Search

Assessing building energy consumption is of paramount significance in sustainability and energy efficiency (EE) studies. The development of an accurate EE prediction model is pivotal for optimizing energy resources and facilitating effective building planning. Traditional physical modeling approaches are encumbered by high complexity and protracted modeling cycles. In this paper, we introduce a novel evolutionary dendritic neural regression (EDNR) model tailored to forecasting residential building EE. Acknowledging the vast landscape and complexity of the EDNR weight space, coupled with the inherent susceptibility of traditional optimization algorithms to local optima, we propose a complex network-guided strategy-based differential evolution algorithm for training the EDNR model. This strategy adeptly strikes a balance between exploration and exploitation during the search process, significantly enhancing the predictive and generalization capacities of EDNR. To our knowledge, this study represents the inaugural application of dendritic neural regression in real-world prediction scenarios. Extensive experimental findings demonstrate the efficacy of EDNR in accurately predicting building EE with commendable performance. Furthermore, the results of two nonparametric statistical tests affirm the validity and stability of EDNR. Consequently, our proposed methodology exhibits high potential and competitiveness in machine learning applications within the energy domain. [ABSTRACT FROM AUTHOR]

Global warming, climate change and the energy crisis are trending topics around the world, especially within the energy sector. The rising cost of energy, greenhouse gas (GHG) emissions and global temperatures stem from the over-reliance on fossil fuel as the major energy resource. These challenges have highlighted the need for alternative energy resources and urgent intervention strategies like energy consumption reduction and improving energy efficiency. The heating, ventilation, and air-conditioning (HVAC) system in a building accounts for about 70% of energy consumption, and a decision to reduce energy consumption may impact the indoor environmental quality (IEQ) of the building. It is important to adequately balance the tradeoff between IEQ and energy management. Artificial intelligence (AI)-based solutions are being explored for improving building energy performance without compromising IEQ. This paper systematically reviews recent studies on AI and machine learning (ML) for building energy management and IEQ by exploring common use areas, the methods or algorithms applied and the results obtained. The overall purpose of this research is to add to the existing body of work and to highlight energy-related AI applications in buildings and the related gaps. The result shows five common application areas: thermal comfort and indoor air quality (IAQ) control; energy management and energy consumption prediction; indoor temperature prediction; anomaly detection; and HVAC controls. Gaps involving policy, real-life scenario applications, and insufficient study of the visual and acoustic comfort areas are also identified. Very few studies take into consideration the need to follow IEQ standards in the selection process and positioning of sensors in AI applications for IEQ in buildings. This study reveals a need for more systematically summarized research. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to uncover how the process of managing electricity in buildings based on the Internet of Things occurs. In particular, the work seeks to depict the factors affecting electricity consumption and management, as well as the application of the Internet of Things in energy management. A systematic literature review is used to examine the breadth of the electric energy management literature, encompassing bibliometric and thematic analysis based on an established procedure. The findings show the evolution of this field within key research networks with a few papers covering important elements of energy management, such as energy use, consumption and monitoring, assessment, and planning, in an integrative manner. Within this field, lacking in theory and practice, the originality of the work is the assembly of electric energy management into a conceptual framework based on real-time consumption and the Internet of Things (IoT). Indeed, the framework brings together the breadth of factors affecting consumption, energy use, and improvements that have been dispersed across the literature into one place. This framework, therefore, represents a stage towards an integrative view of IoT electric energy management and subsequent enhancement of theory and energy efficiency adoption. [ABSTRACT FROM AUTHOR]

Over the last few decades, energy efficiency has received increasing attention from the Architecture, Engineering, Construction and Operation (AECO) industry. Digital Twins have the potential to advance the Operation and Maintenance (O&M) phase in different application fields. With the increasing industry interest, there is a need to review the current status of research developments in Digital Twins for building energy efficiency. This paper aims to provide a comprehensive review of the applications of digital twins for building energy efficiency, analyze research trends and identify research gaps and potential future research directions. In this review, Sustainability and Energy and Buildings are among the most frequently cited sources of publications. Literature reviewed was classified into four different topics: topic 1. Optimization design; topic 2. Occupants' comfort; topic 3. Building operation and maintenance; and topic 4. Energy consumption simulation. [ABSTRACT FROM AUTHOR]

This paper explores the potential of rooftop solar PV to meet the electricity demand in the urban areas of Abha city, Saudi Arabia (KSA), minimising imports from the grid. A localised energy system for Abha is proposed that considers two types of loads: (i) residential loads with a monthly aggregated energy consumption of 172,440 MWh and an electric demand of 239.5 MW, and (ii) commercial loads with a monthly aggregated energy consumption of 179,280 MWh and an electric demand of 249 MW. The grid currently supplies this load. This paper proposes a PV development planning tool for residential and commercial areas to calculate the total PV production for each type of load to achieve a balanced energy area, considering (i) the number of buildings, (ii) the type of load, (iii) the peak load, and (iv) the total PV array area in m2 per building. The results of the modelling study using real data demonstrate that the anticipated total PV production in residential and commercial areas is sufficient to meet local peak demand, and there is an excess of power that can either be stored locally or exported to the grid. [ABSTRACT FROM AUTHOR]

Featured Application: Energy impacts and material resources used in the operation phase of a building should be considered to obtain more precise energy certifications than the current practical and regulatory schemes. Recent studies have identified that buildings all over the world are great contributors to energy consumption and greenhouse gas emissions. The relationship between the building industry and environmental pollution is continuously discussed. The building industry includes many phases: extraction of raw materials, manufacturing, construction, use, and demolition. Each phase consumes a large amount of energy, and subsequent emissions are released. The life cycle energy assessment (LCEA) is a simplified version of the life cycle assessment (LCA) that focuses only on the evaluation of energy inputs for different phases of the life cycle. Operational energy is the energy required for day-to-day operation processes of buildings, such as heating, cooling and ventilation systems, lighting, as well as appliances. This use phase accounts for the largest portion of energy consumption of the life cycle of conventional buildings. In addition, energy performance certification of buildings is an obligation under current European legislation, which promotes efficient energy use, so it is necessary to ensure that the energy performance of the building is upgraded to meet minimum requirements. For this purpose, this work proposes the consideration of the energy impacts and material resources used in the operation phase of a building to calculate the contribution of these energy impacts as new variables for the energy performance certification. The application of this new approach to the evaluation of university buildings has been selected as a case study. From a methodological point of view, the approach relied on the energy consumption records obtained from energy and materials audit exercises with the aid of LCA databases. Taking into practice the proposed methodology, the primary energy impact and the related emissions were assessed to simplify the decision-making process for the energy certification of buildings. From the results obtained, it was concluded that the consumption of water and other consumable items (paper) are important from energy and environmental perspectives. [ABSTRACT FROM AUTHOR]

