scholarly journals A Non-Intrusive Approach for Indoor Occupancy Detection in Smart Environments

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3953 ◽  
Author(s):  
Bruno Abade ◽  
David Perez Abreu ◽  
Marilia Curado
Keyword(s):  
Machine Learning ◽  
Environmental Data ◽  
Machine Learning Techniques ◽  
Smart Environments ◽  
Prototype System ◽  
Indoor Environments ◽  
Process Data ◽  
Indoor Space ◽  
Occupancy Detection ◽  
Real Scenario

Smart Environments try to adapt their conditions focusing on the detection, localisation, and identification of people to improve their comfort. It is common to use different sensors, actuators, and analytic techniques in this kind of environments to process data from the surroundings and actuate accordingly. In this research, a solution to improve the user’s experience in Smart Environments based on information obtained from indoor areas, following a non-intrusive approach, is proposed. We used Machine Learning techniques to determine occupants and estimate the number of persons in a specific indoor space. The solution proposed was tested in a real scenario using a prototype system, integrated by nodes and sensors, specifically designed and developed to gather the environmental data of interest. The results obtained demonstrate that with the developed system it is possible to obtain, process, and store environmental information. Additionally, the analysis performed over the gathered data using Machine Learning and pattern recognition mechanisms shows that it is possible to determine the occupancy of indoor environments.

scholarly journals A MACHINE LEARNING APPROACH ON OCCUPANT NUMBER PREDICTION FOR INDOOR SPACES

2018 ◽  
Vol XLII-4 ◽  
pp. 275-281
Author(s):  
U. Isikdag ◽  
K. Sahin ◽  
S. Cansiz
Keyword(s):  
Machine Learning ◽  
Sound Level ◽  
Machine Learning Techniques ◽  
Quality Level ◽  
Indoor Space ◽  
Occupancy Detection ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Level Sensor ◽  
Sensor Temperature

<p><strong>Abstract.</strong> The knowledge about the occupancy of an indoor space can serve to various domains ranging from emergency response to energy efficiency in buildings. The literature in the field presents various methods for occupancy detection. Data gathered for occupancy detection, can also be used to predict the number of occupants at a certain indoor space and time. The aim of this research was to determine the number of occupants in an indoor space, through the utilisation of information acquired from a set of sensors and machine learning techniques. The sensor types used in this research was a sound level sensor, temperature/humidity level sensor and an air quality level sensor. Based on data acquired from these sensors six automatic classification techniques are employed and tested with the aim of automatically detecting the number of occupants in an indoor space by making use of multi-sensor information. The results of the tests demonstrated that machine learning techniques can be used as a tool for prediction of number of occupants in an indoor space.</p>

scholarly journals Comparative Analysis of Machine Learning Methods for Non-Intrusive Indoor Occupancy Detection and Estimation

2021 ◽  
Author(s):  
Muhammad S. Aliero ◽  
Muhammad F. Pasha ◽  
Adel N. Toosi ◽  
Imran Ghani ◽  
Ali S. Sadiq ◽  
...  
Keyword(s):  
Machine Learning ◽  
Training Dataset ◽  
Living Room ◽  
Prototype System ◽  
Building Energy Efficiency ◽  
Process Data ◽  
Occupancy Detection ◽  
Model Training ◽  
Active Research

Abstract Occupancy-driven application research has been active research for a decade that focuses on improving or replacing new building infrastructure to improve building energy efficiency. Existing approaches for HVAC energy saving are putting more emphasis on occupancy detection, estimation, and localization to trade-off between energy consumption and thermal comfort satisfaction. In a non-intrusive approach, various sensors, actuators, and analytic data methods are commonly used to process data from occupant surroundings and trigger appropriate action to achieve the task. However, the performance of the non-intrusive approach reported in the literature is relatively poor due to the lack of quality of dataset used in model training and expropriate choice of machine learning model. This study proposed a non-intrusive approach that to improve the collection and quality of dataset using data pre-processing. The study collected a training dataset using various sensors installed in the building and developed a model using five machine learning models to determine occupant’s presence and estimate their number in the building. The proposed solution is tested in the living room with a prototype system integrated with various sensors designed to obtain occupant surrounding environmental datasets. The model’s prediction results obtained indicate that it is possible for the proposed solution to obtain data, process, and predict the occupant number with high accuracy (73.6 -99.7% using random forest).

