Energy Efficient Building Systems For Urban Transformation Projects:Case Study

In last decades uncontrolled rapid urbanization in Turkey led to existence of squatter areas and disaster-vulnerable building stocks.After 1999 Marmara earthquake urban renewal has become the base of urbanization politics and planning agenda in Turkey.Turkish building industry usually uses RC buildings in the urban renewal projects.In recent years cold formed steel CFS and 3D panel building systems due to its lightweight,fast constructed,energy efficient,and economy start to be used as an alternatives to reinforced concrete buildings especially in seismic areas.In this paper energy performance of three building systems were investigated on a case study school building.Analysis results shows that 3D panel and CFS buildings systems will established with 59%and 36%less energy requirements with respect to traditional reinforced concrete non-insulated buildings.

EMERGENT PERMITTING STRATEGIES FOR NATURAL BUILDING SYSTEMS IN ONTARIO

INTRODUCTION Lowering the carbon intensity of the built environment is one of many tasks that must be undertaken in order to address climate change and to encourage sustainability.The siting,design,construction,occupancy,renovation,and disposal of single-family homes are all factors that contribute to the large carbon emissions generated by the sector.There are numerous strategies that seek to minimize the amount of emissions generated by a house during its lifecycle.This paper explores the use of so-called natural building systems in building envelope construction.

Comparative Analysis of Building Rating Systems and Occupant Rating Systems

To begin with, rating systems are a beneficial tool in determining the efficiency of a building’s ability to utilise its resources effectively. In this study, the two elements under comparison are the Building Rating Systems (BRSs) and Occupant Rating Systems (ORSs). The main objective of this paper is to be able to examine the most commonly applied international and national BRS and ORS and, based on that, discover the possibility of developing an integration of both the BRS and ORS into one rating system. Quite simply, a BRS is a method by which buildings are assessed and given a score based on numerous features such as the efficiency of each of the services, total energy consumption, and alternate options of consumption. There are various BRSs that are implemented globally, each with its own set of criteria and specifications. Thus, based on the analysis of the benefits and drawbacks of both types of rating systems, it could be deduced that a well-rounded rating system with all technical and non-technical aspects combined would be beneficial to both the efficiency of the building as well as the building occupants’ health and well-being.

Identifying the validity domain of machine learning models in building energy systems

The building sector significantly contributes to climate change.To improve its carbon footprint,applications like model predictive control and predictive maintenance rely on system models.However,the high modeling effort hinders practical application.Machine learning models can significantly reduce this modeling effort.To ensure a machine learning model’s reliability in all operating states,it is essential to know its validity domain.Operating states outside the validity domain might lead to extrapolation,resulting in unpredictable behavior.This paper addresses the challenge of identifying extrapolation in data-driven building energy system models and aims to raise knowledge about it.For that,a novel approach is proposed that calibrates novelty detection algorithms towards the machine learning model.Suitable novelty detection algorithms are identified through a literature review and a benchmark test with 15 candidates.A subset of five algorithms is then evaluated on building energy systems.First,on two-dimensional data,displaying the results with a novel visualization scheme.Then on more complex multi-dimensional use cases.The methodology performs well,and the validity domain could be approximated.The visualization allows for a profound analysis and an improved understanding of the fundamental effects behind a machine learning model’s validity domain and the extrapolation regimes.

Smart Buildings for A Sustainable Development

The use of sustainable technologies for buildings, with the goal of creating an environment for living and working that uses fewer resources and generates less waste, also aims to retrofit existing buildings to be more efficient in terms of energy and water. Many cities are following this way targeting both commercial and municipal buildings. These cities are called smart cities where all life processes and nerve centers of social life are read, in order to radically improve quality of life, opportunity, prosperity, social and economic development, thanks to the use of technology. This paper deals with the study of smart buildings within smart cities, namely the use in an integrated project of computer and telematics tools with automation organized systems and passive bioclimatic strategies in architecture, determining a socio-technical management of intelligent building. The article is the result of a research carded out within the framework of intelligent buildings in the last generation cities, such as those ones with zero emissions that are taking place in the Middle East countries （Dubai, Masdar, Tiajin, and Kochi）. The topic deals with the issues of building automation as a form of technological intelligence and the study of those smart technologies integrated into the building envelope that improve its performances, making it more sustainable. The research methodology has provided a bibliographic retrieval on the state of the art and the latest technological trends in the building field, later has followed a theoretical and comparative approach of the examined technologies, which led to the development of reasoning on operation, performance and functional capabilities of a building that is both sustainable and home automation, to arrive at the final concept of sustainable intelligent building, able to combine the artificial intelligence, home automation, and technological devices of the architectural project to enhance the building energy performance. In conclusion, the proposed result is that of an integrated intelligent building in which artificial intelligence will become part of the shell-building in order to achieve high levels of energy efficiency and thus environmental sustainability.