The HVAC (Heating, Ventilation, and Air Conditioning) system is an important component of a building's energy consumption, and its primary function is to provide a comfortable thermal environment for occupants. Accurate prediction of occupant thermal comfort is essential for improving building energy utilization as well as health and work efficiency. Therefore, the development of accurate thermal comfort prediction models is of great value. Deep learning based on data-driven techniques has excellent potential for predicting thermal comfort due to the development of artificial intelligence. However, the inability to obtain large quantities of detailed thermal comfort labeling data from residents presents a substantial challenge to the modeling endeavor. This paper proposes a building-to-building transfer learning framework to make deep learning models applicable in data-limited interior building environments, thereby resolving the issue and enhancing model predictive performance. The transfer learning method (TL) is applied to a novel technology dubbed the Transformer model, which has demonstrated outstanding performance in data trend prediction. The model exploits the spatiotemporal relationship of data regarding thermal comfort. Experiments are conducted using the source dataset (Scales project dataset and ASHRAE RP-884 dataset) and the target dataset (Medium US office dataset), and the results show that the proposed TL-Transformer achieves 62.6% accuracy, 57% precision, and a 59% F1 score, and the prediction performance is better than other existing methods. The model is useful for predicting indoor thermal comfort in buildings with limited data, and its validity is verified by experimental results. [ABSTRACT FROM AUTHOR]

This research provides a comprehensive exploration of energy efficiency dynamics in non-residential public buildings such as schools, swimming pools, hospitals, and museums. Recognizing the distinct energy consumption patterns of each building type, the study accentuates the unique challenges they present, with a particular focus on the continuous and intensive energy demands of hospitals and the unparalleled energy needs of swimming pools. Through an extensive review of various case studies, the research unveils prevailing energy consumption trends, highlighting the role of metrics in assessing energy efficiency and the inherent challenges these metrics face in ensuring uniformity and direct comparability. A core element of this analysis emphasizes the dual nature of technical retrofitting, categorizing interventions into passive and active measures. The research delves into the sustainability imperatives of energy interventions, exploring the economic motivations underpinning retrofit decisions, and the intricate relationship between advanced technological solutions and the behavioral tendencies of building operators and users. Additionally, the study uncovers the influence of external determinants such as climatic factors and government policies in shaping energy consumption in public buildings. In synthesizing these findings, the paper offers insightful recommendations, emphasizing the need for an integrated approach that harmonizes technological innovations with informed operational habits, aiming to optimize energy efficiency in public non-residential buildings. [ABSTRACT FROM AUTHOR]

Environmental, economic, and social activities involve inherent spatial dimensions. The geospatial information system (GIS), a platform containing principles, methods, and tools to link, create, visualize, analyze, and model artificial activities and environment, provides the possibility to develop sustainability in the building sector. With globally political collaborations across governments, the demands to manage and visualize sustainable data (e.g., building energy and environment with geospatial reference) and implement more rigorous building modelling are increasing. A systematic mapping at multiple scales will help urban engineers, architectural engineers, policymakers, and energy planners identify emission hotspots, locate spatial resources, restructure district energy mix, and achieve net zero energy targets. To achieve net zero energy goals, it is crucial to minimize energy consumption, improve energy efficiency, and most importantly, apply renewable energy in buildings. However, these processes imply many aspects and challenges, regarding e.g., data availability, scalability, integrability, and a lack of clear and applicable frameworks. In this conceptional perspective paper, we aim to explore the potential of applying and installing renewable energy in net zero energy buildings using the GIS. More specifically, the described virtual framework will effectively support policy- and decision-makers in optimizing the energy structure, reducing building emissions, and applying renewable energy technologies. We also present challenges, limitations, and future directions for real practice. [ABSTRACT FROM AUTHOR]

Wood is one of the most abundant biomaterials on Earth, which has been used for centuries in construction applications including furniture, roofing, flooring, and cabinetry. However, wood chips—which are a low-quality and plentiful waste byproduct of lumber milling, woodworking, and shipping operations—have low economic value and complicated disposal methods. In this paper, we propose a strategy for wood chip reuse through the fabrication of bio-based building insulation foam. Through a high-temperature chemical treatment delignification process, we introduced additional small pores within the wood chips, effectively lowering their thermal conductivity, and used them in combination with a binding agent to produce a porous insulation foam. The porous insulation foam achieved a low thermal conductivity of 0.038 W/(m·K) and a high compressive strength of 1.1 MPa (70% strain). These characteristics demonstrate that wood waste can be repurposed into an effective building material, addressing challenges in both waste management and sustainable construction. [ABSTRACT FROM AUTHOR]

The Architecture, Engineering and Construction (AEC) industry has been utilizing Decision Support Systems (DSSs) for a long time to support energy efficiency improvements in the different phases of a building's life cycle. In this context, there has been a need for a proper means of exchanging and managing of different kinds of data (e.g., geospatial data, sensor data, 2D/3D models data, material data, schedules, regulatory, financial data) by different kinds of stakeholders and end users, i.e., planners, architects, engineers, property owners and managers. DSSs are used to support various processes inherent in the various building life cycle phases including planning, design, construction, operation and maintenance, retrofitting and demolishing. Such tools are in some cases based on established technologies such Building Information Models, Big Data analysis and other more advanced approaches, including Internet of Things applications and semantic web technologies. In this framework, semantic web technologies form the basis of a new technological paradigm, known as the knowledge graphs (KG), which is a powerful technique concerning the structured semantic representation of the elements of a building and their relationships, offering significant benefits for data exploitation in creating new knowledge. In this paper, a review of the main ontologies and applications that support the development of DSSs and decision making in the different phases of a building's life cycle is conducted. Our aim is to present a thorough analysis of the state of the art and advancements in the field, to explore key constituents and methodologies, to highlight critical aspects and characteristics, to elaborate on critical thinking and considerations, and to evaluate potential impact of KG applications towards the decision-making processes associated with the energy transition in the built environment. [ABSTRACT FROM AUTHOR]

