Developing a Thermal Comfort Report Card for Building

AbstractBuildings consume 40% of total energy in the United States and approximately 48% of which is consumed by Heating Ventilation and Air Conditioning (HVAC). This highlights the importance of developing robust and dynamic Building Monitoring Systems (BMS) that are capable of providing the optimal operation of HVAC systems in terms of maximizing thermal comfort of building occupants while minimizing energy consumptions. Numerous empirical studies have demonstrated that occupant behavior is a key factor underlying energy consumption in existing buildings. However, few if any reliable data sets exist documenting precise human activities and their associated occupant comfort levels within buildings. Furthermore, little if anything is known about how this information directly relates to building energy performance. This research documents on-going development of software prototype tools for modeling thermal comfort in buildings based on real-time occupant and building systems data. The outcomes help building owners to identify areas that require improvements with regard to thermal comfort with broader impacts that improve occupant productivity, comfort, and well-being. The primary technical contribution is to model human comfort on the building level based on actual occupant usage, in order to identify and target energy efficiency measures that optimize energy usage according to comfort rather than maximum energy savings alone. Future research will synthesize building occupant and sensor data to support regression analysis that may identify the correlation of the reported thermal comfort, activities of building occupants, and building conditions. Such data may also be used to develop algorithms for controlling interior lighting, exhaust fans, ventilation, and HVAC temperature set points that optimize comfort while minimizing energy demands

Cross-source sensing data fusion for building occupancy prediction with adaptive lasso feature filtering

Fusing various sensing data sources can significantly improve the accuracy and reliability of building occupancy detection. Fusing environmental sensors and wireless network signals are seldom studied for its computational and technical complexity. This study aims to propose an integrated adaptive lasso model that is able to extract critical data features for environmental and Wi-Fi probe dual sensing sources. Through rapid feature extraction and process simplification, the proposed method aims to improve the computational efficiency of occupancy detecting models. To validate the proposed model, an onsite experiment was conducted to examine two occupancy data resolutions, (real-time and four-level occupancy resolutions). The results suggested that, among all twelve features, eight features are most relevant. The mean absolute error of the real-time occupancy can be reduced to 2.18 and F1_accuracy is about 84.36% for the four-level occupancy

The design of an indirect method for the human presence monitoring in the intelligent building

This article describes the design and verification of the indirect method of predicting the course of CO2 concentration (ppm) from the measured temperature variables Tindoor (degrees C) and the relative humidity rH(indoor) (%) and the temperature T-outdoor (degrees C) using the Artificial Neural Network (ANN) with the Bayesian Regulation Method (BRM) for monitoring the presence of people in the individual premises in the Intelligent Administrative Building (IAB) using the PI System SW Tool (PI-Plant Information enterprise information system). The CA (Correlation Analysis), the MSE (Root Mean Squared Error) and the DTW (Dynamic Time Warping) criteria were used to verify and classify the results obtained. Within the proposed method, the LMS adaptive filter algorithm was used to remove the noise of the resulting predicted course. In order to verify the method, two long-term experiments were performed, specifically from February 1 to February 28, 2015, from June 1 to June 28, 2015 and from February 8 to February 14, 2015. For the best results of the trained ANN BRM within the prediction of CO2, the correlation coefficient R for the proposed method was up to 92%. The verification of the proposed method confirmed the possibility to use the presence of people of the monitored IAB premises for monitoring. The designed indirect method of CO2 prediction has potential for reducing the investment and operating costs of the IAB in relation to the reduction of the number of implemented sensors in the IAB within the process of management of operational and technical functions in the IAB. The article also describes the design and implementation of the FEIVISUAL visualization application for mobile devices, which monitors the technological processes in the IAB. This application is optimized for Android devices and is platform independent. The application requires implementation of an application server that communicates with the data server and the application developed. The data of the application developed is obtained from the data storage of the PI System via a PI Web REST API (Application Programming Integration) client.Web of Science8art. no. 2

