Wavelet-based filtration procedure for denoising the predicted CO2 waveforms in smart home within the Internet of Things

The operating cost minimization of smart homes can be achieved with the optimization of the management of the building's technical functions by determination of the current occupancy status of the individual monitored spaces of a smart home. To respect the privacy of the smart home residents, indirect methods (without using cameras and microphones) are possible for occupancy recognition of space in smart homes. This article describes a newly proposed indirect method to increase the accuracy of the occupancy recognition of monitored spaces of smart homes. The proposed procedure uses the prediction of the course of CO2 concentration from operationally measured quantities (temperature indoor and relative humidity indoor) using artificial neural networks with a multilayer perceptron algorithm. The mathematical wavelet transformation method is used for additive noise canceling from the predicted course of the CO2 concentration signal with an objective increase accuracy of the prediction. The calculated accuracy of CO2 concentration waveform prediction in the additive noise-canceling application was higher than 98% in selected experiments.Web of Science203art. no. 62

Occupancy Patterns Scoping Review Project

Understanding the occupancy and heating patterns of UK domestic consumers is important for understanding the role of demand-side technologies, such as occupancy-based smart heating controls to manage energy consumption more efficiently.The research undertakes a systematic scoping review to identify and assess the quality of the UK and international evidence on occupancy patterns, to critically review the common methods of measuring occupancy, and to discuss the potential role of occupancy-based smart heating controls in meeting energy savings, thermal comfort and usability requirements.This report was prepared by a team at the University of Southampton and commissioned by the former Department of Energy and Climate Change (DECC).<br/

Machine learning for smart building applications: Review and taxonomy

© 2019 Association for Computing Machinery. The use of machine learning (ML) in smart building applications is reviewed in this article. We split existing solutions into two main classes: occupant-centric versus energy/devices-centric. The first class groups solutions that use ML for aspects related to the occupants, including (1) occupancy estimation and identification, (2) activity recognition, and (3) estimating preferences and behavior. The second class groups solutions that use ML to estimate aspects related either to energy or devices. They are divided into three categories: (1) energy profiling and demand estimation, (2) appliances profiling and fault detection, and (3) inference on sensors. Solutions in each category are presented, discussed, and compared; open perspectives and research trends are discussed as well. Compared to related state-of-the-art survey papers, the contribution herein is to provide a comprehensive and holistic review from the ML perspectives rather than architectural and technical aspects of existing building management systems. This is by considering all types of ML tools, buildings, and several categories of applications, and by structuring the taxonomy accordingly. The article ends with a summary discussion of the presented works, with focus on lessons learned, challenges, open and future directions of research in this field

APPLICATIONS OF MACHINE LEARNING AND COMPUTER VISION FOR SMART INFRASTRUCTURE MANAGEMENT IN CIVIL ENGINEERING

Machine Learning and Computer Vision are the two technologies that have innovative applications in diverse fields, including engineering, medicines, agriculture, astronomy, sports, education etc. The idea of enabling machines to make human like decisions is not a recent one. It dates to the early 1900s when analogies were drawn out between neurons in a human brain and capability of a machine to function like humans. However, major advances in the specifics of this theory were not until 1950s when the first experiments were conducted to determine if machines can support artificial intelligence. As computation powers increased, in the form of parallel computing and GPU computing, the time required for training the algorithms decreased significantly. Machine Learning is now used in almost every day to day activities. This research demonstrates the use of machine learning and computer vision for smart infrastructure management. This research’s contribution includes two case studies – a) Occupancy detection using vibration sensors and machine learning and b) Traffic detection, tracking, classification and counting on Memorial Bridge in Portsmouth, NH using computer vision and machine learning. Each case study, includes controlled experiments with a verification data set. Both the studies yielded results that validated the approach of using machine learning and computer vision. Both case studies present a scenario where in machine learning is applied to a civil engineering challenge to create a more objective basis for decision-making. This work also includes a summary of the current state-of-the -practice of machine learning in Civil Engineering and the suggested steps to advance its application in civil engineering based on this research in order to use the technology more effectively

Building Occupancy Estimation Using machine learning algorithms

Building occupancy recently has drawn the attention of many researchers. With the advance of new technologies in AI and IoT, it has become possible to further optimize building energy consumption without compromising comfort of the occupants. In this thesis project, occupancy is estimated by training models on data collected from the building called Arkivenshus in Stavanger. The data collected includes measurements of electricity consumption, ventilation, hot and cold-water consumption and PIR sensors (Passive infra-red sensors). The models that are trained are classification algorithms such as KNN, decision tree, random forest, and support vector machine. Data from the building is collected over two months period where data points are collected every 15min. Occupancy detection solutions that employ cameras, WIFI activities etc can be used to detect occupancy in buildings, however these solutions can be intrusive, costly and computationally expensive. Moreover, PIR sensors which are used for activation of lighting systems detect occupancy, they however cannot be directly related to the count of number of people. To estimate the number of people inside building I have labelled the data in five categories, where 1 represents counts less than 5, 2 represents between 5 and 25,3 represents between 25 and 50, 4 represents between 50 and 75 and for counts greater than 75 they are represented by class 5. Due to the pandemic I was not able to register number of people inside the building more than 80, which presumably has an impact on the efficiency of my model. The performance of the models are compared using various metrices, Since the data is nor balanced and I have divided the target into five classes, looking only the accuracy of a model is a bit misleading in selecting the best model. Considering accuracy, confusion matrix and learning curves of each model the best performing model is found to be SVM (Support vector machine)

A smart phone based multi-floor indoor positioning system for occupancy detection

At present there is a lot of research being done simulating building environment with artificial agents and predicting energy usage and other building performance related factors that helps to promote understanding of more sustainable buildings. To understand these energy demands it is important to understand how the building spaces are being used by individuals i.e. the occupancy pattern of individuals. There are lots of other sensors and methodology being used to understand building occupancy such as PIR sensors, logging information of Wi-Fi APs or ambient sensors such as light or CO2 composition. Indoor positioning can also play an important role in understanding building occupancy pattern. Due to the growing interest and progress being made in this field it is only a matter of time before we start to see extensive application of indoor positioning in our daily lives. This research proposes an indoor positioning system that makes use of the smart phone and its built-in integrated sensors; Wi-Fi, Bluetooth, accelerometer and gyroscope. Since smart phones are easy to carry helps participants carry on with their usual daily work without any distraction but at the same time provide a reliable pedestrian positioning solution for detecting occupancy. The positioning system uses the traditional Wi-Fi and Bluetooth fingerprinting together with pedestrian dead reckoning to develop a cheap but effective multi floor positioning solution. The paper discusses the novel application of indoor positioning technology to solve a real world problem of understanding building occupancy. It discusses the positioning methodology adopted when trying to use existing positioning algorithm and fusing multiple sensor data. It also describes the novel approach taken to identify step like motion in absence of a foot mounted inertial system. Finally the paper discusses results from limited scale trials showing trajectory of motion throughout the Nottingham Geospatial Building covering multiple floors