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

2020 ◽  
Vol 172 ◽  
pp. 22005
Author(s):  
Lucia Hanfstaengl ◽  
Michael Parzinger ◽  
Uli Spindler ◽  
Ulrich Wellisch ◽  
Markus Wirnsberger
Keyword(s):  
Machine Learning ◽  
Boundary Conditions ◽  
Heat Dissipation ◽  
Measurement Data ◽  
Test Sample ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Automation System ◽  
Precise Control ◽  
Occupancy Detection

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.

scholarly journals Can machine learning techniques predict customer dissatisfaction? A feasibility study for the automotive industry

2016 ◽  
Vol 6 (1) ◽  
pp. 80 ◽  
Author(s):  
Stefan Meinzer ◽  
Ulf Jensen ◽  
Alexander Thamm ◽  
Joachim Hornegger ◽  
Björn M. Eskofier
Keyword(s):  
Machine Learning ◽  
Customer Satisfaction ◽  
Automotive Industry ◽  
Machine Learning Techniques ◽  
Service Process ◽  
Svm Classifier ◽  
Objective Data ◽  
Process Data ◽  
The Future ◽  
Customer Dissatisfaction

The automotive industry is in the strongest competition ever, as this sector gets disrupted by new arising competitors. Providing services to maximum customer satisfaction will be one of the most crucial competitive advantages in the future. Around 1 Terabyte of objective data is created every hour today. This volume will significantly grow in the future by the increasing numberof connected services within the automotive industry. However, customer satisfaction determination is solely based on subjective questionnaires today without taking the vast amount of objective sensor and service process data into account. This work presents an industrial application that fills this lack of research and thus provides a solution with a high practical impact to survive in the tough competition of the automotive industry. Therefore, the work addresses these fundamental business questions: 1) Candissatisfied customers be classified based on data that is produced during every service visit? 2) Can the dissatisfaction indicators be derived from service process data? A machine learning problem is set up that compared 5 classifiers and analyzed data from 19,008 real service visits from an automotive company. The 105 extracted features were drawn from the most significant available sources: warranty, diagnostic, dealer system and general vehicle data. The best result for customer dissatisfaction classification was 88.8% achieved with the SVM classifier (RBF kernel). Furthermore, the 46 most potential indicators for dissatisfaction were identified by the evolutionary feature selection. Our system was capable of classifying customer dissatisfaction solely based on the objective data that is generated by almost every service visit. As the amount of these data is continuously growing, we expect that the presented data-driven approach can achieve even better results in the future with a higher amount of data.

scholarly journals Simulation of a CSP Solar Steam Generator, Using Machine Learning

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3613
Author(s):  
Adrian Gonzalez Gonzalez ◽  
Jose Valeriano Alvarez Cabal ◽  
Miguel Angel Vigil Berrocal ◽  
Rogelio Peón Menéndez ◽  
Adrian Riesgo Fernández
Keyword(s):  
Machine Learning ◽  
Performance Model ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Concentrated Solar Power ◽  
Process Data ◽  
Mena Region ◽  
Power Block ◽  
Learning Techniques ◽  
Fitting In

