Abstract: The rapid development of machine learning and artificial intelligence technologies has promoted the widespread application of data-driven algorithms in the field of building energy consumption prediction. This study comprehensively explores diversified prediction strategies for different time scales, building types, and energy consumption forms, constructing a framework for artificial intelligence technologies in this field. With the prediction process as the core, it deeply analyzes the four key aspects of data acquisition, feature selection, model construction, and evaluation. The review covers three data acquisition methods, considers seven key factors affecting building loads, and introduces four efficient feature extraction techniques. Meanwhile, it conducts an in-depth analysis of mainstream prediction models, clarifying their unique advantages and applicable scenarios when dealing with complex energy consumption data. By systematically combing the existing research, this paper evaluates the advantages, disadvantages, and applicability of each method and provides insights into future development trends, offering clear research directions and guidance for researchers.
Permalink: https://doi.org/10.3390/app15063086

Abstract: Featured Application:
The findings of this review provide a comprehensive understanding of return temperature reduction strategies in district heating systems, with a focus on system-level components. These insights can guide the design, operation, and optimization of district heating networks, enhancing energy efficiency and reducing operational costs. This review also serves as a valuable resource for developing smart control systems and cascading applications, thereby supporting the transition to low-temperature district heating schemes and facilitating sustainable energy goals.
Abstract:
This review paper provides a comprehensive examination of current strategies and technical considerations for reducing return temperatures in district heating (DH) systems, aiming to enhance the utilization of available thermal energy. Return temperature, a parameter indirectly influenced by various system-level factors, cannot be adjusted directly but requires careful management throughout the design, commissioning, operation, and control phases. This paper explores several key factors affecting return temperature, including DH network, heat storage, and control strategies as well as the return temperature effect on the heat source. This paper also considers the influence of non-technical aspects, such as pricing strategies and maintenance practices, on system performance. The discussion extends to the complex interplay between low return temperatures and temperature differences, and between operational temperature schemes and economic considerations. Concluding remarks emphasize the importance of adopting a holistic approach that integrates technical, operational, and economic factors to improve DH system efficiency. This review highlights the need for comprehensive system-level optimization, effective management of system components, and consideration of unique heat production characteristics. By addressing these aspects, this study provides a framework for advancing DH system performance through optimized return temperature management.
Permalink: https://doi.org/10.3390/app15062982

Abstract: Highlights:
• A multi-objective optimization evaluation system considering economy, environmental protection and hydrogen blending ratio.
• Under conditions of constant heat consumption, the hydrogen blending ratio is 10.67 % after optimization.
• The Integrated energy system maximizes the integration of renewable energy sources, and reduces carbon emissions.
Abstract:
In order to solve the problem of new energy consumption and reduce the use of fossil fuels, a multi-objective optimization and evaluation system is constructed for Integrated energy system (IES). The system takes into account factors including economy, environmental conservation, and hydrogen blending ratio. It establishes a mixed-integer linear programming model for multi-objective optimal scheduling. The conversion of renewable energy sources into electricity, followed by its conversion into hydrogen. By changing the hydrogen blending ratio of natural gas pipeline network, each objective function can reach the best. The optimal operation of each equipment of the heat and cold networks is obtained by changing the proportion of hydrogen blending into the natural gas pipeline network. Based on the multi-objective comprehensive analysis under conditions of constant heat consumption, the optimal solution is a hydrogen blending ratio of 10.67 %. This solution results in a cost of $305,426.29, carbon emissions of 19,709,221.40 kg, and a total natural gas consumption of 19,128,759.35 m3. Compared with the conventional energy system, this scheme not only offers better economy but also has characteristics of energy saving and emission reduction, making it superior in comprehensive evaluation compared to other schemes. In conclusion, this system enhances energy utilization efficiency, maximizes the integration of renewable energy sources, and reduces carbon emissions.
Permalink: https://doi.org/10.1016/j.applthermaleng.2025.125942

