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The energy efficiency of existing buildings is a crucial factor in addressing energy consumption challenges in European countries, accounting for nearly 40% of the total energy usage. One such country is Cyprus, which faces significant challenges in transforming its existing building stock into energy-efficient and sustainable structures. To face this situation, extensive focus has been made by the government on the energy-efficient retrofit of non-residential public buildings erected before 2010, which lack any energy efficiency measures. This study examines the case of the Pano Polemidia Cultural Hall (PPCH), which represents the building stock of that period. Through the simulation of two scenarios, before and after the adoption of retrofit measures, the existing energy performance is initially evaluated and then the adoption of sustainable solutions, which improve substantially the energy efficiency and can be easily adopted from the relevant authorities, is explored. These retrofit measures include installation of HVAC system, covering of the shell of the building with external thermal insulation, lighting replacement with LED devices, installation of PV system and solar panels, and replacement of the external openings with aluminum windows. The results derived show that the energy consumption of the building was reduced from 468 to 218 kWh/m2·yr, with renewable energy sources (RESs) contributing 177 kWh/m2·yr, the CO2 emissions were reduced from 136.73 to 11.5 kg/m2·yr, while the reduction in energy consumption per sector ranged from 25% in lighting to 83% in hot water. Therefore, it is evident that a comprehensive retrofitting plan can transform the PPCH into a near-zero energy consumption building that also provides value to the local community and can act as a successful example for any other non-residential buildings with similar characteristics. [ABSTRACT FROM AUTHOR]

In the context of an insulated area with a subtropical climate, such as La Réunion island, it is crucial to reduce the energy consumption of buildings and develop local renewable energy sources to achieve energy autonomy. Direct current (DC) nanogrids could facilitate this by reducing the energy conversion steps, especially for solar energy. This article presents the deployment and efficiency evaluation of a 48 VDC low-voltage direct current (LVDC) nanogrid, from conception to real-world installation within a company. The nanogrid consists of a photovoltaic power plant, a lithium–iron–phosphate (LFP) battery, and DC end-use equipment, such as LED lighting and DC fans, for two individual offices. For identical test conditions, which are at an equivalent cabling distance and with the same final power demand, the total power consumed by the installation is measured for several stages from 50 to 400 W, according to a 100% DC configuration or a conventional DC/AC/DC PV configuration incorporating an inverter and AC/DC converter. The methodology used enables a critical view to be taken of the installation, assessing both its efficiency and its limitations. Energy savings of between 23% and 40% are measured in DC for a power limit identified at 150 W for a distance of 25 m. These results show that it is possible to supply 48 VDC in an innovative way to terminal equipment consuming no more than 100 W, such as lighting and air fans, using the IEEE 802.3 bt power over ethernet (PoE) protocol, while at the same time saving energy. The nanogrid hardware and software infrastructure, the methodology employed for efficiency quantification, and the measurement results are described in the paper. [ABSTRACT FROM AUTHOR]

Addressing Europe's decarbonisation challenge involves widespread deployment of nearly zero-energy buildings, deep energy renovations and renewable energy integration in the building sector. Enhancing energy efficiency in public buildings necessitates tailored solutions and strategic planning involving Local Public Administration. This work focuses on advancing insights into the application of the PrioritEE Decision Support Tool in Portuguese public buildings, highlighting the energy and financial savings and carbon dioxide emission reduction potential. Using detailed building characterisation data from energy performance certificates, we applied the tool across 22 public buildings of diverse typologies in three distinct regions of Portugal, representing various public entities. Results demonstrate the tool's adaptability, enabling a comprehensive assessment of energy performance and facilitating the exploration of customised energy efficiency and renewable energy solutions. The research emphasises the critical role of user-friendly tools in aiding policymakers and local administration technicians in meeting national renovation targets and contributing to the broader energy transition objectives. [ABSTRACT FROM AUTHOR]

Solar walls provide transformative solutions by harnessing solar energy to generate electricity, improve thermal comfort, and reduce energy consumption and emissions, contributing to zero-energy buildings and mitigating climate change. In hot and humid regions, solar walls can reduce indoor temperatures by 30% to 50%, significantly improving energy efficiency. Optimizing the performance of solar walls includes factors such as glazing, shading, solar orientation, ventilation, and catalytic techniques, allowing them to be adapted to different climates. Innovative solar wall variants that include photovoltaic panels, water storage, and phase-change materials offer multifunctionality and sustainability in building design and are in line with global energy efficiency and environmentally conscious goals. In addition, innovative solar wall variants that combine photovoltaic panels, water storage, and phase-change materials promise even more sustainability in building design. These multifunctional solar wall systems can efficiently heat, cool, and generate energy, further reducing a building's environmental impact. Solar walls have the potential to significantly reduce heating energy consumption; align with global goals for energy-efficient, environmentally conscious, and climate-responsive building design; and offer dynamic and adaptable solutions for sustainable architecture. [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]

ANNs have become a cornerstone in efficiently managing building energy management systems (BEMSs) as they offer advanced capabilities for prediction, control, and optimization. This paper offers a detailed review of recent, significant research in this domain, highlighting the use of ANNs in optimizing key energy systems, such as HVAC systems, domestic water heating (DHW) systems, lighting systems (LSs), and renewable energy sources (RESs), which have been integrated into the building environment. After illustrating the conceptual background of the most common ANN architectures for controlling BEMSs, the current work dives deep into relative research applications, thereby exhibiting their methodology and outcomes. By summarizing the numerous impactful applications during 2015–2023, this paper categorizes the predominant ANN-based techniques according to their methodological approach, specific energy equipment, and experimental setups. Grounded in the different perspectives that the integrated studies illustrate, the primary focus of this paper is to evaluate the overall status of ANN-driven control in building energy management, as well as to offer a deep understanding of the prevailing trends at the building level. Leveraging detailed graphical depictions and comparisons between different concepts, future directions, and fruitful conclusions are drawn, and the upcoming innovations of ANN-based control frameworks in BEMSs are highlighted. [ABSTRACT FROM AUTHOR]

Building geometry design decisions are important for energy efficiency and daylight performance. Sensitivity analysis, coupled with optimization, is an important approach to investigate and optimize building geometry in the early design stage. Incorporating skylights is an important daylighting strategy in commercial buildings; however, skylight-to-floor ratio (SFR) is often the only design variable evaluated in precedent studies. More design variables related to skylight geometry, clerestory geometry, skylight material, and building geometry need to be evaluated. This study investigates the skylight design of a 2000-square-meter commercial building. Eighteen design variables are evaluated according to their influence on building energy and daylight performance. One-at-a-time (OAT), linear regression, and Morris sensitivity analysis approaches are utilized to identify the most influential variables. Seven of the twelve building geometry variables and two of the six building material variables are considered as important. Then, a multi-objective optimization with genetic algorithms is processed to find out the optimal design solution. The three objectives are energy use intensity (EUI), daylight autonomy (DA), and daylight uniformity (DU). After the optimization, five candidate design options are picked from the Pareto front. Discussions are made on the features of these designs, and one design is selected as the optimal solution. [ABSTRACT FROM AUTHOR]