Developing an accurate concentrated solar power (CSP) performance model requires significant effort and time. The power block (PB) is the most complex system, and its modeling is clearly the most complicated and time-demanding part. Nonetheless, PB layouts are quite similar throughout CSP plants, meaning that there are enough historical process data available from commercial plants to use machine learning techniques. These algorithms allowed the development of a very accurate black-box PB model in a very short amount of time. This PB model could be easily integrated as a block into the PM. The machine learning technique selected was SVR (support vector regression). The PB model was trained using a complete year of data from a commercial CSP plant situated in southern Spain. With a very limited set of inputs, the PB model results were very accurate, according to their validation against a new complete year of data. The model not only fit well on an aggregate basis, but also in the transients between operation modes. To validate applicability, the same model methodology is used with a data from a very different CSP Plant, located in the MENA region and with more than double nominal electric power, obtaining an excellent fitting in the validation.

scholarly journals APPLICATION OF MACHINE LEARNING TO THE PREDICTION OF VEGETATION HEALTH

2016 ◽  
Vol XLI-B2 ◽  
pp. 465-469 ◽  
Author(s):  
Emily Burchfield ◽  
John J. Nay ◽  
Jonathan Gilligan
Keyword(s):  
Machine Learning ◽  
Predictive Power ◽  
Vegetation Index ◽  
Region Of Interest ◽  
Environmental Data ◽  
Decision Makers ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Health Dynamics ◽  
Vegetation Health

This project applies machine learning techniques to remotely sensed imagery to train and validate predictive models of vegetation health in Bangladesh and Sri Lanka. For both locations, we downloaded and processed eleven years of imagery from multiple MODIS datasets which were combined and transformed into two-dimensional matrices. We applied a gradient boosted machines model to the lagged dataset values to forecast future values of the Enhanced Vegetation Index (EVI). The predictive power of raw spectral data MODIS products were compared across time periods and land use categories. Our models have significantly more predictive power on held-out datasets than a baseline. Though the tool was built to increase capacity to monitor vegetation health in data scarce regions like South Asia, users may include ancillary spatiotemporal datasets relevant to their region of interest to increase predictive power and to facilitate interpretation of model results. The tool can automatically update predictions as new MODIS data is made available by NASA. The tool is particularly well-suited for decision makers interested in understanding and predicting vegetation health dynamics in countries in which environmental data is scarce and cloud cover is a significant concern.

scholarly journals bletl - A Python package for integrating microbioreactors in the design-build-test-learn cycle

2021 ◽  
Author(s):  
Michael Osthege ◽  
Niklas Tenhaef ◽  
Rebecca Zyla ◽  
Carolin Mueller ◽  
Johannes Hemmerich ◽  
...  
Keyword(s):  
Machine Learning ◽  
Growth Rate ◽  
Time Variable ◽  
Machine Learning Techniques ◽  
Calibration Model ◽  
Process Data ◽  
Learning Techniques ◽  
Interactive Visualizations ◽  
Short Time ◽  
Python Package

Microbioreactor (MBR) devices have emerged as powerful cultivation tools for tasks of microbial phenotyping and bioprocess characterization and provide a wealth of online process data in a highly parallelized manner. Such datasets are difficult to interpret in short time by manual workflows. In this study, we present the Python package bletl and show how it enables robust data analyses and the application of machine learning techniques without tedious data parsing and preprocessing. bletl reads raw result files from BioLector I, II and Pro devices to make all the contained information available to Python-based data analysis workflows. Together with standard tooling from the Python scientific computing ecosystem, interactive visualizations and spline-based derivative calculations can be performed. Additionally, we present a new method for unbiased quantification of time-variable specific growth rate μ(t) based on a novel method of unsupervised switchpoint detection with Student-t distributed random walks. With an adequate calibration model, this method enables practitioners to quantify time-variable growth rate with Bayesian uncertainty quantification and automatically detect switchpoints that indicate relevant metabolic changes. Finally, we show how time series feature extraction enables the application of machine learning methods to MBR data, resulting in unsupervised phenotype characterization. As an example, t-distributed Stochastic Neighbor Embedding (t-SNE) is performed to visualize datasets comprising a variety of growth/DO/pH phenotypes.

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