No description available.

In September 2020, Northeast China suffered three unprecedented typhoons in half a month, and there was freezing rain in early November, all of which led to the large-scale urban power failure. The occurrence of these phenomena makes people directly see climate change and its impact on the living environment of human beings. Many studies have shown that the cause of climate change is the increase of artificial greenhouse gas emissions since industrialization. In addition to the increase of extreme weather disasters, the most direct manifestations of climate change are the rising temperature, droughts, and rising sea levels. The building sector accounts for 39% of global greenhouse gas emissions and 36% of energy consumption. To ensure the long-term integrity and normal operation of buildings, we need to understand the impact of climate on buildings, and how to deal with it. This paper reviews the literature on climate change and building energy by searching search engines and literature databases. For extreme weather, most literature talks about the impact of power failure, the main strategy is to improve reliability, resilience, sustainability, and robustness, it can help reduce losses and recover as soon as possible. On the other hand, the methods of adaptation to and mitigation of non-disaster weather are reviewed from the perspective of sustainability. This paper mainly reviews the methods of passive technology and strategy for exemplary buildings, building envelope, passive ventilation, lighting/shading, solar energy, bioenergy, dehumidification, passive cooling, and design strategy. According to the local climate, the geographical characteristics of the building, to develop comprehensive passive technology and strategy, can meet or close to meet their energy saving, emission reduction, comfort needs. This paper can provide a technical and strategic reference for the building sector to deal with climate change. / <p></p><p>Via online ZOOM meeting Presentation</p><p></p><p></p><p></p><p></p><p></p><p></p>

Building energy models are used to forecast building energy use to design and control efficient building energy systems. Building energy use can generally be decomposed into heating, ventilation and air conditioning, refrigeration, appliance and lighting loads. These loads will depend on multiple factors such as outdoor weather conditions, occupants, building type, controls and scheduling. Data-driven models have become more popular with the increase in smart meter data available that can be used to train and fit the models. Additionally, buildings with high refrigeration loads have greater heat harvesting potential, however, few data-driven models have been developed for buildings such as supermarkets and ice rinks. In this work, linear regression models are used to predict the disaggregated space cooling, heating, baseload and refrigeration components of building energy use. In most cases, measured aggregate electricity use is available, however individual appliances or component loads require submetering equipment which can be expensive. Therefore the proposed models use time-based and weather features to separate the thermal and baseload portions of the electrical load. A generalized approach is also used to predict new buildings with data from existing buildings. Furthermore, a simplified model is used to predict hourly space heating from monthly natural gas measurements and hourly weather measurements. The models were evaluated on real data from buildings in Ontario and the disaggregated loads were verified with synthetic data. The results found that aggregate use was predicted reasonably well using linear regression methods, with most building types having a median normalized root mean squared error between 0.2 and 0.3, depending on the forecasting period. The model is flexible as it does not require detailed information related to the building such as lighting or setpoint schedules, however, it can be adapted in the future to include additional information and improve predictive capability. / Thesis / Master of Applied Science (MASc)

The environmental impact from our energy production and use today is a central concern for every major decision maker regardless of interest area. Together with transport and industry, housing and services is a major contributor to our energy consumption. In 2013 housing and services accounted for 38% of the energy consumption in Sweden Therefore, energy consumption in buildings has become an area of great importance with many technological solutions developing to meet the demands from investors and legislation. However, present solutions primarily aim to solve current problems, while ongoing technological and social development is setting new conditions for future buildings. This study investigates the future of office buildings heading towards a more flexible work environment. Advancement of technology accompanied with new emerging economic and social practices is facilitating the flexibility, as well as remote work and more out-of-office time, with possible decrease of the thermal energy produced by humans and equipment. In addition, desktop computers will be replaced by energy efficient thin clients, tablets, and phones while other equipment is also becoming more energy efficient, resulting in reduced secondary heat production and thus lower internal gains. This scenario supposes reduced internal gains, resulting in decreased cooling requirements but also increased heating requirements. However, an alternative scenario with increased internal gains is also likely, due to activity-based workplaces. Activity-based offices dispose of personal desks, instead utilising activity-based areas where employees choose an area or desk where to work, depending on their current task. Disposing of personal desks supports higher occupancy, as employees working elsewhere don’t occupy workplaces at the office. Thus, the amount of desks can be matched to the actual amount of employees working at the office during peak loads. These scenarios are developed, quantified, and used as a basis for the building simulation models. These models are then optimised to meet these new conditions, utilising simulation and multi objective optimisation. The key finding is that office buildings are resilient to changing conditions, and that a state-of-theart office from today meet the demands of tomorrow. / Miljöpåverkan från vår produktion och energianvändning är idag är en central fråga för varje större beslutsfattare oavsett intresse i området. Tillsammans med transport och industri, är bostäder och service en viktig bidragande orsak till vår energiförbrukning. År 2013 bostäder och service stod för 38% av energiförbrukningen i Sverige. Därför har energiförbrukningen i byggnader kommit att få stor betydelse, vilket driver utvecklingen mot nya tekniska lösningar för att möta kraven från investerare och lagstiftning. De nuvarande lösningarna syftar dock främst till att lösa nuvarande problem, samtidigt som teknisk och social utveckling skapar nya förutsättningar för framtida byggnader. Detta projekt undersöker framtiden för kontorsbyggnader, där utvecklingen verkar vara på väg mot distansarbete, outsourcing, och mer arbetstid spenderad utanför kontoret, vilket minskar mängden interna laster som värmer upp kontoret. Samtidigt utvecklas stationära datorer och annan utrustning som ersättas med energieffektiva tunna klienter, tablets, och smarta telefoner, vilket minskar de interna lasterna ytterligare. Ett alternativt scenario är aktivitetsbaserade kontor, där de anställda inte har sin egen arbetsyta, utan istället använder aktivitetsbaserade områden beroende på arbetsuppgift. Detta scenario kan tänkas leda till ökade interna laster då ytan kan användas mer effektivt, som kan kompensera för användning av mer energieffektiv kontorsutrustning. Projektet undersöker tänkbara framtida scenarier och hur framtida kontor kan anpassas för att möta dessa nya förutsättningar med hjälp av klimat- och energisimuleringsmjukvara. Resultaten tyder på flera tydliga trender i användningen av kontorsbyggnader, men effekterna av dessa trender kan resultera i flera scenarier. Därför projektet omfattar flera scenarier för att utvärdera spannet av möjligheter. Simuleringarna tyder på att kontorsbyggnader är motståndskraftiga mot förändrade villkor, och att ett modernt kontor från idag kan möta morgondagens behov.

