teacher educators. However, the professionalism of teacher educators receives more policy attention at the level of individual Member States. Most of these policies are part of general policies for higher education teachers, while the initiative lies with governments and teacher education institutes. The role of the professionals themselves in developing policies to strengthen their professionalism seems very limited.