Safe operation of online learning data driven model predictive control of building energy systems

Model predictive control is a promising approach to reduce the CO 2 emissions in the building sector.However,the vast modeling effort hampers the widescale practical application.Here,data-driven process models,like artificial neural networks,are well-suited to automatize the modeling.However,the underlying data set strongly determines the quality and reliability of artificial neural networks.In general,the validity domain of a machine learning model is limited to the data that was used to train it.Predictions based on system states outside that domain,so-called extrapolations,are unreliable and can negatively influence the control quality.We present a safe operation approach combined with online learning to deal with extrapolation in data-driven model predictive control.Here,the k-nearest neighbor algorithm is used to detect extrapolation to switch to a robust fallback controller.By continuously retraining the artificial neural networks during operation,we successively increase the validity domain of the artificial neural networks and the control quality.We apply the approach to control a building energy system provided by the BOPTEST framework.We compare controllers based on two data sets,one with extensive system excitation and one with baseline operation.The system is controlled to a fixed temperature set point in baseline operation.Therefore,the artificial neural networks trained on this data set tend to extrapolate in other operating points.We show that safe operation in combination with online learning significantly improves performance.

SUSTAINABILITY RATING SYSTEMS

There are five different publications that establish guidelines for sustainable building development that are examined in this report:(1)Leadership in Energy and Environmental Design(“LEED”);(2)CalGreen;(3)the International Green Construction Code(“IGCC”);(4)ASHRAE Standard 189.1(“Standard 189.1”);and(5)The San Francisco’s Green Building Ordinance(“SFGBO”).Having multiple publications can cause confusion among building developers,architects,engineers,building consultants,or various jurisdictions on what publication to follow,use,or reference in building development projects.This article will provide various parties involved in building development a thorough understanding of each publication and the similarities or differences between them,which will ultimately assist in identifying areas for all publications to improve.Specifically,this article demonstrates that the Material and Energy sections for all the publications must advance beyond the current requirements.Also,the comparison validates that CalGreen’s Tier 2 is similar to LEED’s local ordinances,like the SFGBO.This may mean two things:(1)LEED will need to advance its gold or platinum certification requirements,or potentially become less relevant;or(2)local ordinances should reference or adopt CalGreen Tier 2 so that there is common language between local and state regulations.This article identifies that LEED has the most stringent guidelines under the Building Site section out of all the publications.Likewise,the IGCC and Standard 189.1 have provisions under the Water Use section,that goes beyond other publications.Additionally,similar language between LEED and Standard 189.1 was found,which was unsurprising as both publications are authored by the USGBC.

A real-time abnormal operation pattern detection method for building energy systems based on association rule bases

Expert systems are effective for anomaly detection in building energy systems.However,it is usually inefficient to establish comprehensive rule bases manually for complex building energy systems.Association rule mining is available to accelerate the establishment of the rule bases due to its powerful capability of discovering rules from numerous data.This paper proposes a real-time abnormal operation pattern detection method towards building energy systems.It can benefit from both expert systems and association rule mining.Association rules are utilized to establish association rule bases of abnormal and normal operation patterns.The established rule bases are then utilized to develop an expert system for real-time detection of abnormal operation patterns.The proposed method is applied to an actual chiller plant for evaluating its performance.Results show that 15 types of known abnormal operation patterns and 11 types of unknown abnormal operation patterns are detected successfully by the proposed method.

A review of data mining technologies in building energy systems:Load prediction,pattern identification,fault detection and diagnosis

With the advent of the era of big data,buildings have become not only energy-intensive but also data-intensive.Data mining technologies have been widely utilized to release the values of massive amounts of building operation data with an aim of improving the operation performance of building energy systems.This paper aims at making a comprehensive literature review of the applications of data mining technologies in this domain.In general,data mining technologies can be classified into two categories,i.e.,supervised data mining technologies and unsupervised data mining technologies.In this field,supervised data mining technologies are usually utilized for building energy load prediction and fault detection/diagnosis.And unsupervised data mining technologies are usually utilized for building operation pattern identification and fault detection/diagnosis.Comprehensive discussions are made about the strengths and shortcomings of the data mining-based methods.Based on this review,suggestions for future researches are proposed towards effective and efficient data mining solutions for building energy systems.