This research seeks to improve the temperature control of AHU in building sub-levels using optimization algorithms. Specifically, the study applies the FA and PSO algorithms to optimize the PID control of AHU's temperature. The study addresses the issue of temperature control in building sub-levels, which is a common challenge in HVAC systems. The study uses optimization algorithms and a nonlinear model to improve temperature control and reduce fluctuations in temperature from the desired setting. Additionally, a NL-ARX algorithm is utilized to create a nonlinear model based on the thermal dynamics and energy behavioral patterns of ACMV cooling systems. The study evaluates the performance of three controllers—PID, FA-PID, and PSO-PID—based on ITSE as a performance index. The study compares the performance of these controllers to achieve the desired temperature setting, and it analyses the influence of temperature regulation on occupant comfort levels. In this study, we compare different controllers using ITSE as a performance indicator. This shows how well different optimization algorithms work at setting the right temperature. The research gap is the lack of efficient temperature control solutions in building sub-levels that can optimize occupant comfort and energy efficiency. The experimental findings confirm that PSO-PID outperforms conventional PID and FA-PID optimization in terms of achieving the goal objective via computational complexity. Overall, this study's focus is to explore and compare different optimization algorithms to improve temperature control and occupant comfort in building sub-levels. [ABSTRACT FROM AUTHOR]

The energy efficiency of the building should be understood as the degree of preparation of the building to ensure the comfort of its use in accordance with its intended use with the lowest possible energy consumption of the building. The article presents an in-depth analysis of the possibility of changing the utility function of a historic building in such a way that ensures all aspects of comfort while meeting energy efficiency conditions. Combinatorics methods were used for this purpose. Four possible utility functions were proposed, including the reference state, i.e., the existing state. Five aspects of comfort were considered: thermal comfort, carbon footprint, energy efficiency, noise and vibration. For these five aspects of comfort, boundary conditions were adopted depending on the adopted building class. The selected utility function is therefore the result of comfort, energy efficiency and economic aspects. The purpose of the study was to verify whether the developed methodology for the selection of the utility function for historic buildings, based on combinatorial analyses, would allow the selection of the optimal function from the point of view of energy efficiency, user comfort and environmental impact. The methodology was tested on a historic building located in southern Poland. The new utility function for the analyzed building is to use the historic villa (or some part of it) as an Art Gallery. [ABSTRACT FROM AUTHOR]

As building energy consumption gains ever-increasing attention worldwide, the focus on addressing it through the examination and optimization of efficient heat recovery solutions continues to intensify. With well-insulated and airtight buildings, the proportion of heating needs attributed to ventilation is growing, leading to the widespread integration and optimization of heat recovery solutions in mechanical ventilation systems. Heat recovery in ventilation is a highly efficient strategy for reducing heat losses and conserving energy. This study involves the investigation of a ventilation unit installed in an apartment situated in Riga, Latvia, as a practical examination of heat recovery system efficiency within the Latvian climate conditions, representing a cold climate region. The objective of this study was to examine the heat recovery efficiency of the ventilation system in the Latvian climate with variable outdoor and exhaust air parameters, given that the dry heat recovery efficiency is different from the actual heat recovery efficiency. The ventilation unit was equipped with a plate heat exchanger at an airflow rate of 105 m3/h. To evaluate heat recovery efficiency, extensive measurements of air temperature and relative humidity were conducted. The collected data was analyzed, employing statistical regression analysis to ensure measurement reliability and assess correlations. The findings indicated a strong correlation between variables such as heat content, moisture content, and sensible air parameters. It was observed that the actual heat recovery efficiency was 6% higher than the calculated dry efficiency, emphasizing the importance of considering real-world conditions in heat recovery assessments. Additionally, regression analysis demonstrated a positive linear correlation with a coefficient of 0.77, highlighting the dependency between actual measurements and the theoretical model. These quantitative outcomes provide essential insights for optimizing heat recovery systems and enhancing energy-efficient ventilation practices, especially in cold climate environments. Moreover, this study highlights the strong correlation between variables such as heat content, moisture content, and sensible air parameters. Findings offer essential insights for optimizing heat recovery systems and enhancing energy-efficient ventilation practices, especially in cold climate environments. [ABSTRACT FROM AUTHOR]

The building sector, one of the most energy-consuming, is among the most current topics due to the maturing concerns about the anthropogenic factor's impact on CO2 quantities in the atmosphere and its association with global temperature rise. Using sustainable building materials is a promising alternative in building envelope applications to improve in-use energy efficiency. These materials, having a low environmental impact, the advantage of being renewable, and low embodied energy, contribute to global sustainability. This comprehensive literature review presents a broad overview of these materials' hygrothermal characteristics, thermal performance, and energy use. The main goal is to compile the most important research findings on these materials' capabilities for building construction and their contributions and effects on energy performance and thermal insulation. [ABSTRACT FROM AUTHOR]

Energy behaviours will play a key role in decarbonising the building sector but require the provision of tailored insights to assist occupants to reduce their energy use. Energy disaggregation has been proposed to provide such information on the appliance level without needing a smart meter plugged in to each load. However, the use of public datasets with pre-collected data employed for energy disaggregation is associated with limitations regarding its compatibility with random households, while gathering data on the ground still requires extensive, and hitherto under-deployed, equipment and time commitments. Going beyond these two approaches, here, we propose a novel data acquisition protocol based on multiplexing appliances' signals to create an artificial database for energy disaggregation implementations tailored to each household and dedicated to performing under conditions of time and equipment constraints, requiring that only one smart meter be used and for less than a day. In a case study of a Greek household, we train and compare four common algorithms based on the data gathered through this protocol and perform two tests: an out-of-sample test in the artificially multiplexed signal, and an external test to predict the household's appliances' operation based on the time series of a real total consumption signal. We find accurate monitoring of the operation and the power consumption level of high-power appliances, while in low-power appliances the operation is still found to be followed accurately but is also associated with some incorrect triggers. These insights attest to the efficacy of the protocol and its ability to produce meaningful tips for changing energy behaviours even under constraints, while in said conditions, we also find that long short-term memory neural networks consistently outperform all other algorithms, with decision trees closely following. [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]