The rational use of energy systems is one of the main discussions in sustainability in the 21st century. Water pumping systems are one of the most significant consumers of electricity in urban systems, whether for urban water supply, sewage, or use in vertical buildings. Thus, this work aims to present Industry 4.0 (I4.0) technologies applied in buildings' water pumping systems, focusing on energy efficiency, supervision, and control of the pumping system. The work involves four steps: (i) identifying the existing I4.0 technologies and (ii) mapping the possibilities of applying Industry 4.0 technologies in building pumping systems. The study includes the analysis of (16) articles published in journals between 2018 and June 2021 to identify I4.0 technologies cited in the publications. It identified and grouped eighteen (18) technologies based on twenty-two (22) terms observed in the papers. The study classified the identified technologies into three possible applications in a building water pumping system. The applications include: (i) directly applicable, (ii) partially applicable, and (iii) application not yet identified. Therefore, the study presents the advantages of I4.0 technologies developed primarily for the industry sector, also applicable in residential building water pumping systems. These technologies' benefits include energy efficiency, user control, a reduction from periods of failure of the pumping system (maintenance), water quality, and moving towards Intelligent Pumping or Pumping 4.0. [ABSTRACT FROM AUTHOR]

Building characteristics, household compositions, lifestyles, and home equipment are recognized as the main factors influencing residential energy consumption, which has been a subject of extensive exploration for many years now. However, the quantitative correlation models between the above factors and residential end-use energy have not been fully studied. This paper aims to explore the determinants of residential end-use energy consumption by a comprehensive analysis based on the factors of building characteristics, household compositions, lifestyles, and home equipment. For this purpose, we investigated and collected the building information of 66 households and obtained the data through an installed measurement system of the annual residential end-use energy from July 2019 to June 2020. Subsequently, six multiple regression models were used to quantitatively analyze the valid determinants of each end-use energy. The main results were as follows: for cooling energy consumption, the greatest effective variable was FM_no (22–59, number of family members aged 22 to 59); the most influential variable was found to be FM_no (number of family members) for DHW and appliances energy consumption; for lighting and cooking energy consumption, the most effective variables were AREA (floor area) and Cooking (average daily cooking hours), respectively. Moreover, the order of influence of building characteristics, household compositions, lifestyles, and home equipment over each end-use energy consumption is as follows: households > equipment > lifestyles for cooling and DHW, households > buildings > equipment for lighting, equipment > lifestyles for appliances and cooking. [ABSTRACT FROM AUTHOR]

Different policies with the goal of reducing energy consumption and other environmental impacts in the building sector coexist in Europe. Sustainable product polices, such as the Ecodesign and Energy Labelling Directives, have recently broadened the scope of their target product groups from a strict product approach to extended product and system approaches. Indeed, there is a potential for greater savings when the focus is at a system level rather than on regulating individual products. Product policies for space and water heating systems have recently introduced and implemented the package label, which is a modular approach, standing between the extended product and the system approaches. This paper presents a systematic analysis of the different system approaches of various policies from an engineering perspective. It analyses in detail the package concept and its features through a practical application using a real case study. It focuses on how the package concept can support decisions made in the building design phase and, in particular, how can support the choice of appropriate components based on estimating system performances. This brings building engineers and regulators closer regarding the use of more consistent data on energy performance. Finally, this paper highlights the need to improve the alignment of the building-related product policies with the Energy Performance of Buildings Directive. [ABSTRACT FROM AUTHOR]

Among all the activities in a society, construction has a key role in environmental, social, and economic pillars. Construction is also responsible for a considerable amount of waste production, energy consumption, pollutant gas emissions, and consumption of nonrenewable natural resources. Regarding energy consumption, a high demand for building operational energy has been observed in the last decades due to the more demanding requirements of the users with a continuous search for better thermal comfort in their homes, namely in developed countries. In Portugal, for instance, more than 20% of the electricity consumed is related to residential buildings, which is based on CO2 emissions and other pollutants that negatively affect the environment. Much of this consumed energy is a result of the HVAC systems installed inside buildings to provide users with thermal comfort. One exciting opportunity to mitigate buildings' operational energy consumption while contributing to thermal user comfort is the use of passive solutions. Even though several passive options are available and constantly under research, their use is still considered limited. This paper overviews and highlights the potential of energy-efficiency passive strategies, namely for Mediterranean-climate countries, where passive solar technologies can be set as a viable solution, as this climate is mainly known for its solar availability (solar hours and solar irradiance). A comprehensive overview of innovative and traditional housing passive solutions currently available is presented and discusses the main advantages, disadvantages, and concerns contributing to the optimal use of climatic conditions and natural resources in those regions. [ABSTRACT FROM AUTHOR]

The question of how best to tackle the problem of energy inefficient older housing in the UK is considerable, and is further complicated by the question of tenure. Social landlords are working to update and improve their properties, which make up around 15% of the total UK housing stock (4 million properties). The success of such efficiency improvements depends in part on the cooperation of tenants, and their ability and willingness to engage with the process of change. This paper describes the experiences of eleven social housing tenant households whose properties were fitted with External Wall Insulation (EWI), based on pre- and post-installation interviews and data collection. It includes discussion of tenants' knowledge, attitudes, and expectations prior to and following installation; household thermal comfort and energy spending before installation; tenant experiences of having EWI installed; tenant perceptions regarding the effects of EWI on thermal comfort, energy spending, and housing attractiveness; impacts of EWI on internal temperatures and heat loss (measured via thermal imaging); energy bill comparisons. Households experienced an average saving of 33% on energy bills following EWI installation, and the majority of tenants reported benefits including improved thermal comfort and related positive impacts on health and wellbeing. The paper concludes by highlighting potential learning points for engaging tenants in the process of enhancing energy efficiency in UK social rented housing. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) imaging and infrared (IR) thermography are powerful tools in many areas in engineering and sciences. Their joint use is of great interest in the buildings sector, allowing inspection and non-destructive testing of elements as well as an evaluation of the energy effeciency. When dealing with large and complex structures, as buildings (particularly historical) generally are, 3D thermography inspection is enhanced by Unmanned Aerial Vehicles (UAV-also known as drones). The aim of this paper is to propose a simple and cost-effective system for aerial 3D thermography of buildings. Special attention is thus payed to instrument and reconstruction software choice. After a very brief introduction to IR thermography for buildings and 3D thermography, the system is described. Some experimental results are given to validate the proposal. [ABSTRACT FROM AUTHOR]