No description available.

Balancing indoor comfort and energy consumption is crucial to building energy efficiency. Occupancy information is a vital aspect in this process, as it determines the energy demand. Although there are various sensors used to gather occupancy information, environmental sensors stand out due to their low cost and privacy benefits. Machine learning algorithms play a critical role in estimating the relationship between occupancy levels and environmental data. To improve performance, more complex models such as deep learning algorithms are necessary. Long Short-Term Memory (LSTM) is a powerful deep learning algorithm that has been utilized in occupancy estimation. However, recently, an algorithm named Attention has emerged with improved performance. The study proposes a more effective model for occupancy level estimation by incorporating Attention into the existing Long Short-Term Memory algorithm. The results show that the proposed model is more accurate than using a single algorithm and has the potential to be integrated into building energy control systems to conserve even more energy. / Master of Science / The motivation for energy conservation and sustainable development is rapidly increasing, and building energy consumption is a significant part of overall energy use. In order to make buildings more energy efficient, it is necessary to obtain information on the occupancy level of rooms in the building. Environmental sensors are used to measure factors such as humidity and sound to determine occupancy information. However, the relationship between sensor readings and occupancy levels is complex, making it necessary to use machine learning algorithms to establish a connection. As a subfield of machine learning, deep learning is capable of processing complex data. This research aims to utilize advanced deep learning algorithms to estimate building occupancy levels based on environmental sensor data.

The work presented in this thesis focuses on reducing the energy demand of a residential building by using a coupled ground-source heat pump/energy recovery ventilation (GSHP-ERV) system to present a novel approach to space condition and domestic hot water supply for a residence. The proposed system is capable of providing hot water on-demand with a high coefficient of performance (COP), thus eliminating the need for a hot water storage tank and circulation system while requiring little power consumption. The necessary size of the proposed system and the maximum and normal heating and cooling loads for the home were calculated based on the assumptions of an energy efficient home, the assumed construction specifications, and the climate characteristics of the Blacksburg, Virginia region. The results from the load analysis were used to predict energy consumption and costs associated with annual operations.The results for the predicted heating annual energy consumption and costs for the GSHP-ERV system were compared to an air-source heat pump and a natural gas furnace. On average, it was determined that the proposed system was capable of reducing annual energy consumption by 56-78% over air-source heat pumps and 85-88% over a natural gas furnace. The proposed GSHP-ERV system reduced costs by 45-61% over air-source heat pump systems and 52-58% over natural gas furnaces. The annual energy consumption and costs associated with cooling were not calculated as cooling accounts for a negligible portion (6%) of the total annual energy demand for a home in Blacksburg. / Master of Science