A deep energy retrofit of building envelopes is a vital strategy to reduce final energy use in existing buildings towards their net-zero emissions performance. Building energy modeling is a reliable technique that provides a pathway to analyze and optimize various energy-efficient building envelope measures. However, conventional optimization analyses are time-consuming and computationally expensive, especially for complex buildings and many optimization parameters. Therefore, this paper proposed a novel optimization algorithm, MEVO (metamodel-based evolutionary optimizer), developed to efficiently identify optimal retrofit solutions for building envelopes while minimizing the need for extensive simulations. The key innovation of MEVO lies in its integration of evolutionary techniques with design-of-computer experiments, machine learning, and metaheuristic optimization. This approach continuously refined a machine learning model through metaheuristic optimization, crossover, and mutation operations. Comparative assessments were conducted against four alternative metaheuristic algorithms and Bayesian optimization, demonstrating MEVO's effectiveness in reliably finding the best solution within a reduced computation time. A hypothesis test revealed that the proposed algorithm is significantly better than Bayesian optimization in finding the best cost values. Regarding computation time, the proposed algorithm is 4–7 times faster than the particle swarm optimization algorithm and has a similar computational speed as Bayesian Optimization. [ABSTRACT FROM AUTHOR]

Background: Although many works focus on increasing the energy efficiency of buildings, there are still a number of problems that need to be solved, such as reducing heat losses at the window-to-wall interface, especially since the requirements for saving energy used for heating/cooling rooms are constantly increasing. This paper analyses the impact of the material parameters of the external wall and the window installation in the insulation layer on the size of thermal bridges around the window. Purpose: The aim of the work is to demonstrate the benefits resulting from the correct installation of the window, the appropriate location of the window in relation to the face of the external wall, as well as the correct selection of construction materials. Methodology: In order to show the improvement in the energy efficiency of buildings, an analysis of the heating/cooling energy consumption was carried out for the selected buildings. The thermal and humidity analyses were carried out using TRISCO program, while the economic analysis was performed using the Audytor OZC program. Results: It was found that the proposed system of window installation in the thermal insulation layer reduced the annual heating demand by at least 10% on average. Conclusions: It has been shown that the method of window installation and the type of the wall structural materials are interrelated and therefore should be considered simultaneously. Their proper selection allows for a reduction in the amount of energy needed for heating and cooling buildings, and thus a reduction of heating/cooling costs, as well as limiting greenhouse gas emissions. [ABSTRACT FROM AUTHOR]

The Smart Readiness Indicator (SRI) is a newly developed framework that measures a building's technological readiness to improve its energy efficiency. The integration of data obtained from this framework with data derived from Building Information Modeling (BIM) has the potential to yield compelling results. This research proposes an algorithm for a Recommendation System (RS) that uses SRI and BIM data to advise on building energy-efficiency improvements. Following a modular programming approach, the proposed system is split into two algorithmic approaches linked with two distinct use cases. In the first use case, BIM data are utilized to provide thermal envelope enhancement recommendations. A hybrid Machine Learning (ML) (Random Forest–Decision Tree) algorithm is trained using an Industry Foundation Class (IFC) BIM model of CERTH'S nZEB Smart Home in Greece and Passive House database data. In the second use case, SRI data are utilized to develop an RS for Heating, Ventilation, and Air Conditioning (HVAC) system improvement, in which a process utilizes a filtering function and KNN algorithm to suggest automation levels for building service improvements. Considering the results from both use cases, this paper provides a solid framework that exploits more possibilities for coupling SRI with BIM data. It presents a novel algorithm that exploits these data to facilitate the development of an RS system for increasing building energy efficiency. [ABSTRACT FROM AUTHOR]

Building-integrated photovoltaic technologies have considerable potential for the generation of onsite renewable energy. Despite this, their market penetration is in a relatively embryonic phase with respect to grounded or building-attached solutions, and they have limited commercial application. Their integration into building façades may represent a key asset in meeting the net-zero emissions by 2050 scenario, in particular for high-rise buildings in which the roof-to-façade ratio is unfavorable for the fulfillment of the energy load using only roof photovoltaic technology. Moreover, different façade orientations extend the production time throughout the day, flattening the power generation curve. Because of the present interest in BIPV systems, several researchers have conducted high-quality reviews focused on specific designs. In this work, various photovoltaic technologies and methods used to manufacture façade BIPV devices are reviewed with the aim of presenting researchers with the recent technological advancements and providing an overview of photovoltaic systems designed for different purposes and their applications rather than a detailed analysis of a specific technology. Lastly, future prospects and the limitations of building-integrated photovoltaic devices are presented. [ABSTRACT FROM AUTHOR]

Given its adaptable and efficient energy consuming devices during peak hours, the residential building sector is urged to take part in demand response (DR) initiatives with the use of a building energy management system (BMS). The residents of buildings with BMS enjoy secure, pleasant, and fully managed lifestyles. Although the BMS helps the building consume less energy and encourages occupant engagement in energy-saving initiatives, unwelcome interruptions and harsh instructions from the system are inconvenient for the inhabitants, which further discourages their participation in DR initiatives. Building automation control is a crucial factor for improving buildings' energy efficiency and management, as well as improving the electricity grid's reliability indices. Smart houses that use the right sizing procedure and energy-management techniques can help lower the demand on the entire grid and potentially sell clean energy to the utility. Recently, smart houses have been presented as an alternative to traditional power-system issues including thermal plant emissions and the risk of blackouts brought on by malfunctioning bulk plants or transmission lines. This paper describes the necessary technology requirements and presents the methodology and the decentralized building automation novel algorithm for efficient demand side management in a building management system. Human comfort aspects including thermal comfort and visual comfort were taken into consideration when selecting heating and lighting controls. The suggested BMS relies primarily on a load-shifting technique, which moves controllable loads to low-cost periods to avoid high loading during peak hours. The model aims to minimize the individual household electricity consumption cost while considering customers' comfort and lifestyle. All these are applied in an experimental university microgrid, and the results are presented in terms of energy saving in kWh, money in €, and working hours. The results demonstrated that the proposed approach might successfully lower energy use during the DR period and enhance occupant comfort. [ABSTRACT FROM AUTHOR]

This paper presents the concept of an innovative control of a central heating system in a multifamily building based on the original thermodynamic model, the resulting architecture of the control system, and the originally designed and manufactured wireless temperature sensors for thermal zones. The novelty of this solution is the developed layers of the control system: distributed measurement and correction analysis, which is based on the existing infrastructure and the local HVAC controller. This approach allows for the effective use of the measured temperature data from thermal zones and finally sending the value of the calculated correction of settings to the controller. Moreover, in the analytical layer, a model was also implemented that calculates the necessary amount of energy based on data from the subsystem of temperature sensors located in the thermal zones of the building. The use of the algorithmic strategy presented in this paper extends the functionality and significantly improves the energy efficiency of the existing, classic, reference heating control algorithm by implementing additional control loops. Additionally, it enables integration with demand-side response systems. The presented concept was successfully tested, achieving real energy savings for heating by 12%. These results are described in a case-study format. The authors believe that this concept can be used in other buildings and thus will have a positive impact on the energy savings used to maintain thermal comfort in buildings and significantly reduce CO2 emissions. [ABSTRACT FROM AUTHOR]