Demonstrating the potential of indoor positioning for monitoring building occupancy through ecologically valid trials

Assessing building performance related to energy consumption in post-design-occupancy stage requires knowledge of building occupancy pattern. These occupancy data can potentially be collected from trials and used to improve the prediction capability of building performance models. Due to the limitations of passive sensors in detecting an individual’s occupancy throughout the building, indoor positioning can provide a viable alternative. Previous work on using indoor positioning techniques for detecting building occupancy mainly focused on passive monitoring through Wi-Fi or BLE proximity sensing by estimating the number of occupants at any given time. This paper extends our previous research and demonstrates the merit of occupancy monitoring through active tracking at an individual level using a smartphone-based multi-floor indoor positioning system. The paper discusses the design of a novel occupancy detection trial setup, mimicking real-world office occupancy and discusses the outcome of the ecologically valid trials using the developed positioning system. In total 50 occupancy trials were carried out by around 22 participants comprising of a variety of routes within the building. The trial results are presented to demonstrate the level of accuracy achievable against a specific set of the performance metric necessary for building occupancy detection and modelling

Non-Intrusive Occupancy Detection Methods and Models

Occupants in the built environment impact facility energy consumption and indoor air quality. Predicting the presence of occupants within the built environment can therefore be used to manage these factors while providing additional benefits in terms of emergency management and future space utilization. Detecting occupancy requires a combination of sensors and models to accurate assess data collected within facilities to predict occupancy. This thesis investigated occupancy detection through a non-invasive data collection sensors and model. Specifically, this thesis sought to answer two research questions examining the ability of a radial basis function to accurately predict occupancy when generated from data collected from two facilities. Generated models were evaluated on the data from which they were derived, self-estimation, as well as applied to other areas within the same facility, cross-estimation. The motivation, sensors and models, were discussed to establish a framework. The principle implications of this research is to reduce energy consumption by knowing when the built environment is occupied through the use of non-invasive data collection sensors supplying inputs into a model. The resulting accuracy rates of the derived models ranged from 48% - 68% when using three collected parameters: temperature, relative humidity and carbon dioxide

Investigating the impact of actual and modeled occupant behavior information input to building performance simulation

© 2021 by the authors. Licensee MDPI, Basel, Switzerland. Occupant behaviors are one of the most dominant factors that influence building energy use. Understanding the influences from building occupants can promote the development of energy- efficient buildings. This paper quantifies the impact of different occupant behavior information on building energy model (BEM) from multiple perspectives. For this purpose, an occupant behavior model that uses agent-based modeling (ABM) approach is implemented via co-simulation with a BEM of an existing commercial building. Then, actual occupant behavior data in correspondence to ABM output, including operations on window, door, and blinds in selected thermal zones of the building are recorded using survey logs. A simulation experiment is conducted by creating three BEMs with constant, actual, and modeled occupant behavioral inputs. The analysis of the simulation results among these scenarios helps us gain an in-depth understanding of how occupant behaviors influence building performance. This study aims to facilitate robust building design and operation with human-in-the-loop system optimization

Occupancy estimation and people flow prediction in smart environments

Two related problems have been analysed. Inthe one hand, the problem of detecting people by using indoor climate monitoring infrastructure is analysed, while on the other hand, predicting the amount of people in one space based on some criteria is studied. These two problems are grouped in the Ambient Intelligence (AmI) research field. In the smart building and cities (SBC) are avarious research paths are gaining increasing attention, especially with the advances in the Internet of Things (IoT) paradigm and the Big Data analysis. Some hot topics in this research field include city security, surveillance, providing more efficient public services, event scheduling, etc. The analysed problems are introduced with the state of the art for each one, current research paths and possible limitations of the proposed methods are also mentioned. In the last section of this chapter some supervised learning algorithms used in this work are introduced and explained

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/