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Whitepaper : Smart sensors, the new senses of logistics

Classification of dwelling characteristics with machine learning algorithms based on smart meter & weather data

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.

Smart cities and logistics. (Presentation parallel session Places & Flows)

Gaining insights into dwelling characteristics using machine learning for policy making on nearly zero-energy buildings with the use of smart meter and weather data

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.

Crafting Conflict: Designing Meaningful Interactions in a Smooth Society Or: Beyond Seamless: Creating Impact Through Designed Friction

Our current smart society, where problems and frictions are smoothed out with smart, often invisible technology like AI and smart sensors, calls for designers who unravel and open the smart fabric. Societies are not malleable, and moreover, a smooth society without rough edges is neither desirable nor livable. In this paper we argue for designing friction to enhance a more nuanced debate of smart cities in which conflicting values are better expressed. Based on our experiences with the Moral Design Game, an adversarial design activity, we came to understand the value of creating tangible vessels to highlight conflict and dipartite feelings surrounding smart cities.

Reducing the environmental impact and improving the efficiency of construction transport

Abstract: Last few years the hindrance, accidents, pollution and other negative side effects of construction projects and namely construction transport have become an issue particularly in urban areas across Europe such as in London, and in the Netherlands as well, including the cities of Utrecht, Rotterdam and Amsterdam. Municipalities have issued new legislation and stricter conditions for vehicles to be able to access cities and city centres in particular and accessibility of older and polluting vehicles. Considerate clients, public as well private, have started developing tender policies to encourage contractors to reduce the environmental impact of construction projects. Contractors and third party logistics providers have started applying consolidation centres. These developments have shown considerable reductions of number of vehicles needed to deliver goods and to transport workers to site. In addition these developments have led to increased transport efficiency, labour productivity and cost reductions on site as well as down the supply chain. Besides these developments have led to increased innovations in the field of logistics planning software, use of ICT , and handling hardware and equipment. This paper gives an overview of current developments and applications in the field of construction logistics in the Netherlands, and in a few project cases in particular. Those cases are underway as part of an ongoing applied research project and studied by using an ethnographic participative action research approach. The case findings and project results show initial advantages how the projects, the firms involved and the environment can profit from the advancement of logistics management leading to reduced environmental impact and increased efficiencies of construction transport.