Against the background of climate protection and the rising costs of a fossil-fuel-based energy supply, the interest in the energy performance and indoor climate of buildings in real operation is rising. This paper, therefore, deals with the indoor climate investigation of two medium-sized office buildings in Germany by taking measurements over a whole year. These relate to one new building and one refurbished building. Sensors of various types were installed and operated in a large number of office rooms, so that in total results are available for over 100 rooms, typically occupied by one or two persons. The analysis focuses on the indoor temperature in summer and the air quality in winter based on the CO2 concentration. The comfort classes according to DIN EN 16798 including the adaptive comfort approach are used as a basis to cluster the results. Both buildings have movable sun protection and openable windows but no facilities for active cooling. They, thus, represent a large number of existing 'low tech' office buildings in Germany and central Europe. The results reflect the respective building concepts but also show a wide range between the rooms due to the user preferences and behaviour. The refurbished building shows better results, especially in terms of air quality but also in terms of summer room temperatures. This underlines the benefit of the targeted measures as a result of an analysis of the deficits in the existing building before the refurbishment. The additional measures for decentralised mechanical ventilation and passive cooling are having positive effects. As part of the projects, further measures to improve the indoor climate were investigated in both buildings. In one case, this involved CO2 traffic lights to stimulate personal window ventilation in winter, and in the other, the use of newly developed individual ceiling fans supports convective heat dissipation on the human body during hot spells in summer. The positive effect could be demonstrated for both measures. [ABSTRACT FROM AUTHOR]

Europe needs to save energy, and lowered indoor temperature is frequently promoted as part of the solution. To facilitate this, heating control systems with feedback from indoor temperature sensors are often required to avoid thermal discomfort and achieve long-term temperature reductions. This article describes a measurement- and interview-based study on feedback control where 107 Swedish multifamily buildings were analysed. The obtained results show that buildings with lowered indoor temperatures had reduced annual heating demand by 4 kWh/m2 and a reduced indoor temperature of 0.4 °C. There were, however, significant individual differences and even buildings with increased indoor temperatures, which harmed the energy savings. Temperature fluctuation was most often significantly reduced, but the impact on heating power demand during cold weather was, on average, only 2%. An interview with different actors indicated higher energy savings, possibly due to their stock's original room temperature levels. Several interviewees also mentioned other advantages of temperature mapping. Most of the results obtained in this study were in line with several previous investigations. The study's novelty lies in the large number of investigated buildings with mature commercial heat control technology, including PI-control for adjusting supply temperature, indoor temperature sensors in almost every apartment and a parallel analysis of additional affected parameters. [ABSTRACT FROM AUTHOR]

On 26 April 2017, Mexico formally adopted the 2030 Sustainable Development Agenda to link the national government to local governments, the private sector, civil society and academia. The restoration of heritage residential buildings (recovery programme) and the construction of new residential buildings aim to create a diversified environment for populations at different economic levels. However, the restoration programme faces conservation challenges. Given this context, this paper presents the results of in-depth, semi-structured interviews conducted with stakeholders in Mexico's heritage and energy sectors. The duration of each interview was approximately two hours, with 52,372 total interview words. The paper identifies prevailing opinions regarding prioritising historical values, energy efficiency, historic buildings and users concerning Mexico City's Historical Centre, a World Heritage Site needing a more appropriate sustainable development plan. Using grounded theory and thematic analysis, the interview data were analysed based on the interrelationship between thermal comfort, energy efficiency and heritage conservation changes over time. The results of this research will strengthen our understanding of the interventions and processes involved in managing and living in this World Heritage Site and its future impacts on buildings. The approach also underscores the importance of how stakeholders prioritise different values in making energy efficiency-promoting decisions and enhances awareness of the decision-making process and actions adopted by heritage building users. Thus, understanding the dynamic interrelationship between values, users and energy could improve the sustainable management of heritage sites and future development. [ABSTRACT FROM AUTHOR]

Green building materials have nontoxic properties and are made from recycled materials. This means they are, in most cases, created from renewable resources in comparison to non-renewable resources. This research aims to further improve the justification of green buildings and their materials. This is undertaken to determine the validity of such construction techniques. This research utilizes both qualitative and quantitative methods through five Australian case studies. The case studies, which are based on new and redeveloped structures, are selected via different geological locations and are evaluated via logical argumentation along with correlation research. Further, the research will address the problem by identifying a variety of green building materials that can be used to substitute non-green building materials. With careful comparisons among the five buildings, the green signs and implementation advantages and disadvantages will be evaluated. The result of this comparison will assist in improving the current education around the topic of green building and benefit the overall response to positive change within the construction industry. Although green building initiatives are not difficult to apply, they can be cost efficient. To maximize their cost efficiency, these initiatives need to be fully adopted. This includes the adaptation of specific building orientation, design, and sealing off penetrations to greatly improve passive heating and cooling. Further, the use of rainwater tanks also assists with energy efficiency by reducing the amount of mains water used. The utilization of natural lighting along with an advanced solar power system would further reduce the overall energy usage. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive analysis of the potential benefits and feasibility of integrating Building Energy Management Systems (BEMSs) with Building Information Modeling (BIM) in, but not limited to, the construction and building management sectors. By examining advantages, challenges, and real-world case studies, this study offers valuable insights into the impact of BEMS-BIM integration on building operations. The research methodology includes a literature review and bibliometric analysis to understand the subject domain and identify prevalent keywords. Additionally, case studies demonstrate the effectiveness of BEMS-BIM integration in real-world scenarios. This study investigates the possibilities and challenges of BIM to the BEMS methodology for energy-efficient industrial buildings, emphasizing the importance of addressing uncertainties and enhancing software interoperability. This research highlights the potential of BEMS-BIM integration to revolutionize building performance, enhance sustainability, and contribute to a greener and more efficient future for the construction and building management industries. [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]

