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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.

Optimal Sizing of a Battery-Supported Electric Vehicle Charging Hub with a Limited-Capacity Grid Connection

The ever-increasing electrification of society has been a cause of utility grid issues in many regions around the world. With the increased adoption of electric vehicles (EVs) in the Netherlands, many new charge points (CPs) are required. A common installation practice of CPs is to group multiple CPs together on a single grid connection, the so-called charging hub. To further ensure EVs are adequately charged, various control strategies can be employed, or a stationary battery can be connected to this network. A pilot project in Amsterdam was used as a case study to validate the Python model developed in this study using the measured data. This paper presents an optimisation of the battery energy storage capacity and the grid connection capacity for such a P&R-based charging hub with various load profiles and various battery system costs. A variety of battery control strategies were simulated using both the optimal system sizing and the case study sizing. A recommendation for a control strategy is proposed.

Case: PV generation and Electricity demand at the Johan Cruijff Arena

PV systems are used more and more. Not always is it possible to install them in the optimal direction for maximum energy output over the year. At the Johan Cruijff ArenA the PV panels are placed all around the roof in all possible directions. Panels oriented to the north will have a lower energy gain than those oriented to the south. The 42 panel groups are connected to 8 electricity meters. Of these 8 energy meters monthly kWh produced are available. The first assignment is to calculate the energy gains of the 42 panel groups, and connect these in the correct way with the 8 energy meter readings, so simulated data is in accordance with measured data.Of the year 2017 there are also main electricity meter readings available for every quarter of an hour. A problem with these readings is that only absolute values are given. When electricity is taken of the grid this is a positive reading, but when there is a surplus of solar energy and electricity is delivered to the grid, this is also a positive reading. To see the effect on the electricity demand of future energy measures, and to use the Seev4-City detailed CO2 savings calculation with the electricity mix of the grid, it is necessary to know the real electricity demand of the building.The second assignment is to use the calculations of the first assignment to separate the 15 minute electricity meter readings in that for real building demand and for PV production.This document first gives information for teachers (learning goals, possible activities, time needed, further reading), followed by the assignment for students.

Robotics Simulation Labs

pyPaSWAS

BACKGROUND: Our previously published CUDA-only application PaSWAS for Smith-Waterman (SW) sequence alignment of any type of sequence on NVIDIA-based GPUs is platform-specific and therefore adopted less than could be. The OpenCL language is supported more widely and allows use on a variety of hardware platforms. Moreover, there is a need to promote the adoption of parallel computing in bioinformatics by making its use and extension more simple through more and better application of high-level languages commonly used in bioinformatics, such as Python.RESULTS: The novel application pyPaSWAS presents the parallel SW sequence alignment code fully packed in Python. It is a generic SW implementation running on several hardware platforms with multi-core systems and/or GPUs that provides accurate sequence alignments that also can be inspected for alignment details. Additionally, pyPaSWAS support the affine gap penalty. Python libraries are used for automated system configuration, I/O and logging. This way, the Python environment will stimulate further extension and use of pyPaSWAS.CONCLUSIONS: pyPaSWAS presents an easy Python-based environment for accurate and retrievable parallel SW sequence alignments on GPUs and multi-core systems. The strategy of integrating Python with high-performance parallel compute languages to create a developer- and user-friendly environment should be considered for other computationally intensive bioinformatics algorithms.

Deep learning with Python for crack detection

An illustrative non-technical review was published on Towards Data Science regarding our recent Journal paper “Automatic crack classification and segmentation on masonry surfaces using convolutional neural networks and transfer learning”.While new technologies have changed almost every aspect of our lives, the construction field seems to be struggling to catch up. Currently, the structural condition of a building is still predominantly manually inspected. In simple terms, even nowadays when a structure needs to be inspected for any damage, an engineer will manually check all the surfaces and take a bunch of photos while keeping notes of the position of any cracks. Then a few more hours need to be spent at the office to sort all the photos and notes trying to make a meaningful report out of it. Apparently this a laborious, costly, and subjective process. On top of that, safety concerns arise since there are parts of structures with access restrictions and difficult to reach. To give you an example, the Golden Gate Bridge needs to be periodically inspected. In other words, up to very recently there would be specially trained people who would climb across this picturesque structure and check every inch of it.