Exergy Analysis of Single and Multi-Step Thermal Processes

The present paper introduces the concepts of exergy and treats it applications to analysis of the gain in exergy efficiency between one-step and multi-step thermal processes. The analysis, which is carried out with the Excel-based SEPE program, is exemplified with the comparison between single-step and two-steps heat pump setup for providing heat to a floor heating system and for domestic hot water. The paper discusses the use of the concept of exergy efficiency as a measure of success for design of a heat pump application and how the use of information on exergy destruction and temperature levels in different parts of the system add a new perspective to the analysis and the evaluation of the system performance. The paper shows how this information can be used to improve the system configuration and also the operation of the system for given boundary conditions. This is especially useful when the energy from the low temperature sources can be utilized at different temperature or quality levels such as for space heating and domestic hot water.

Development of a Long-Term Operational Optimization Model for a Building Energy System Supplied by a Geothermal Field

In order to reduce energy consumption and CO_(2) emissions in the building sector, more and more renewable energy sources are integrated into energy systems. Especially geothermal fields combined with heat pumps are able to supply buildings with heat and cold at low carbon emissions. However, using geothermal fields as heat and cold source influences the ground temperature. Consequently, the ground temperature can change dramatically over a building’s lifetime, leading to less efficient operation of the energy system. Therefore, a sustainable operation is required to ensure the long-term efficiency of geothermal fields. In this paper, we develop an optimization model to derive operating strategies for an efficient long-term operation of a building energy system coupled to a geothermal field. The investigated energy system is the main building of the E.ON Energy Research Center in Aachen, Germany, which includes a heat pump, two boilers, a combined heat, and power unit, a glycol cooler, and a geothermal field with 41 probes. For each component, we develop energy-based sub-models, which are connected to form the overall system. The geothermal field is modeled by using a g-functions approach as well as a simplified resistance-capacitance approach. To achieve short computing times and realize an optimization horizon of several years, the optimization problem is formulated as mixed-integer linear programming(MILP). The developed model is optimized regarding two different objectives: the minimization of energy costs and the minimization of long-term temperature changes in the ground. Conclusions for an efficient and sustainable operation of the field, especially for the cooling supply, can be derived from the optimization results. It is shown that a state of equilibrium should be aimed to achieve an energy-efficient operation, in which the temperature of the field is close to the initial ground temperature.

A Practical Approach to Representation of Real-time Building Control Applications in Simulation

Computer based automation and control systems are becoming increasingly important in smart sustainable buildings,often referred to as automated buildings(ABs),in order to automatically control,optimize and supervise a wide range of building performance applications over a network while minimizing energy consumption and associated green house gas emission.This technology generally refers to building automation and control systems(BACS)architecture.Instead of costly and time-consuming experiments,this paper focuses on development and design of a distributed dynamic simulation environment with the capability to represent BACS architecture in simulation by run-time coupling two or more different software tools over a network.This involves using distributed dynamic simulations as means to analyze the performance and enhance networked real-time control systems in ABs and improve the functions of real BACS technology.The application and capability of this new dynamic simulation environment are demonstrated by an experimental design,in this paper.

AI in HVAC fault detection and diagnosis:A systematic review

Recent studies show that artificial intelligence(AI),such as machine learning and deep learning,models can be adopted and have advantages in fault detection and diagnosis for building energy systems.This paper aims to conduct a comprehensive and systematic literature review on fault detection and diagnosis(FDD)methods for heating,ventilation,and air conditioning(HVAC)systems.This review covers the period from 2013 to 2023 to identify and analyze the existing research in this field.Our work concentrates explicitly on synthesizing AI-based FDD techniques,particularly summarizing these methods and offering a comprehensive classification.First,we discuss the challenges while developing FDD methods for HVAC systems.Next,we classify AI-based FDD methods into three categories:those based on traditional machine learning,deep learning,and hybrid AI models.Additionally,we also examine physical model-based methods to compare them with AI-based methods.The analysis concludes that AI-based HVAC FDD,despite its higher accuracy and reduced reliance on expert knowledge,has garnered considerable research interest compared to physics-based methods.However,it still encounters difficulties in dynamic and time-varying environments and achieving FDD resolution.Addressing these challenges is essential to facilitate the widespread adoption of AI-based FDD in HVAC.