Small and medium commercial buildings account for nearly half of the energy consumed by commercial buildings in the United States. While energy retrofits can significantly reduce building energy consumption, buildings' owners often lack the capital and experience to perform detailed energy audits and retrofit assessments. The purpose of this paper is to introduce a low-investment, bottom-up and simplified methodology for identifying energy retrofit opportunities that benefit the owners of small and medium sized office buildings In particular, the paper addresses small and medium commercial buildings on a university campus as a proof-of-concept for other owner-operators that have small and medium commercial facilities in their portfolio. The methodology consists of an eight-step framework using publicly-available and simplified tools. While energy audits and retrofit opportunity assessments are not new, a low-cost methodology for owner-operators of small and medium commercial buildings to analyze energy consumption and identify retrofit opportunities represents a contribution to knowledge. A medium office building on a university campus in Arizona served as a case study to validate the methodology. The case study showed a maximum potential energy reduction of an estimated 50%, but the figure varies based on the types of retrofit (deep versus light), energy conservation measures selected and implemented, invested resources, and interactive effects between measures. This methodology is extensible to other owner-operators that have building utility data and would like to perform retrofit opportunity assessments themselves. [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]

Improving energy efficiency indicators and reasonable use of energy resources in the context of the increasing demand for energy are sectors that must definitely be paid attention to. The fight against climate change must start in the construction sector, as buildings are the main consumers of energy. Saving energy through the rational use of energy sources and good thermal insulation of buildings allows you to reduce the amount of heating and/or cooling bills as well as to care for the environment by reducing emissions. This article presents aspects of improving the energy efficiency of a health clinic building in Mszana Dolna through the use of comprehensive thermal modernization of the external envelope. Thermal modernization of the most energy-intensive and leaky external partitions in the building, i.e., the external walls below and above the ground and the ventilated flat roof, managed to save 53% of the building's thermal energy, which directly translates into lowering the building's operating costs. We managed to achieve an improvement in energy efficiency ratios from 37% to almost 60%, and a reduction in CO2 emissions at a level of nearly 50%. [ABSTRACT FROM AUTHOR]

The increasing price of and demand for energy have prompted several organizations to develop intelligent strategies for energy tracking, control, and conservation. Demand side management is a critical strategy for averting substantial supply disruptions and improving energy efficiency. A vital part of demand side management is a smart energy management system that can aid in cutting expenditures while still satisfying energy needs; produce customers' energy consumption patterns; and react to energy-saving algorithms and directives. The Internet of Things is an emerging technology that can be employed to effectively manage energy usage in industrial, commercial, and residential sectors in the smart environment. This paper presents a smart energy management system for smart environments that integrates the Energy Controller and IoT middleware module for efficient demand side management. Each device is connected to an energy controller, which is the inculcation of numerous sensors and actuators with an IoT object, collects the data of energy consumption from each smart device through various time-slots that are designed to optimize the energy consumption of air conditioning systems based on ambient temperature conditions and operational dynamics of buildings and then communicate it to a centralized middleware module (cloud server) for management, processing, and further analysis. Since air conditioning systems contribute more than 50% of the electricity consumption in Pakistan, for validation of the proposed system, the air conditioning units have been taken as a proof of concept. The presented approach offers several advantages over traditional controllers by leveraging real-time monitoring, advanced algorithms, and user-friendly interfaces. The evaluation process involves comparing electricity consumption before and after the installation of the SEMS. The proposed system is tested and implemented in four buildings. The results demonstrate significant energy savings ranging from 15% to 49% and highlight the significant benefits of the system. The smart energy management system offers real-time monitoring, better control over the air conditioning systems, cost savings, environmental benefits, and longer equipment life. The ultimate goal is to provide a practical solution for reducing energy consumption in buildings, which can contribute to sustainable and efficient use of energy resources and goes beyond simpler controllers to address the specific needs of energy management in buildings. [ABSTRACT FROM AUTHOR]

The construction industry is responsible for high energetic consumption, especially associated with buildings' heating and cooling needs. This issue has attracted the attention of the scientific community, governments and authorities from all over the world, especially in the European Union, motivated by recent international conflicts which forced the countries to rethink their energy policies. Over the years, energy consumption has been based on non-renewable energy sources such as natural gas, oil and coal. Nowadays, it is urgent to implement solutions that aim to minimize these high energetic consumptions and act based on clean and renewable energy sources. In recent years, phase change materials (PCM) have become an area of high interest and development, since they allow to minimize the energy consumption in buildings, based in solar energy, due to their thermal storage capacity. The main objective of this work consists of a perspective of the evolution of the development and application of thermal storage technology through the incorporation of PCM in the construction sector, focusing on the last 10 years of research, showing the most recent developments of its application in construction materials, such as mortars, concrete, incorporation in porous aggregates, naturally based materials, carbon-based materials, boards, blocks and solar thermal systems. [ABSTRACT FROM AUTHOR]

As the urgency to mitigate climate change intensifies, the achievement of zero carbon targets in the built environment has become a critical objective. Building retrofitting plays a vital role in reducing energy consumption and carbon emissions in existing buildings. This paper presents an approach that combines Building Information Modeling (BIM) with multiple domains to strive for zero carbon targets in building retrofit projects. The proposed approach is based on a dynamic multi-parameter analysis that integrates indoor comfort, energy savings, CO2eq reduction, and the social cost of carbon while considering investment costs. Renewable energy technologies, such as photovoltaic panels and solar thermal systems, are emphasized to achieve the desired zero-carbon outcomes. Real-time monitoring mechanisms enable continuous performance evaluation and adaptive retrofit strategies for further energy savings. This approach is validated through a case study of an existing thermal bath building, known as "Terme Lucane", located in southern Italy. The results of the study demonstrate significant reductions in energy consumption and carbon emissions, highlighting the potential of the proposed approach to achieve zero carbon targets through the integration of multi-data BIM implementation. These findings offer a promising pathway for building retrofit projects aiming for zero carbon targets through multi-data BIM modeling. [ABSTRACT FROM AUTHOR]

Reducing energy demand and greenhouse gas emissions in the construction industry is one of the daunting challenges to be addressed in the context of global warming. The purpose of these analyses was to examine how the energy class of a multi-family residential building regarding thermal insulation and type of ventilation affects the usable energy demand for heating and ventilation purposes, the length of the heating season, and the amount of demand for energy consumed by auxiliary devices. This article presents the energy analyses of multi-family residential buildings with identical technical parameters located in different locations in Poland. For research purposes, a total of 354 energy balances were compiled, covering 59 meteorological stations, 3 types of ventilation systems, and 2 building insulation standards. This article presents the ways in which the location and energy class of buildings affect the length of the heating season and the demand for energy required for heating and ventilation purposes. The results of the analyses carried out in this article show that the location and the energy class of the building have a significant impact on the demand for primary energy (EP). As a result, it was concluded that when designating a reference building for the energy rating system, its location should be taken into account and reference buildings should be designated considering climate zones. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive analysis of public procurement documents in the domain of university buildings taken from the e-procurement platform, particularly focusing on their transformation towards more efficient energy consumption. Using a corpus of the titles of the public procurement documents from 2020 to 2022, we used Latent Dirichlet Allocation (LDA) for topic modeling to understand the key thematic areas of focus. The methodology presented in this study incorporated a bifurcated approach. This two-stage procedure began with preprocessing and dictionary creation from the corpus of titles of procurement documents. Following this, the Latent Dirichlet Allocation (LDA) model was employed for topic extraction and trend analysis, thereby providing a comprehensive understanding of the thematic progression in procurement practices over time. Our analysis revealed a shift in emphasis from modernization towards the adoption of energy-saving technologies as well as a growing focus on broader sustainability initiatives. However, a less prevalent topic was adherence to cooling & heating systems, suggesting potential areas for improvement in procurement practices. These findings contribute to the growing body of knowledge on sustainable procurement in university buildings and offer valuable insight for universities to enhance their energy efficiency strategies. [ABSTRACT FROM AUTHOR]

