Identifying occupant presence in a room based on machine learning techniques by measuring indoor air conditions

Knowing about the presence and number of people in a room can be of interest for precise control of heating, ventilation and air conditioning. To determine the number and presence of occupants cost-effectively, it is of interest to use already existing air condition sensors (temperature, humidity, CO2) of the building automation system. Different approaches and methods for determining presence have attracted attention in recent years. We propose an occupancy detection method based on a method of supervised machine learning. In an experiment, measurement data were recorded in a research apartment with controllable boundary conditions. The presence of people was simulated by artificial injection of water vapour, CO2 and heat dissipation. The variation of the number of artificial users, the duration of presence and the supply air volume flow of the ventilation resulted in a total of 720 combinations. By using artificial users, the boundary conditions were accurately defined, and different presence situations could be measured time-effectively. The data is evaluated with a method of supervised machine learning called random forest. The statistical model can determine precisely the number of people in over 93% of the cases in a disjoint test sample. The experiments took part in the Rosenheim Technical University of Applied Sciences laboratory

Identifying faults in the building system based on model prediction and residuum analysis

The energy efficiency of the building HVAC systems can be improved when faults in the running system are known. To this day, there are no cost-efficient, automatic methods that detect faults of the building HVAC systems to a satisfactory degree. This study induces a new method for fault detection that can replace a graphical, user-subjective evaluation of a building data measured on site with an automatic, data-based approach. This method can be a step towards cost-effective monitoring. For this research, the data from a detailed simulation of a residential case study house was used to compare a faultless operation of a building with a faulty operation. We argue that one can detect faults by analysing the properties of residuals of the prediction to the actual data. A machine learning model and an ARX model predict the building operation, and the method employs various statistical tests such as the Sign Test, the Turning Point Test, the Box-Pierce Test and the Bartels-Rank Test. The results show that the amount of data, the type and density of system faults significantly affect the accuracy of the prediction of faults. It became apparent that the challenge is to find a decision rule for the best combination of statistical tests on residuals to predict a fault

Residual Analysis of Predictive Modelling Data for Automated Fault Detection in Building’s Heating, Ventilation and Air Conditioning Systems

Faults in Heating, Ventilation and Air Conditioning (HVAC) systems affect the energy efficiency of buildings. To date, there rarely exist methods to detect and diagnose faults during the operation of buildings that are both cost-effective and sufficient accurate. This study presents a method that uses artificial intelligence to automate the detection of faults in HVAC systems. The automated fault detection is based on a residual analysis of the predicted total heating power and the actual total heating power using an algorithm that aims to find an optimal decision rule for the determination of faults. The data for this study was provided by a detailed simulation of a residential case study house. A machine learning model and an ARX model predict the building operation. The model for fault detection is trained on a fault-free data set and then tested with a faulty operation. The algorithm for an optimal decision rule uses various statistical tests of residual properties such as the Sign Test, the Turning Point Test, the Box-Pierce Test and the Bartels-Rank Test. The results show that it is possible to predict faults for both known faults and unknown faults. The challenge is to find the optimal algorithm to determine the best decision rules. In the outlook of this study, further methods are presented that aim to solve this challenge

The concerns of the young protesters are justified: A statement by Scientists for Future concerning the protests for more climate protection

In March 2019, German-speaking scientists and scholars calling themselves Scientists for Future, published a statement in support of the youth protesters in Germany, Austria, and Switzerland (Fridays for Future, Klimastreik/Climate Strike), verifying the scientific evidence that the youth protestors refer to. In this article, they provide the full text of the statement, including the list of supporting facts (in both English and German) as well as an analysis of the results and impacts of the statement. Furthermore, they reflect on the challenges for scientists and scholars who feel a dual responsibility: on the one hand, to remain independent and politically neutral, and, on the other hand, to inform and warn societies of the dangers that lie ahead. © 2019 G. Hagedorn et al.; licensee oekom verlag.This Open Access article is published under the terms of the Creative Commons Attribution License CCBY4.0 (http://creativecommons.org/licenses/by/4.0)

Salon for a speculative future

The Salon for a Speculative Future was inaugurated in March 2019 in celebration of Women’s History Month, as a platform for cross-generational and cross-disciplinary exchange. Reflecting on the current political and economic global situation, in particular the exponential acceleration of a technology-driven platform capitalism, many women advocate positive change for an ecologically sustainable and humane future. In The Left Hand of Darkness, Ursula K. Le Guin argues that science fiction does not simply extrapolate from the present to predict the future—instead, the fiction writer engages in thought-experiments where ideas and intuition move within the confines set by the experiment. This book hosts imaginative thinking by seventy-five women artists, sharing their influences, inspired by women’s contributions to diverse fields, from art, education, and science to political activism. Salon for a Speculative Future honours and shares insights and experimental thinking towards a positive future