A multifaceted analysis of COVID-19 propagation in confined spaces: a techno-economic assessment of ventilation, heating, and renewables integration

vii, 73 p., xiiThe outbreak of novel coronavirus disease 2019 (COVID19) has spread rapidly, affecting nearly all countries and territories around the globe, impacting every aspect of human life. Governments and various organizations worldwide have issued mitigation measures to counteract COVID-19 virus propagations, whether in indoor spaces or outdoors. Although the underlying uncertainty concerning COVID-19 transmission details, most international organizations such as WHO, ECDC, ASHRAE, REHVA, and CIBSE agree on the important role of ventilation to minimize the causes and reduce the viability of SARS-CoV-2 in confined spaces. Given that natural ventilation is variable, which depends on the intermittent wind source, mechanical ventilation systems provide stable airflow rates that ensure reliability and adequacy to meet the minimum ventilation rates for building users in a controlled environment. Thus, a paradigm shift in the mechanical ventilation system is needed to steer the focus from the predominant energy efficient space-based design to occupant-based design. This study will discuss the cost-related effects to ensure stable and adequate ventilation by setting up ventilation scenarios with parameters derived from the recommendations published in recent guidelines focusing on HVAC operations. A working methodology is applied to a case study on two zones, an office, and a nursery. The results show that maintaining a minimum of five and seven air changes per hour for office and nursery, respectively, with proper indoor air distribution can reduce the risk of infection by more than half while ensuring an economic balance between ventilation costs and infection risk. Additionally, the study suggests using photovoltaics installations to power ventilation rates higher than five air changes per hour which can save at least forty-five tons of CO2 while reaching a payback period in thirteen years. Based on the achieved results, the paper presents recommendations to operate the two zones’ ventilation, space heating, and photovoltaics cost-effectively while ensuring COVID-19 probability of infection reduction

Model predictive control for building temperature regulation

With the rapid increase in human consumption of energy, global warming is becoming an urgent problem. According to the past research, a significant part of greenhouse gas release and electricity consumption has been connected to buildings. Heating systems are the major contributor to high energy consumption of buildings. Better methods for decreasing energy consumption of buildings should be proposed and applied. Model predictive control is widely used in building temperature regulation. Each model predictive control has its advantage. Through the comparison of several methods, this thesis discusses their respective features. The combination of a Matlab-based modeling system CVX and model predictive control makes the optimization easy. A new idea for a model predictive control method about temperature regulation is proposed. The simulation of the disturbance will be implemented as an input. The demonstration of its advantage is shown. This thesis uses temperature index and energy index to help people evaluate the result. An index tuner is put forward to simplify the optimization

Shared solar and battery storage configuration effectiveness for reducing the grid reliance of apartment complexes

More than 2 million houses in Australia have installed solar photovoltaic (PV) systems; however, apartment buildings have adopted a low percentage of solar PV and battery storage installations. Given that grid usage reduction through PV and battery storage is a primary objective in most residential buildings, apartments have not yet fully benefited from installations of such systems. This research presents shared microgrid configurations for three apartment buildings with PV and battery storage and evaluates the reduction in grid electricity usage by analyzing self-sufficiency. The results reveal that the three studied sites at White Gum Valley achieved an overall self-sufficiency of more than 60%. Owing to the infancy of the shared solar and battery storage market for apartment complexes and lack of available data, this study fills the research gap by presenting preliminary quantitative findings from implementation in apartment buildings

Indoor Air Quality Design and Control in Low-Energy Residential Buildings: Current challenges, selected case studies and innovative solutions covering indoor air quality, ventilation design and control in residences. International Energy Agency Report, Energy in Buildings and Communities Programme, IEA EBC Annex 68

The objective of Subtask 4 in the IEA EBC Annec 68 was to integrate knowledge and results from remaining Subtasks and present them in the context with current knowledge. The focus of the Subtask 4 was on practitioners dealing with ensuring high Indoor Air Quality (IAQ) in modern low-energy residences, the demands and challenges they meet during daily work. This especially includes architects and ventilation designers, facility managers, property developers and employees of public authorities. This publication is a result if Subtask 4’s work. It brings a collection of 24 “case studies” related to IAQ design and control in Low-Energy Residential Buildings. By a “case study” we mean a real life construction project, laboratory investigation or a simulation study that provides innovative approach. The case studies were selected to give the practitioners new insigts, inspiration and motivation to go along new paths leading to sustainable and comfortable homes of the future. The report is organized into three main chapters: “Ways to design residential ventilation in the future” and “Towards better performance and user satisfaction”. The descriptions of case studies are accompanied by “lessons learned” sections aiming directly at practical utilization of results as well as recommended future reading section providing the most important references

Scaling energy management in buildings with artificial intelligence

L'abstract è presente nell'allegato / the abstract is in the attachmen

SPACERGY:

SPACERGY builds upon the need for planning authorities to develop new models to implement energy transition strategies in the urban environment, departing from the exploitation or reciprocity between space and energy systems. Several policies have been made by each EU nation, but effective and practical tools to guide the urban transformations towards a carbon-neutral future present several challenges. The first challenge is to confront long term changes in envisioning how a specific socio-cultural context can respond to the application of solutions for energy efficiency. Secondly, the engagement of communities in bottom-up approaches mainly includes the sphere of urban planning that underestimates the importance of relating spatial transformations with the energy performances generated in the urban environment. The third challenge regards the tools used for the assessment of the energy performance and the necessity of enlarging the scale in which energy demand is analyzed, from the scale of the building to that of the district. In this context, the project explores the role of mobility, spatial morphologies, infrastructural elements and local community participation in regards to the smart use of local resources. The project addresses a knowledge gap in relation to interactions and synergies between spatial programming, energy and mobility systems planning and stakeholder involvement necessary to improve models of development and governance of urban transformations. Based on detailed spatial morphology and energy use modeling, SPACERGY develops new toolsets and guidelines necessary to advance the implementation of energy-efficient urban districts. New toolsets are tested in three urban areas under development in the cities of Zurich, Almere, and Bergen, acting as living laboratories for real-time research and action in collaboration with local stakeholders. The results of this research project support planners and decision-makers to facilitate the transition of their communities to more efficient, livable and thus prosperous urban environments

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

Occupant-Centric Modeling and Control for Low-Carbon and Resilient Communities

Global climate change and resulting frequent extreme weather events have highlighted the significance of energy sustainability and resilience. Communities, which refer to a group of buildings located geographically together, are important units for energy generation and consumption. Hence, the research of community energy sustainability and resilience has drawn much attention during the past decades. However, there remain many challenges surrounding community energy modeling and control to achieve the low-carbon and resilient goals. First, few tools are readily available for community-scale dynamic modeling and control-based studies. To address this gap, a community emulator was developed, which was designed to be hierarchical, scalable, and suitable for various applications. Data-driven stochastic building occupancy prediction was integrated into the emulator using logistic regression methods. Based on this work, we publicly released a library for net-zero energy community modeling using the object-oriented equation-based modeling language Modelica. Second, building load control informed by real-time carbon emission signals is underdeveloped as utility price-driven control has so far been dominant. To better facilitate community energy sustainability through decarbonization, we proposed four rule-based carbon emission responsive building control algorithms to reduce the annual carbon emissions through thermostatically controllable loads. The impact of carbon net-metering, as well as the evolvement of the future energy generation mix, is analyzed on top of both momentary and predictive rules. Based on the simulation results, the average annual household carbon emissions are decreased by 6.0% to 20.5% compared to the baseline. The average annual energy consumption is increased by less than 6.7% due to more clean hours over the year. The annual energy cost change lies between -4.1% and 3.4% on top of the baseline. Third, the enhancement of community resilience in an islanded mode through optimal operation strategies is often faced with computational challenges given the large number of controllable loads. To tackle this, we proposed a two-layer model predictive control-based resource allocation and load scheduling framework for community resilience enhancement. Within this framework, the community operator layer optimally allocates the available PV generation to each building, while the building agent layer optimally schedules controllable loads to minimize the unserved load ratio while maintaining thermal comfort. We found that the allocation process is mostly constrained by the building load flexibility. More specifically, buildings with less load flexibility tend to be allocated more PV generation than other buildings. Further, we identified the competitive relationship between the objectives of minimizing unserved load ratio and maximize comfort. Therefore, it is necessary for the building agent to have multi-objective optimization. Finally, to account for the uncertainties of occupant behavior and its impact on resilient community load scheduling, we developed a preference-aware scheduler for resilient communities. Stochastic occupant thermostat-changing behavior models were introduced into the deterministic load scheduling framework as a source of uncertainty. KRIs such as the unserved load ratio, the required battery size, and the unmet thermal preference hours were adopted to quantify the impacts. Uncertainties from occupants&rsquo; thermal preferences and their impact on load scheduling are then studied and addressed through chance constraints. Generally, the proposed controller performs better in terms of the unmet thermal preference hours and the battery sizes compared to the deterministic controller.</p

An investigation into the energy and control implications of adaptive comfort in a modern office building

PhD ThesisAn investigation into the potentials of adaptive comfort in an office building is carried out using fine grained primary data and computer modelling. A comprehensive literature review and background study into energy and comfort aspects of building management provides the backdrop against which a target building is subjected to energy and comfort audit, virtual simulation and impact assessment of adaptive comfort standard (BS EN 15251: 2007). Building fabric design is also brought into focus by examining 2006 and 2010 Approved Document part L potentials against Passive House design. This is to reflect the general direction of regulatory development which tends toward zero carbon design by the end of this decade. In finishing a study of modern controls in buildings is carried out to assess the strongest contenders that next generation heating, ventilation and air-conditioning technologies will come to rely on in future buildings. An actual target building constitutes the vehicle for the work described above. A virtual model of this building was calibrated against an extensive set of actual data using version control method. The results were improved to surpass ASHRAE Guide 14. A set of different scenarios were constructed to account for improved fabric design as well as historical weather files and future weather predictions. These scenarios enabled a comparative study to investigate the effect of BS EN 15251:2007 when compared to conventional space controls. The main finding is that modern commercial buildings built to the latest UK statutory regulations can achieve considerable carbon savings through adaptive comfort standard. However these savings are only modestly improved if fabric design is enhanced to passive house levels. Adaptive comfort can also be readily deployed using current web-enabled control applications. However an actual field study is necessary to provide invaluable insight into occupants’ acceptance of this standard since winter-time space temperature results derived from BS EN 15251:2007 constitute a notable departure from CIBSE environmental guidelines