Construction projects demand a higher amount of energy predominantly for heating, ventilation, and illumination purposes. Modular construction has come into the limelight in recent years as a construction method that uses sustainable building materials and optimizes energy efficiency. Ventilation openings in buildings are designed to facilitate air circulation by naturally driven ventilation and could aid in reducing energy consumption in construction projects. However, a knowledge gap makes it difficult to propose the best dimensions of ventilation openings in buildings. Hence, the aim of this work is to empower the decision-making process in terms of proposing the best ventilation opening dimensions toward sustainable energy use and management in buildings. A novel framework is presented herein to evaluate the impact and propose the best dimensions of ventilation openings for metal frame modular construction in Brazil, using building information modeling. The ventilation openings were constructed and their dimensions evaluated in eight Brazilian cities, based on the bioclimatic zone (BioZ) classification indicated in ABNT NBR 15220: Curitiba (1st BioZ); Rio Negro (2nd BioZ); São Paulo (3rd BioZ); Brasília (4th BioZ); Campos (5th BioZ); Paranaíbe (6th BioZ); Goiás (7th BioZ); and Rio de Janeiro (8th BioZ). The study results show that the energy consumption of the same building model would vary based on the dimensions of ventilation openings for each BioZ in Brazil. For instance, modeling the same modular construction unit in the city of Rio Negro could consume around 50% of the energy compared to the same unit constructed in the city of Rio de Janeiro, using the small opening sizes based on the smallest dimensions of the ventilation openings. Similarly, modeling the construction unit in Curitiba, São Paulo, Brasília, Campos, Paranaíba, and Goiás could reduce energy consumption by around 40%, 34%, 36%, 18%, 20%, and 16%, respectively, compared to constructing the same building in the city of Rio de Janeiro, using the small opening sizes based on the smallest dimensions of the ventilation openings. This work could help practitioners and professionals in modular construction projects to design the best dimensions of the ventilation openings based on each BioZ towards increasing energy efficiency and sustainability. [ABSTRACT FROM AUTHOR]

More than half of the world population live in urban settlements which are responsible for a large share of energy consumption and, consequently, carbon emissions. The transition towards a more sustainable urban environment requires a change in paradigm in terms of how we design and manage our cities. Urban areas require innovative technologies and strategies to reduce energy consumption and carbon emissions, and to be included in comprehensive plans encompassing all technical, social and economic dimensions which characterise cities. This involves the transformation of urban contexts, with a focus on local and urban-level mitigation measures, such as the construction of positive energy buildings, deployment of renewable energy, promotion of a sustainable mobility, creation of resilient urban infrastructure, implementation of circular economy and recycling practices, etc. The present article provides a perspective on the sustainable energy transition in cities, focusing on the building and transportation sectors. Furthermore, insights on supporting mechanisms and innovative management strategies are presented. [ABSTRACT FROM AUTHOR]

Along the roadmap to a Sustainable Real Estate-Scape, energy retrofit campaigns on wide city compartments represent a pivotal task, where the importance of the collaboration between the public and private sectors is crucial. Energy retrofit programs on building assets are subject to multiple uncertainty factors (e.g., climate, energy-economy forecasts, etc.) that act as a primary barrier to investment in this field. This paper aims to discuss risk management techniques to understand better how to deal with this kind of uncertainty. The research specifically addresses the techniques of sensitivity analysis and Monte Carlo simulation, focusing first on the phase of variables selection and their probability definition, including climatic, environmental, energy, economic, financial, and stochastic parameters. In this article, it is suggested to include correlation coefficients in the input variables of risk analysis, preferring the two-dimension Monte Carlo simulation to its simple version, since the results are more reliable when separating aleatory from epistemic uncertainty; thus, the results are more reliable. Moreover, it is seen how a sensitivity analysis based on percentile variations of the inputs provides a more accurate representation of risk if compared to the most common sensitivity analysis based on percentage deviations of the inputs. Conducting a sensitivity analysis using percentile variations gives realistic and reliable results, reflecting the tailored definition of uncertainty around the inputs on the basis of specific market analyses or historical series. [ABSTRACT FROM AUTHOR]

The publication analyzes aspects of energy efficiency of various types and several technological concepts of facade washing devices. The conducted analyses and tests answered the most essential question of this stage: which, from the technical point of view of solving the problem of stabilizing the track of the washing machine, gives the highest guarantee of effective stabilization of this track in unfavorable wind conditions. The literature analysis showed several solutions to the problem of track stabilization of facade washing machines on the market, of which suction cups stabilize the machine device, a system not attached to the wall of the building, and fans or propellers have been commercialized. However, it pointed out that there are no universal solutions. Detailed analysis of solutions under many criteria led to finding the solution with the fewest defects at this stage of analysis and potentially the greatest chance of success. Thanks to the results of work and research on the effectiveness of technology, it was possible to implement a number of solutions leading to the improvement of work efficiency, safety, and the development of Industry 4.0. [ABSTRACT FROM AUTHOR]

Existing buildings are a source of great potential for energy efficiency through renovation. In this study, the national energy requirements equivalent for the major renovation of existing non-residential buildings using the example of office buildings in five European case countries are drawn out and discussed. The non-residential building sector has been found to be complex and heterogenous with much less available data than for the residential sector, but having greater average specific energy consumption per floor area. The existing non-residential building stock in the studied countries has been divided into varying amounts of groups and sub-groups. The energy requirements have been shifting from the increasing requirements for the U-values of the building envelope (before the 2000s) towards calculated energy demand for buildings as a whole (currently). The requirements for buildings in the near future will be carbon emission based. The energy efficiency of N-RBs in all of the studied case countries have been steadily improved during different decades of construction. Relatively older office stock combined with the relatively late introduction of national energy requirements was found to have a significantly larger potential for energy savings. Different terms and contents in national requirements were used to describe the principles of "major renovation" as from EPBD. [ABSTRACT FROM AUTHOR]

