Instrumental Methods for Cage Occupancy Estimation of Gas Hydrate

Studies revealed that gas hydrate cages, especially small cages, are incompletely filled with guest gas molecules, primarily associated with pressure and gas composition. The ratio of hydrate cages occupied by guest molecules, defined as cage occupancy, is a critical parameter to estimate the resource amount of a natural gas hydrate reservoir and evaluate the storage capacity of methane or hydrogen hydrate as an energy storage medium and carbon dioxide hydrate as a carbon sequestration matrix. As the result, methods have been developed to investigate the cage occupancy of gas hydrate. In this review, several instrument methods widely applied for gas hydrate analysis are introduced, including Raman, NMR, XRD, neutron diffraction, and the approaches to estimate cage occupancy are summarized.

Audio Feature Engineering for Occupancy and Activity Estimation in Smart Buildings

The occupancy and activity estimation are fields that have been severally researched in the past few years. However, the different techniques used include a mixture of atmospheric features such as humidity and temperature, many devices such as cameras and audio sensors, or they are limited to speech recognition. In this work is proposed that the occupancy and activity can be estimated only from the audio information using an automatic approach of audio feature engineering to extract, analyze and select descriptors/variables. This scheme of extraction of audio descriptors is used to determine the occupation and activity in specific smart environments, such that our approach can differentiate between academic, administrative or commercial environments. Our approach from the audio feature engineering is compared to previous similar works on occupancy estimation and/or activity estimation in smart buildings (most of them including other features, such as atmospherics and visuals). In general, the results obtained are very encouraging compared to previous studies.

Privacy-Preserving Sensing and Two-Stage Building Occupancy Prediction Using Random Forest Learning

Sensing and predicting occupancy in buildings is an important task that can lead to significant improvements in both energy efficiency and occupant comfort. Rich data streams are now available that allow for machine learning-based algorithm implementation of direct and indirect occupancy estimation. We evaluate ensemble models, namely, random forests, on data collected from an 8 × 8 PIR matrix thermopile sensor with the dual goal of predicting individual cell temperature values and subsequently detecting the occupancy status. Evaluation of the method is based on a real case study deployed in an IT Hub in Bucharest, for which we have collected over three weeks of ground data, analyzed, and used it in order to predict occupancy in a room. Results show a 2–4% mean absolute percentage error for the temperature prediction and > 99% accuracy for a three-class model to detect human presence. The resulting outputs can be used by predictive building control models to optimize the commands to various subsystems. By separating the specific deployment from the system architecture and data structure, the application can be easily translated to other usage profiles and built environment entities. As compared to vision-based systems, our solution preserves privacy with improved performance when compared to single PIR or indirect estimation.

Computational Intelligence Technologies for Occupancy Estimation and Comfort Control in Buildings

This paper focuses on the development of a multi agent control system (MACS), combined with a stochastic based approach for occupancy estimation. The control framework aims to maintain the comfort levels of a building in high levels and reduce the overall energy consumption. Three independent agents, each dedicated to the thermal comfort, the visual comfort, and the indoor air quality, are deployed. A stochastic model describing the CO2 concentration has been studied, focused on the occupancy estimation problem. A probabilistic approach, as well as an evolutionary algorithm, are used to provide insights on the stochastic model. Moreover, in order to induce uncertainty, parameters are treated in a fuzzy modelling framework and the results on the occupancy estimation are investigated. In the control framework, to cope with the continuous state-action space, the three agents utilise Fuzzy Q-learning. Simulation results highlight the precision of parameter and occupancy estimation, as well as the high capabilities of the control framework, when taking into account the occupancy state, as energy consumption is reduced by 55.9%, while the overall comfort index is kept in high levels, with values close to one.