Nowadays, the importance of implementing energy efficiency (EE) measures is growing significantly worldwide, based on its potential to reduce energy demands and mitigate climate change effects. Paraguay is a developing country with the highest per capita hydroelectric energy generation in the world, but only 18% of local consumption is hydroelectric and 41% of its energy matrix corresponds to oil products. This paper aims to analyse the importance the Country places on EE as a strategy towards sustainable development and to highlight as EE is an effective pathway to mitigate the climate changes and contrast their effects. The authors initially provide an insight into the climate scenarios for Paraguay and underline the effects of the climate changes on the buildings' comfort. Subsequently, the authors provide, by resourcing a bibliographic review, a description of the Paraguayan sectors of greater energy consumption, its policies and targets set for increasing EE. Besides, the main EE projects developed by other neighbouring South American countries are analysed to show the level of development of each one in the scope of EE and to offer a reference basis of potential virtuous solutions to be adopted in Paraguay. A focus on the building sector is also made to provide a foundation for policy analyses to enhance EE in this sector. As a result of this review, evidence that EE is beginning to take part in Paraguay's public policies was found, with the leaders becoming aware of its importance. Nevertheless, many concrete results could not be achieved as of yet and overcoming these barriers still involve a great challenge. Regarding the building sector, few advances have been noticed regarding the regulations of buildings' thermal performance, a reason for which the National objectives set need to be more specific to achieve greater collective awareness to enforce them. Finally, key actions are recommended for Paraguay aiming to improve EE levels to face the climate change phenomenon. [ABSTRACT FROM AUTHOR]

The recast of the energy performance of buildings directive (EPBD) describes a comparative methodological framework to promote energy efficiency and establish minimum energy performance requirements in buildings at the lowest costs. The aim of the cost-optimal methodology is to foster the achievement of nearly zero energy buildings (nZEBs), the new target for all new buildings by 2020, characterized by a high performance with a low energy requirement almost covered by renewable sources. The paper presents the results of the application of the cost-optimal methodology in two existing buildings located in the Mediterranean area. These buildings are a kindergarten and a nursery school that differ in construction period, materials and systems. Several combinations of measures have been applied to derive cost-effective efficient solutions for retrofitting. The cost-optimal level has been identified for each building and the best performing solutions have been selected considering both a financial and a macroeconomic analysis. The results illustrate the suitability of the methodology to assess cost-optimality and energy efficiency in school building refurbishment. The research shows the variants providing the most cost-effective balance between costs and energy saving. The cost-optimal solution reduces primary energy consumption by 85% and gas emissions by 82%-83% in each reference building. [ABSTRACT FROM AUTHOR]

During the past century, energy consumption and associated greenhouse gas emissions have increased drastically due to a wide variety of factors including both technological and population-based. Therefore, increasing our energy efficiency is of great importance in order to achieve overall sustainability. Forecasting the building energy consumption is important for a wide variety of applications including planning, management, optimization, and conservation. Data-driven models for energy forecasting have grown significantly within the past few decades due to their increased performance, robustness and ease of deployment. Amongst the many different types of models, artificial neural networks rank among the most popular data-driven approaches applied to date. This paper offers a review of the studies published since the year 2000 which have applied artificial neural networks for forecasting building energy use and demand, with a particular focus on reviewing the applications, data, forecasting models, and performance metrics used in model evaluations. Based on this review, existing research gaps are identified and presented. Finally, future research directions in the area of artificial neural networks for building energy forecasting are highlighted. [ABSTRACT FROM AUTHOR]

This paper examines the energy consumption of varying aspect ratio in multi-unit residential buildings in Canadian cities. The aspect ratio of a building is one of the most important determinants of energy efficiency. It defines the building surface area by which heat is transferred between the interior and exterior environment. It also defines the amount of building area that is subject to solar gain. The extent to which this can be beneficial or detrimental depends on the aspect ratio and climate. This paper evaluates the relationship between the geometry of buildings and location to identify a design vernacular for energy-efficient designs across Canada. [ABSTRACT FROM AUTHOR]

In areas with a deep floor plan, the distribution of natural light is not uniform. Consequently, relying solely on daylight may not suffice to meet the space's lighting requirements, necessitating the use of artificial lighting in darker areas. Therefore, a lighting system is needed that not only controls the glare near the windows but also increases the light at the end of the room and provides uniform daylight. One of the widely used systems is the "light shelf", which has three main functions: shading, increasing the depth of light penetration, and reducing glare. Review articles about light shelves were published in 2015 and 2017, while more than 80% of the studies have been carried out since 2016, and light shelves with more diverse forms and dynamic elements and many consolidations have been proposed. Therefore, there is a need for a more comprehensive review. The main question of this research is how different parameters (including climate, material, ceiling, and integrated systems) can help to increase the efficiency of light shelves. By using a systematic review, studies in the past three decades were classified in order to determine the effect of these parameters on improving lighting performance and controlling solar heat gain. [ABSTRACT FROM AUTHOR]

Thermal comfort is a complex issue in the built environment due to the physiological and psychological differences of each individual in a building. There is a growing worry over the environmental implications of energy use as a result of the warming of the global climate and the growth in the number of instances of extreme weather events. Many review articles have been written, but these reviews have focused on a specific aspect of occupant behavior and thermal comfort. To research the trends of thermal comfort and energy, this research adopted mixed reviews, i.e., quantitative and qualitative, to understand the state-of-the-art factors affecting the thermal comfort of occupants concerning energy, different occupant modeling approaches, functions, and limitations. The in-depth qualitative discussion provides deeper insights into the impacts of occupant behaviors, factors affecting thermal comfort, and occupant behavior modeling approaches. This study classified occupant behaviors into five categories: occupant characteristics, perceptions of the occupant, realistic behaviors, heat gain, and occupant interactions with the system. It also went further to classify the factors affecting the thermal comfort of users based on past works of literature. These include structural, environmental, and human factors. It was concluded that factors that have the most significant impact on energy are human, structural, and environmental factors, respectively. In addition, most of the occupant behavior modeling approaches that have been used in past studies have pros and cons and cannot accurately predict human behaviors because they are stochastic. Future research should be conducted on thermal comfort for different building functions by examining the varied activity intensity levels of users, especially in educational or commercial buildings. Additionally, a proper investigation should be carried out on how thermal insulation of structural members influences thermal comfort. These should be compared in two similar buildings to understand occupant behavioral actions and energy consumption. [ABSTRACT FROM AUTHOR]