Growing concerns over environmental issues and sustainable living have resulted in increased interest in renewable energy and energy efficiency. The real estate market is no exception, with homeowners increasingly considering the market value of green and sustainable buildings, which can offer both energy efficiency and potential health benefits. This study investigates the level of interest among homeowners in investing in renewable energy sources and energy efficiency measures for their homes and how it relates to their perception of the market value of green or sustainable buildings in the real estate market. A survey was conducted in the Paphos urban complex in Cyprus, with 180 participants over the age of 18. The participants were selected through a random sampling method and were representative of the general population in terms of gender, age, and income. Data were collected on their attitudes towards renewable energy sources and energy efficiency, as well as their perceptions of the market value of green buildings. The data collected were analyzed using various statistical methods, including Cronbach's α coefficient, the non-parametric Friedman test, descriptive statistics, and factor analysis, with the Statistical Package for the Social Sciences (SPSS) being used for coding and analysis. Results indicate that 64% of the homeowners surveyed were interested in investing in renewable energy sources, and 72% were interested in energy efficiency measures. Additionally, findings suggest a moderate level of interest (58%) among homeowners in investing in renewable energy sources and that this is positively associated with their perception of the market value of green buildings. Furthermore, homeowners with higher income and education levels tend to be more interested in investing in renewable energy sources and energy efficiency measures and perceive green buildings as having higher market value. This study provides insights into the factors that drive homeowners' investment in renewable energy sources and energy efficiency measures, shedding light on the relationship between homeowners' perceptions of the market value of green buildings and their interest in such investments. [ABSTRACT FROM AUTHOR]

Economical and efficient use of energy is promoted around the world as a model of conscious care for the environment in which we live. A mere change of habits in the use of energy can reduce its costs by 5% to 15%, and investments in energy-saving technologies can pay for themselves after just a few years. This case study shows how significant steps can be taken in saving energy in the building of public utility buildings through deep thermomodernization using renewable energy sources—compressor heat pumps and photovoltaics. The article presents a comprehensive thermomodernization of a school building made according to Polish regulations. A detailed description of the tested object is given, and the calculation procedure is described. Next, the optimal investment variant and ex post analysis are described. The implementation of these projects significantly improved the energy efficiency of the building and generated final energy savings of 80%, which will significantly reduce the school's operating costs. Thanks to the applied improvements, it was possible to save 72.30% of thermal energy in the building, which directly translates into lowering the building's operating costs. The improvement of energy efficiency indicators ranges from 66% for usable energy to almost 85% for non-renewable primary energy. Furthermore, by reducing the demand for energy used in the building by nearly 74%, we see a reduction in CO2 emissions. The methods used were desk research and an extended case study of Poland, a country facing a number of problems related to energy prices during the energy crisis. In this article, we identify the challenges faced by Poland due to its geopolitical situation, and the solutions introduced to the difficult situation in the energy market come in the form of the thermomodernization of buildings. It was on this basis that Poland was selected as a case study. [ABSTRACT FROM AUTHOR]

The automation strategies currently used in HVAC systems do not control the system temperature variables (supplied air, chilled, and condensation water temperatures) in an optimized way. Normally, these temperatures are fixed in design conditions, or vary according to the weather conditions. However, studies demonstrate that adequate control of these three temperatures can provide significant reductions in the energy consumption of the air conditioner system. Therefore, this work analyzes the benefits of individualized and integrated automation of these three variable temperatures in different tropical and subtropical weather conditions through computer simulation for a typical commercial building. The results of integrated automation show savings in consumption between 5.03% and 19.68% compared to a fixed control, and between 3.22% and 8.21% compared to a weather-based control alone, showing that the integrated strategies are better than both models adopted as market benchmarks. [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]

The development of data-driven building energy consumption prediction models has gained more attention in research due to its relevance for energy planning and conservation. However, many studies have conducted the inappropriate application of data-driven tools for energy consumption prediction in the wrong conditions. For example, employing a data-driven tool to develop a model using a small sample size, despite the recognition of the tool for producing good results in large data conditions. This study delivers a review of 63 studies with a precise focus on evaluating the performance of data-driven tools based on certain conditions; i.e., data properties, the type of energy considered, and the type of building explored. This review identifies gaps in research and proposes future directions in the field of data-driven building energy consumption prediction. Based on the studies reviewed, the outcome of the evaluation of the data-driven tools performance shows that Support Vector Machine (SVM) produced better performance than other data-driven tools in the majority of the review studies. SVM, Artificial Neural Network (ANN), and Random Forest (RF) produced better performances in more studies than statistical tools such as Linear Regression (LR) and Autoregressive Integrated Moving Average (ARIMA). However, it is deduced that none of the reviewed tools are predominantly better than the other tools in all conditions. It is clear that data-driven tools have their strengths and weaknesses, and tend to elicit distinctive results in different conditions. Hence, this study provides a proposed guideline for the selection tool based on strengths and weaknesses in different conditions. [ABSTRACT FROM AUTHOR]

Highlights: Water-sourced heat pumps with hot and cold tanks or a connection to District Energy Systems may be the most economical solution. Concrete interior walls and floors may be the most ecological solution in many cases. Pandemics show that both the EU's and the US's ventilation strategies need to be modified. Monitoring using building automatics allows HVAC optimization in the occupancy stage. Historic buildings with "classic form and ultramodern function" may reach zero energy/emission. The Fourth Industrial Revolution forms a smart grid with diverse sources of energy through the interconnectivity of data. Buildings that were previously the biggest users of energy are now becoming energy producers. Yet, buildings are also continually changing. The ecological definition of buildings, in addition to the building itself, includes solar panels and geothermal energy storage. The need for decarbonization and energy-efficiency brought about the implementation of heat pumps in buildings. The most economic type of heat pump is a water-sourced heat pump with hot and cold tanks or a connection to the District Energy System. Monitoring using building automatics allows HVAC optimization in the occupancy stage. Until the SARS-CoV-2 pandemic, the EU and the US differed in their air handling methodology, but the pandemic showed the limitations of both approaches and led to the creation of a new, integrated approach. These new ventilation systems, based on filtration instead of dilution, come together with decarbonization and the demand for new and retrofitted buildings to be smart, have zero emissions and excellent indoor environments, and be affordable. To fulfill these conditions, design teams must extrapolate experience with passive houses and introduce expertise in building automatic controls (BAC). The authors analyze the heating cooling and ventilation aspects of dwellings in a technology called Ecological Thermo-Active (ETA) technology that can also be applied to the interior retrofitting of buildings, including those with historic facades. The building "with classic form and ultramodern function" is an example of this changing design paradigm. [ABSTRACT FROM AUTHOR]