In the United States, over 40% of the country's total energy consumption is in buildings, most of which are either small-sized (<5,000 sqft) or medium-sized (5,000-50,000 sqft). These buildings offer excellent opportunities for energy saving and demand response (DR), but these opportunities are rarely utilized due to lack of effective building energy management systems and automated algorithms that can assist a building to participate in a DR program. Considering the low load factor in US and many other countries, DR can serve as an effective tool to reduce peak demand through demand-side load curtailment. A convenient option for the customer to benefit from a DR program is to use automated DR algorithms within a software that can learn user comfort preferences for the building loads and make automated load curtailment decisions without affecting customer comfort. The objective of this dissertation is to provide such a solution. First, this dissertation contributes to the development of key features of a building energy management open source software platform that enable ease-of-use through plug and play and interoperability of devices in a building, cost-effectiveness through deployment in a low-cost computer, and DR through communication infrastructure between building and utility and among multiple buildings, while ensuring security of the platform. Second, a set of reinforcement learning (RL) based algorithms is proposed for the three main types of loads in a building: heating, ventilation and air conditioning (HVAC) loads, lighting loads and plug loads. In absence of a DR program, these distributed agent-based learning algorithms are designed to learn the user comfort ranges through explorative interaction with the environment and accumulating user feedback, and then operate through policies that favor maximum user benefit in terms of saving energy while ensuring comfort. Third, two sets of DR algorithms are proposed for an incentive-based DR program in a building. A user-defined priority based DR algorithm with smart thermostat control and utilization of distributed energy resources (DER) is proposed for residential buildings. For commercial buildings, a learning-based algorithm is proposed that utilizes the learning from the RL algorithms to use a pre-cooling/pre-heating based load reduction method for HVAC loads and a mixed integer linear programming (MILP) based optimization method for other loads to dynamically maintain total building demand below a demand limit set by the utility during a DR event, while minimizing total user discomfort. A user defined priority based DR algorithm is also proposed for multiple buildings in a community so that they can participate in realizing combined DR objectives. The software solution proposed in this dissertation is expected to encourage increased participation of smaller and medium-sized buildings in demand response and energy saving activities. This will help in alleviating power system stress conditions by employing the untapped DR potential in such buildings. / Ph. D.

The efficiency, security and resiliency are very important factors for the operation of a distribution power system. Taking into account customer demand and energy resource constraints, electric utilities not only need to provide reliable services but also need to operate a power grid as efficiently as possible. The objective of this dissertation is to design, develop and deploy the Multi-Agent Systems (MAS) - together with control algorithms - that enable demand response (DR) implementation at the customer level, focusing on both residential and commercial customers. For residential applications, the main objective is to propose an approach for a smart distribution transformer management. The DR objective at a distribution transformer is to ensure that the instantaneous power demand at a distribution transformer is kept below a certain demand limit while impacts of demand restrike are minimized. The DR objectives at residential homes are to secure critical loads, mitigate occupant comfort violation, and minimize appliance run-time after a DR event. For commercial applications, the goal is to propose a MAS architecture and platform that help facilitate the implementation of a Critical Peak Pricing (CPP) program. Main objectives of the proposed DR algorithm are to minimize power demand and energy consumption during a period that a CPP event is called out, to minimize occupant comfort violation, to minimize impacts of demand restrike after a CPP event, as well as to control the device operation to avoid restrikes. Overall, this study provides an insight into the design and implementation of MAS, together with associated control algorithms for DR implementation in smart buildings. The proposed approaches can serve as alternative solutions to the current practices of electric utilities to engage end-use customers to participate in DR programs where occupancy level, tenant comfort condition and preference, as well as controllable devices and sensors are taken into account in both simulated and real-world environments. Research findings show that the proposed DR algorithms can perform effectively and efficiently during a DR event in residential homes and during the CPP event in commercial buildings. / Ph. D.

Commercial buildings consume more than 40% of the total energy consumption in the United States. Almost 90% of these buildings are small- and medium-sized buildings that do not have a Building Energy Management (BEM) system. The reasons behind this are – lack of awareness, unavailability of inexpensive packaged solutions, and disincentive to invest in a BEM system if the tenant is not the owner. Several open source tools and technologies have emerged recently that can be used for building automation and energy management. However, none of these systems is turnkey and deployment ready. They also lack consistent and intuitive navigation, security, and performance required for a BEM system. The overall project - of which this thesis research is a part - addresses the design and implementation of an open source secure web based user platform to monitor, schedule, control, and perform functions needed for a BEM system serving small and medium-size buildings. The focus of this work are: principles of intuitive graphical user interface design, abstracting device functions into a comprehensive data model, identifying threats and vulnerabilities, and implementing a security framework for the web platform. Monitor and control solutions for devices such as load controllers and sensors are abstracted and their decentralized control strategies are proposed and implemented using an open source robust scalable user platform accessible locally and remotely. The user platform is open-source, scalable, provides role-based access, dynamic, and modular in design. The comprehensive data model includes a user management model, device model, session model, and a scheduling model. The data model is designed to be flexible, robust and can be extended for any new device type. Security risks are analyzed using a threat model to identify security goals. The proposed security framework includes user authentication, device approval, role-based access, secure information exchange protocols, and web platform security. Performance of the user interface platform is evaluated for responsiveness in different screen sizes, page response times, throughput, and the performance of client side entities. / Master of Science