Energy efficiency is a principle of architectural design that reduces environmental impact. Generative design can offer alternative options to improve energy efficiency in buildings, but significant gaps exist in the application due to accessing complex knowledge. This study aimed to explore publications on generative design and energy efficiency in buildings and identify generative methods for energy efficiency topics. This study conducted a systematic review using the PRISMA methodology in December 2023 by searching publications from databases including Scopus, Google Scholar, and Thai Journals Online. Descriptive analysis examined 34 articles, showing the publication year, source, and citations. Comparative qualitative and descriptive analysis identified generative methods. Publications are increasing over time, and further growth is expected related to the accessibility of computational design and practical applications. Tools and frameworks demonstrated reduced energy usage compared to prototypes or traditional design approaches. The most studied is thermal performance, which was reduced by 28%. Energy performance achieved up to a 23.30% reduction, followed by others and daylighting. In addition to single-topic studies, there are also studies with multiple topics. Evolutionary algorithms are standard. Parametric search strategies have increased. Exploration reveals rule-based and mixed methods. Machine learning and AI garner attention. [ABSTRACT FROM AUTHOR]

It is crucial to consider structural design issues in Trombe wall (T-wall) buildings to promote more suitable indoor climates and thermal comfort standards. Therefore, the present study examined the impact of two different T-wall designs and six different roof types on the energy and operational efficiency of a building located in a low-temperature and high-humidity winter climate. Ansys-CFX 15.0 software was employed to simulate the thermal and fluid dynamics behavior of the T-wall system, and flow, thermal comfort, energy, and exergy analyses were conducted. Three-dimensional simulation results and the pertinent literature data showed a good level of agreement, and the accuracy of the model was ensured. Outcomes revealed an average air velocity variation of 0.186 m/s and maximum average indoor air temperature variation of 3.3 °C between the six roof geometries. The highest air speed (0.988 m/s) was recorded for the gambrel roof while the lowest one (0.802 m/s) was recorded for the typical flat roof. The shed roof right with a rounded T-wall was more comfortable for standing and sitting activity than the others for the two T-wall shapes, and, at Y = 0.6 m and Y = 1.1 m, the average predicted percentages of dissatisfied (PPD) values were 31 and 28%, respectively. Furthermore, it was determined in the study that solar radiation intensity and T-wall and roof geometries had a significant effect on energy and exergy efficiency, and high energy and exergy efficiencies were achieved at higher solar intensity values. The best energy and exergy efficiencies were obtained for the butterfly and shed roof configurations. This study can serve as a reference for the thermal environment design of buildings with T-walls. [ABSTRACT FROM AUTHOR]

Increased use of energy in buildings and HVAC systems requires advanced control schemes like model-based control to improve energy efficiency, which in turn requires accurate thermodynamic models of buildings. The Resistive-Capacitive (RC) method is a popular and versatile approach for thermal modeling of buildings. Despite this, it is not easy to find practical solutions of implementation of the RC method. It is the goal of this paper to clarify the RC method and demonstrate simple implementation of this method, especially for multi-zone buildings, which have more potential for energy savings from use of model-based control. This paper provides two contributions. First is a detailed explanation of the RC method, focusing on its use for developing a structure of a model and first-principles approach for estimation of parameters of a model. Second is a demonstration of an algorithm that enables automatic development of the structure of a model from basic information about a building (layout, construction elements) and its combination with data-based parameter estimation. Use of the algorithm is presented with a case-study on industrial multi-zone building, for which such a grey-box model is developed and analyzed. The resulting model is rapidly developed and used in a simulation with the measured data. The outputs of the model are compared with the measured temperatures and they show good fit. [ABSTRACT FROM AUTHOR]

Advances in metering technologies and emerging energy forecast strategies provide opportunities and challenges for predicting both short and long-term building energy usage. Machine learning is an important energy prediction technique, and is significantly gaining research attention. The use of different machine learning techniques based on a rolling-horizon framework can help to reduce the prediction error over time. Due to the significant increases in error beyond short-term energy forecasts, most reported energy forecasts based on statistical and machine learning techniques are within the range of one week. The aim of this study was to investigate how facility managers can improve the accuracy of their building's long-term energy forecasts. This paper presents an extensive study of machine learning and data processing techniques and how they can more accurately predict within different forecast ranges. The Clarendon building of Teesside University was selected as a case study to demonstrate the prediction of overall energy usage with different machine learning techniques such as polynomial regression (PR), support vector regression (SVR) and artificial neural networks (ANNs). This study further examined how preprocessing training data for prediction models can impact the overall accuracy, such as via segmenting the training data by building modes (active and dormant), or by days of the week (weekdays and weekends). The results presented in this paper illustrate a significant reduction in the mean absolute percentage error (MAPE) for segmented building (weekday and weekend) energy usage prediction when compared to unsegmented monthly predictions. A reduction in MAPE of 5.27%, 11.45%, and 12.03% was achieved with PR, SVR and ANN, respectively. [ABSTRACT FROM AUTHOR]

The challenge of maintaining optimal comfort in residents while minimizing energy consumption has long been a focal point for researchers and practitioners. As technology advances, reinforcement learning (RL)—a branch of machine learning where algorithms learn by interacting with the environment—has emerged as a prominent solution to this challenge. However, the modern literature exhibits a plethora of RL methodologies, rendering the selection of the most suitable one a significant challenge. This work focuses on evaluating various RL methodologies for saving energy while maintaining adequate comfort levels in a residential setting. Five prominent RL algorithms—Proximal Policy Optimization (PPO), Deep Deterministic Policy Gradient (DDPG), Deep Q-Network (DQN), Advantage Actor-Critic (A2C), and Soft Actor-Critic (SAC)—are being thoroughly compared towards a baseline conventional control approach, exhibiting their potential to improve energy use while ensuring a comfortable living environment. The integrated comparison between the different RL methodologies emphasizes the subtle strengths and weaknesses of each algorithm, indicating that the best selection relies heavily on particular energy and comfort objectives. [ABSTRACT FROM AUTHOR]

The buildings sector is the single most important end-user of final energy in the European Union and a significant contributor to carbon and greenhouse gas emissions. This work focuses on a review of available data that are used to calculate the annual emissions from electricity generation in the European Union and quantify indirect emissions from the use of electricity in the buildings sector. Historical data since 1990 are used to derive simple empirical correlations for the time evolution of emissions factors related to electricity production in each Member State of the European Union. More recent trajectories using data from the last ten years are also presented. The derived correlations can be easily integrated in building stock modeling and national studies to facilitate forward-looking projections of emissions from electricity use in buildings. The EU-27 averages 0.2883 kgCO2-eq/kWhel, ranging from 0.0456 kgCO2-eq/kWhel in Sweden up to 1.0595 kgCO2-eq/kWhel in Poland. As a case study, the derived coefficients are then used to quantify the indirect emissions from the electricity consumption attributed to the building stock in each EU Member State. The calculated total EU-27 GHG indirect emissions attributed to electricity consumption amounted to 215 MtCO2-eq for residential buildings and 201 MtCO2-eq for non-residential buildings. In addition, the proposed correlations are used to demonstrate how they can be used for more realistic future projections of emissions towards the national targets in Greece and Poland. [ABSTRACT FROM AUTHOR]