The need for healthy indoor conditions, the energy crisis, and environmental concerns make building ventilation systems very important today. The elements of ventilation systems to reduce energy intensity are constantly the subject of much scientific research. The most recent articles published in the last three years are analyzed in this paper. Publications focused on the topic of reducing energy consumption in ventilation systems were selected and divided into five key research areas: (1) the aspect of the airtightness of buildings and its importance for the energy consumption, (2) the methods and effects of implementing the concept of demand-controlled ventilation in buildings with different functions, (3) the possibilities of the technical application of decentralized ventilation systems, (4) the use of earth-to-air heat exchangers, (5) the efficiency of exchangers in exhaust air heat-recovery systems. The multitude of innovative technologies and rapid technological advances are reflected in articles that appear constantly and prompt a constant updating of knowledge. This review constitutes a relevant contribution to recognizing current advancements in ventilation systems and may be helpful to many scientists in the field. [ABSTRACT FROM AUTHOR]

Building energy efficiency has grown strong in a context of soaring energy prices, especially in Europe. The use of energy-saving devices strongly influences its improvement, but in many cases, it is far from sufficient., especially if the energy comes from renewable sources with forced production. In the case of buildings, these are usually photovoltaic (PV) sources. For this reason, energy management systems (EMS) are becoming increasingly popular as they allow the increase in self-consumption and reduce the size of energy storage. This article presents analyses of historical energy consumption profiles in selected small- and medium-sized buildings powered by renewable energy sources. The implementation limitations of this type of systems, depending on the profile of the building, were identified and guidelines were presented to assess low-cost solutions dedicated to small buildings and considering the actual conditions of existing systems. Statistical analyzes were conducted for the energy demand profiles of 15 different buildings. The analyzes consisted of the preparation of box plots for each hour of working days and the calculation of the relative standard deviation (RSD) index for annual profiles of 60 min periods. The analyzes showed that the RSD index has low values for commercial buildings (e.g., hospital 7% and bank 15%) and very high values for residential buildings—even over 100%. On this basis, it can be concluded about the usefulness of energy profiles for demand forecasting. The novelty of the proposed method is to examine the possibility of using measurement data as data to forecast energy consumption based on statistical analysis, dedicated to low-cost EMS system solutions. [ABSTRACT FROM AUTHOR]

In most cases, internal insulation is the only solution to improve the energy efficiency of historic buildings. However, it is one of the most challenging and complex energy efficiency measures due to changes in boundary conditions and hygrothermal behavior of the wall, particularly in cold climates. This study presents the long-term monitoring of the hygrothermal performance of an internally insulated historic stone wall building. The study aimed to assess the hygrothermal behavior of the dolomite wall if mineral wool insulation is applied internally on the north-east wall in the rooms with and without high internal moisture load. The measurements included temperature, relative humidity, water content, and heat flux. Monitoring results are compared with 1D hygrothermal simulations and a building energy consumption simulation. The in situ measurement results and hygrothermal assessment shows energy consumption decreased by 55% with relative humidity under the insulation staying belove 60% for most of the time, with short periods of increase over 80%. Energy consumption simulation shows an energy saving potential of up to 72% in the case of proper energy management. [ABSTRACT FROM AUTHOR]

The article presents the results of the analysis of parameters describing electricity consumption in individual phases of the production cycle on a poultry farm. One full broiler rearing cycle on the farm was analyzed. Electrical parameters were tested during the broiler rearing process using a power quality analyzer with a measurement interval of 1 min. During the tests, the analyzer recorded the active and reactive power, phase and line voltage, power factor, and frequency. On the basis of collected data, original indicators describing electricity consumption per chicken wkA, wkQ, and per unit area wiA, wiQ were determined. The regression curves of active and reactive power consumption in particular stages of the cycle were also determined, and the minimum and maximum values of active and reactive power consumption were determined. The accomplished research can be used in planning electricity demand in energy-self-sufficient areas. The presented original indicators can be used to determine power demand on broiler farms, depending on the planned production volume or size of farm buildings. [ABSTRACT FROM AUTHOR]

The building sector greatly contributes to energy consumption and Greenhouse Gas emissions, relating to the whole building life cycle. Boasting a huge building heritage of historical and architectural value, Europe faces challenging retrofit perspectives, as the potential for high energy efficiency has to be exploited while preserving the buildings' original characteristics. The present work aims to feature the influence of a passive strategy on a heritage building in a mild climate. As historical its façade cannot be modified, its large glazing areas involve multiple issues, such as an increase in the heating ( Q H) and cooling ( Q C ) energy demands and the risk of thermal discomfort. Thus, window replacement was proposed for retrofitting. A dynamic simulation model in TRNSYS was validated with experimental data collected by the continuous monitoring of walls of different thicknesses and orientations. Solutions from replacement with Double Glazing Units (DGUs) with improved thermal insulation, to internal shading activation were applied. All configurations were compared in terms of Q H , Q C , thermal performance of the building and user comfort (Fanger). Low-e DGU enabled the saving of up to 14% of the annual energy demand, and shading also offered good results in summer, reducing Q C by 19%. In summer, DGU involved a maximum PPD reduction of 10 units. [ABSTRACT FROM AUTHOR]

Building energy efficiency is vital, due to the substantial amount of energy consumed in buildings and the associated adverse effects. A high-accuracy energy prediction model is considered as one of the most effective ways to understand building energy efficiency. In several studies, various machine learning models have been proposed for the prediction of building energy efficiency. However, the existing models are based on classical machine learning approaches and small datasets. Using a small dataset and inefficient models may lead to poor generalization. In addition, it is not common to see studies examining the suitability of machine learning methods for forecasting the energy consumption of buildings during the early design phase so that more energy-efficient buildings can be constructed. Hence, for these purposes, we propose a multilayer extreme learning machine (MLELM) for the prediction of annual building energy consumption. Our MLELM fuses stacks of autoencoders (AEs) with an extreme learning machine (ELM). We designed the autoencoder based on the ELM concept, and it is used for feature extraction. Moreover, the autoencoders were trained in a layer-wise manner, employed to extract efficient features from the input data, and the extreme learning machine model was trained using the least squares technique for a fast learning speed. In addition, the ELM was used for decision making. In this research, we used a large dataset of residential buildings to capture various building sizes. We compared the proposed MLELM with other machine learning models commonly used for predicting building energy consumption. From the results, we validated that the proposed MLELM outperformed other comparison methods commonly used in building energy consumption prediction. From several experiments in this study, the proposed MLELM was identified as the most efficient predictive model for energy use before construction, which can be used to make informed decisions about, manage, and optimize building design before construction. [ABSTRACT FROM AUTHOR]