Building energy efficiency is strongly linked to the operations and control systems, together with the integrated performance of passive and active systems. In new high quality buildings in particular, where these two latter aspects have been already implemented at the design stage, users' perspective, obtained through post-occupancy assessment, has to be considered to reduce whole energy requirement during service life. This research presents an innovative and low-cost methodology to reduce buildings' energy requirements through post-occupancy assessment and optimization of energy operations using effective users' attitudes and requirements as feedback. As a meaningful example, the proposed method is applied to a multipurpose building located in New York City, NY, USA, where real occupancy conditions are assessed. The effectiveness of the method is tested through dynamic simulations using a numerical model of the case study, calibrated through real monitoring data collected on the building. Results show that, for the chosen case study, the method provides optimized building energy operations which allow a reduction of primary energy requirements for HVAC, lighting, room-electricity, and auxiliary supply by about 21%. This paper shows that the proposed strategy represents an effective way to reduce buildings' energy waste, in particular in those complex and high-efficiency buildings that are not performing as well as expected during the concept-design-commissioning stage, in particular due to the lack of feedback after the building handover. [ABSTRACT FROM AUTHOR]

The healthcare industry significantly impacts the environment due to its high usage of energy and natural resources and the associated waste generation. This study applied a cradle-to-grave Life Cycle Sustainability Assessment (LCSA) approach to assess the environmental and social life cycles of public hospitals. One hundred twenty-four public hospitals were selected for the current study; their sustainability performance was compared with those certified by Leadership in Energy and Environmental Design (LEED). The comparison revealed several factors contributing to the poor sustainability performance of public hospitals. These include inadequate management, substandard planning, political interference, insufficient staffing and funding, high energy consumption, high expenses, inconsistent healthcare policies, and conventional building designs. System thinking was leveraged, and a causal loop diagram (CLD) was developed to visualize the interdependency of the identified indicators of LCSA. Based on the findings of the study, a policy framework is proposed to guide the development of sustainable healthcare buildings. The framework includes using eco-friendly materials and techniques in construction, harnessing solar energy, improving hospital management practices, promoting public awareness about sustainability, conserving energy and water, and adopting sustainable waste management and transportation. Additionally, it emphasizes addressing social issues such as improving indoor air quality, thermal comfort, lighting, acoustics, patient safety, and security and ensuring that healthcare services are accessible and affordable. This study contributes to the literature on sustainable healthcare buildings by providing a practical policy framework for achieving sustainability in the healthcare sector. [ABSTRACT FROM AUTHOR]

More than half of the world's population lives in cities. A large part of the emissions and energy consumption corresponds to buildings, both in the residential sector and in the service sector. This means that a large part of the measures taken by governments to reduce energy consumption and greenhouse gas emissions are focused on this sector. With this background, this paper studies energy consumption in city buildings and the CO2 emissions they produce. It only makes use of publicly available data. The analysis is made from the point of view of income per inhabitant, and the results are obtained per inhabitant and household. To facilitate the analysis of the results, an index has been defined. The main contributions of this work are to analyze energy consumption and emissions due to buildings, study them from the point of view of the income of their inhabitants, and consider cities individually. The proposed methodology has been applied to the case of Spain. A total of 145 Spanish cities that have more than 50,000 inhabitants have been studied. The results show that the higher the income, the higher the consumption and emissions. Electricity consumptions are almost inelastic, while those of thermal origin are greatly influenced by the level of income. Regarding CO2 emissions, the percentage of emissions of electrical origin with respect to total emissions is higher than that of thermal origin. In addition, the lower the income, the higher the percentage of emissions of electrical origin. [ABSTRACT FROM AUTHOR]

More than 50% of the world's population lives in cities. Its buildings consume more than a third of the energy and generate 40% of the emissions. This makes cities in general and their buildings in particular priority points of attention for policymakers and utilities. This paper uses population density as a variable to know its influence on energy consumption and emissions produced in buildings. Furthermore, to show its effect more clearly, the influence of the climate was eliminated. The usual energy consumption in buildings is thermal and electrical. The study was carried out at the city level, both per inhabitant and per household. The area actually occupied by the city was considered. The proposed method was applied to the case of Spanish cities with more than 50,000 inhabitants. The results show that the higher the population density, the higher the energy consumption per inhabitant and household in buildings. The consumption of thermal energy is elastic, while that of electrical energy is inelastic, varying more than 100% between extreme groups. Regarding CO2 emissions, the higher the population density, the higher the emissions. Emissions of electrical origin barely vary by 2% and are greater than those of thermal origin. In addition, the proportion of emissions of electrical origin, with respect to the total, decreases with increasing population density from 74% to 55%. This research aims to help policymakers and utilities to take the appropriate measures that favor the use of renewable energies and reduce CO2 emissions. [ABSTRACT FROM AUTHOR]

Interpretable and scalable data-driven methodologies providing high granularity baseline predictions of energy use in buildings are essential for the accurate measurement and verification of energy renovation projects and have the potential of unlocking considerable investments in energy efficiency worldwide. Bayesian methodologies have been demonstrated to hold great potential for energy baseline modelling, by providing richer and more valuable information using intuitive mathematics. This paper proposes a Bayesian linear regression methodology for hourly baseline energy consumption predictions in commercial buildings. The methodology also enables a detailed characterization of the analyzed buildings through the detection of typical electricity usage profiles and the estimation of the weather dependence. The effects of different Bayesian model specifications were tested, including the use of different prior distributions, predictor variables, posterior estimation techniques, and the implementation of multilevel regression. The approach was tested on an open dataset containing two years of electricity meter readings at an hourly frequency for 1578 non-residential buildings. The best performing model specifications were identified, among the ones tested. The results show that the methodology developed is able to provide accurate high granularity baseline predictions, while also being intuitive and explainable. The building consumption characterization provides actionable information that can be used by energy managers to improve the performance of the analyzed facilities. [ABSTRACT FROM AUTHOR]

The built environment is the global sector with the greatest energy use and greenhouse gas emissions. As a result, building energy savings can make a major contribution to tackling the current energy and climate change crises. Fluid dynamics models have long supported the understanding and optimization of building energy systems and have been responsible for many important technological breakthroughs. As Covid-19 is continuing to spread around the world, fluid dynamics models are proving to be more essential than ever for exploring airborne transmission of the coronavirus indoors in order to develop energy-efficient and healthy ventilation actions against Covid-19 risks. The purpose of this paper is to review the most important and influential fluid dynamics models that have contributed to improving building energy efficiency. A detailed, yet understandable description of each model's background, physical setup, and equations is provided. The main ingredients, theoretical interpretations, assumptions, application ranges, and robustness of the models are discussed. Models are reviewed with comprehensive, although not exhaustive, publications in the literature. The review concludes by outlining open questions and future perspectives of simulation models in building energy research. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Robust and accurate building energy load forecasting is important for helping building managers and utilities to plan, budget, and strategize energy resources in advance. With recent prevalent adoption of smart-meters in buildings, a significant amount of building energy consumption data became available. Many studies have developed physics-based white box models and data-driven black box models to predict building energy consumption; however, they require extensive prior knowledge about building system, need a large set of training data, or lack robustness to different forecasting scenarios. In this paper, we introduce a new building energy forecasting method based on Gaussian Process Regression (GPR) that incorporates physical insights about load data characteristics to improve accuracy while reducing training requirements. The GPR is a non-parametric regression method that models the data as a joint Gaussian distribution with mean and covariance functions and forecast using the Bayesian updating. We model the covariance function of the GPR to reflect the data patterns in different forecasting horizon scenarios, as prior knowledge. Our method takes advantage of the modeling flexibility and computational efficiency of the GPR while benefiting from the physical insights to further improve the training efficiency and accuracy. We evaluate our method with three field datasets from two university campuses (Carnegie Mellon University and Stanford University) for both short- and long-term load forecasting. The results show that our method performs more accurately, especially when the training dataset is small, compared to other state-of-the-art forecasting models (up to 2.95 times smaller prediction error). [ABSTRACT FROM AUTHOR]

This paper presents a comparison of different scenarios in controlling the space heating systems in residential buildings. The space heating energy consumption of a three-storey residential building is estimated using traditional control methods (fixed-temperature schedule and fixed-time schedule) and a mathematical model-based control strategy. The model-based control technique takes the usage pattern of the building into account and operates the heaters based on the calculated heating time of the building. The results from the experiments confirm that the use of a model in heating control is the best option, which can save up to 1400 kWh and 320 kWh per year compared to a fixed-temperature schedule and fixed-time schedule, respectively. [ABSTRACT FROM AUTHOR]

This article presents causal recipes leading to high and low energy consumption efficiency performances using fuzzy set Qualitative Comparative Analysis (fsQCA). The study found that several causal conditions are sufficient for high energy efficiency performance, including several fiscal and financial incentive policies, a highly educated population, many completed dwellings, and low GDP. The study also found that high inequality in completed dwellings and completed reconstructions, coupled with a low GDP and a low number of policies, lead to high energy consumption efficiency performance. In addition, the analysis showed slight differences between the yearly consistencies, suggesting that time effects are not a concern. On the other hand, a low education level, Gini coefficient, few completed dwellings and reconstructions, coupled with a low number of fiscal and financial policies, are the causal conditions leading to low energy consumption efficiency performance. The study's results suggest that policymakers and stakeholders should consider a combination of several causal conditions when implementing energy efficiency policies. The study also highlights the need for policies focusing on education, fiscal and financial incentives, completed dwellings, and reconstructions to achieve high energy efficiency performance. [ABSTRACT FROM AUTHOR]

This paper provides a review of research that addresses the relationship between indoor temperatures and health outcomes, taking into consideration studies that focus heat or cold exposure within the household context. It aims to extend previous research by considering both indoor temperatures from existing housing, and empirical studies that focus on energy efficiency measures and subsequent health impacts. To achieve this aim, a literature review was undertaken, combining engineering and health databases. The review established that, overall, inadequate indoor temperatures are associated with poor health status, whereas energy efficiency measures have been associated to improved indoor temperatures and occupant's health namely regarding cardiovascular, respiratory and mental health disorders. These health conditions are among the most prevalent non-communicable diseases (NCD). The review also highlighted the need for more empirical studies with an extended timeframe to deal with climate change challenges. It underlined the potential advantages of the convergence between health and energy efficiency studies, for better modelling and planning. [ABSTRACT FROM AUTHOR]

Rooftop gardens ona building have proved to be a good way to improve its storm water management, but many other benefits can be obtained from the installation of these systems, such as reduction of energy consumption, decrease of the heat stress, abatement on CO2 emissions, etc. In this paper, the effect from the presence of these rooftop gardens on abuilding's energy consumption has been investigated by experimental campaigns using a green roof ona public building in a Mediterranean location in Spain. The obtained results demonstrate a substantial improvement by the installation of the green roof onthe building's cooling energy demand for a standard summer day, in the order of 30%, and a reduction, about 15%, in the heating energy demand for a winter day. Thus, given the longer duration of the summer conditions along the year, a noticeable reduction on energy demand could be obtained. Simulation analysis, using commercial software TRNSYS code, previously calibrated using experimental data for typical summer and winter days, allows for the extrapolation to the entire year of these results deducing noticeable improvement in energy efficiency, in the order of 19%, but with an increase of 6% in the peak power during the winter period. [ABSTRACT FROM AUTHOR]

Buildings consume a significant amount of energy, estimated at about one-third of total primary energy resources. Building energy efficiency has turned out to be a major issue in limiting the increasing energy demands of the sector. Literature shows that building user behavior can increase the efficiency of the energy used in the building and different strategies have been tested to address and support this issue. These strategies often combine the quantification of energy savings and qualitative interpretation of occupant behavior in order to foster energy efficiency. Strategies that influence building occupant behaviors include eco-feedback, social interaction, and gamification. This review paper presents a study conducted on the state of the art related to the impact of building user behavior on energy efficiency, in order to provide the research community with a better understanding and up-to-date knowledge of energy, comfort-related practices, and potential research opportunities. Achieving and maintaining energy-efficient behavior without decreasing the comfort of building occupants still represents a challenge, despite emerging technologies and strategies as well as general research progress made over the last decade. Conclusions highlight eco-feedback as an effective way to influence behavior, and gamification as a new opportunity to trigger behavioral change. The impact of user behavior is difficult to quantify for methodological reasons. Factors influencing human behavior are numerous and varied. Multi-disciplinary approaches are needed to provide new insights into the inner dynamic nature of occupant’s energy behavior. [ABSTRACT FROM AUTHOR]

This paper develops an energy analysis for an existing near Zero Energy (nZEB) and Zero Carbon Emissions building called LUCIA, located at the university campus in Valladolid (Spain). It is designed to supply electricity, cooling and heating needs through solar energy (Photovoltaic Systems, PV), biomass and an Earth–Air Heat Exchanger (EAHE), besides a Combined Heat Power (CHP). It is currently among the top three buildings with the highest LEED certification in the World. The building model is simulated with DesignBuilder version 5. The results of the energy analysis illustrate the heating, cooling and lighting consumptions expected, besides other demands and energy uses. From this data, we carried out an energy balance of the nZEB, which will help to plan preventive actions when compared to the actual energy consumptions, improving the management and control of both the building and its systems. The primary energy indicator obtained is 67 kWh/m2 a year, and 121 kWh/m2 a year for renewable energy generation, with respect to 55 kWh/m2 and 45 kWh/m2 set as reference in Europe. The Renewable Energy Ratio (RER) is 0.66. These indicators become a useful tool for the energy analysis of the nZEB according to the requirements in the European regulations and for its comparison with further nZEB. [ABSTRACT FROM AUTHOR]

The performance gap between the expected and actual energy performance of buildings and elements has stimulated interest in in-situ measurements. Most research has employed quasi-static analysis methods that estimate heat loss metrics such as U-values, without taking advantage of the rich time series data that is often recorded. This paper presents a dynamic Bayesian-based method to estimate the thermophysical properties of building elements from in-situ measurements. The analysis includes Markov chain Monte Carlo (MCMC) estimation, priors, uncertainty analysis, and model comparison to select the most appropriate model. Data from two case study dwellings is used to illustrate model performance; U-value estimates from the dynamic and static methods are within error estimates, with the dynamic model generally requiring much shorter time series than the static model. The dynamic model produced robust results at all times of year, including when the average indoor-to-outdoor temperature difference was low, when external temperatures had large daily variation, and measurements were subjected to direct solar radiation. Further, the probability distributions of parameters may provide insights into the thermal performance of elements. Dynamic methods such as that presented herein may enable wider characterisation of the performance of building elements as built, supporting work to reduce the performance gap. [ABSTRACT FROM AUTHOR]

Rapid population growth and urbanization contribute to an ever-increasing global energy demand, of which the building sector accounts for one-third. The increasing average height and density of buildings escalate the need for vertical transportation, expanding elevator usage and energy needs. This phenomenon accounts for a significant amount of the total building energy use, necessitating a study of elevator system energy consumption. This study aimed to analyze the energy consumption and carbon emissions of elevator systems in low- and high-rise buildings towards energy-efficient estimations. A comprehensive analysis was performed based on a hybrid approach of measurement and calculation using a formula and reference values derived from previous studies. Four buildings were selected and thoroughly studied, representing the low- and high-rise categories. Data were collected based on on-site sampling and observation, as well as information from the building management offices. The mechanical parameters of the elevator system in each building and operational factors in terms of speed, number of trips, load, travel distance, and time were studied. In this analysis, the energy consumption calculation was performed according to International Standard ISO 25745. Annual carbon emissions were calculated in accordance with the USA EPA and IPCC guidelines. The elevator energy efficiency class was determined based on daily energy consumption. It was found from this study that the annual energy consumption of an elevator system is positively correlated to an elevator's daily energy consumption. The annual carbon emissions of the elevator systems are dependent on increasing annual energy consumption, which is also connected to building height indirectly. The low-rise buildings showed better energy efficiency compared to the high-rise buildings due to lower travel distance, less trips, and fewer floors. The annual number of trips, travel distances, and energy consumption had an effect on the energy efficiency of the elevator systems in this study. [ABSTRACT FROM AUTHOR]

With regard to climate change, it is imperative to reduce greenhouse gas emissions. One solution approach is to increase energy efficiency in buildings. Buildings contribute a high share of the total global energy usage. As the rate of new building constructions is low, measures applicable to existing buildings become paramount. Before applying new approaches on a large scale, it is necessary to evaluate them in a representative, realistic environment. Living labs such as the Living Lab Energy Campus (LLEC) at Forschungszentrum Jülich (FZJ) facilitate innovative monitoring and control approaches in a real-world setting. In this work, we investigate the required steps for bringing sensor and control networks, comprising more than 1800 devices, into 18 existing and new buildings. This enables both room-level monitoring and control, as well as the integration of building-wide automation. By introducing an ICT infrastructure, we pave the way towards holistic approaches on a district level. We describe the workflows used for selected instrumentation variants and show first insights from the operation of the resulting infrastructure. We show that the investigated instrumentation variants exhibit similar characteristics; however, they affect control behavior differently. We emphasize that instrumentation should be planned in the context of existing infrastructure. Moreover, we present and evaluate sample measurements obtained from different buildings. [ABSTRACT FROM AUTHOR]

Healthcare buildings exhibit a different electrical load predictability depending on their size and nature. Large hospitals behave similarly to small cities, whereas primary care centers are expected to have different consumption dynamics. In this work, we jointly analyze the electrical load predictability of a large hospital and that of its associated primary care center. An unsupervised load forecasting scheme using combined classic methods of principal component analysis (PCA) and autoregressive (AR) modeling, as well as a supervised scheme using orthonormal partial least squares (OPLS), are proposed. Both methods reduce the dimensionality of the data to create an efficient and low-complexity data representation and eliminate noise subspaces. Because the former method tended to underestimate the load and the latter tended to overestimate it in the large hospital, we also propose a convex combination of both to further reduce the forecasting error. The analysis of data from 7 years in the hospital and 3 years in the primary care center shows that the proposed low-complexity dynamic models are flexible enough to predict both types of consumption at practical accuracy levels. [ABSTRACT FROM AUTHOR]

Heating, Ventilating, and Air Conditioning (HVAC) systems are the major energy-consuming devices in buildings. Nowadays, due to the high demand for HVAC system installation in buildings, designing an effective controller in order to decrease the energy consumption of the devices while meeting the thermal comfort demands in buildings are the most important goals of control designers. The purpose of this article is to investigate the different control methods for Heating, Ventilating, and Air Conditioning and Refrigeration (HVAC & R) systems. The advantages and disadvantages of each control method are discussed and finally the Fuzzy Cognitive Map (FCM) method is introduced as a new strategy for HVAC systems. The FCM method is an intelligent and advanced control technique to address the nonlinearity, Multiple-Input and Multiple-Output (MIMO), complexity and coupling effect features of the systems. The significance of this method and improvements by this method are compared with other methods. [ABSTRACT FROM AUTHOR]