Abstract: The accurate assessment of parameters such as burn degree, volume, and depth is a prerequisite for the effective treatment of patients. However, as an unsteady heat transfer process, the temperature of the burn damage volume changes over time, and it is difficult to accurately calculate the integral value of the damage, which is used to assess the burn degree. Therefore, it is impossible to accurately determine the location and volume of damage at all burn degrees. In this study, the C language is used to program a user-defined function of the burn damage integral formula, and the coupled numerical simulation method is used to calculate the heat transfer and damage in a high-temperature water burn process. Then, the temperature and burn damage integral value of each point can be determined to accurately assess and distinguish the burn degree in real time, and estimate the position distribution, volume size, and transient change trend of each burn degree. Under the working conditions selected in this paper, the heat source mainly affects the epidermis and dermis directly below, and has less influence on the area above, which is in convective heat transfer. The damage integral value is very sensitive to temperature, and the highest damage integral value caused by 373 K is two and four orders of magnitude higher than that of 363 K and 353 K, respectively. The increase in the heat source temperature caused the volume of a third-degree burn to increase rapidly in the early stage of injury, but the volume of second-degree and first-degree burns did not change much. After heating at 373 K for 15 s and delaying the action for 45 s, the volume of first-, second-, and third-degree burns accounted for 0.4, 2.9, and 1.9%, respectively, and the total volume of damage accounted for only 5.2% of the total volume.
Permalink: https://doi.org/10.3390/thermo5010004

Abstract: Achieving “Climate-Neutral and Smart Cities” is now high on the agenda and the city of Oslo has set an even more ambitious goal of becoming a zero-emission city. However, the promotion of more compact development may lead to some negative effects such as the entrapment of polluted air, wind tunnel effects or urban heat islands. Green infrastructure (GI) can be used as a mitigation measure, bringing many benefits such as improving air quality, regulating thermal environment, reducing energy consumption, managing storm water, or promoting urban biodiversity. In this work, we aim to map the existing green roof infrastructure in Oslo and develop an evidence-base strategy for its further development. Interviews with stakeholders revealed the practical challenges such as structural limitations, high installation and maintenance costs, and regulatory compliance issues. However, they also recognized the significant environmental advantages that highlight the importance of green roofs in urban sustainability strategies. Geographical information system (GIS) tools are used to identify the potential areas for further green roof implementation, considering the spatial, morphological and environmental conditions. 91 Priority green roof areas (PRIOGRAs) and 13 Potential green roof areas (PGRAs) are identified as the most suitable after applying filters like roof surface area, and dominating roof area and slope criteria, exclusion of cultural heritage buildings and existing green roofs, tree density per person deficit, and building age. 2044 roofs can be considered suitable without the building age criteria. These findings will potentially help providing actionable insights for policymakers, urban planners, and the research community.
Permalink: https://doi.org/10.3384/ecp212.036

Abstract: Highlights:
• Comprehensive consideration of the AI technology for the optimizing of HVAC systems.
• Emphasis on the research hotspots of AI in optimizing the operation of HVAC systems.
• Detailed presentation of the procedural steps of AI application in system optimization.
• Demonstration the potential of applying AI-based strategies in engineering practices.
Abstract:
With the rapid development of the artificial intelligence (AI) technology, its application in optimizing heating, ventilation and air-conditioning (HVAC) systems operation is becoming increasingly widespread. This study reviews the latest advances in AI optimization for HVAC systems operation, systematically outlining the characteristics of the AI technology and its various application methods in air conditioning systems. The main features of the AI technology are first introduced. The main algorithms of supervised learning, reinforcement learning, and deep learning are then analyzed in the fields of air conditioning operation optimization, energy consumption prediction and control, indoor environmental protection, and fault detection and diagnosis. The combination of the AI and digital twin technologies is also explored. This review study focuses on the intelligent control technology, multi-objective optimization of system operation, system optimization based on occupant behavior and thermal comfort, and system fault detection and diagnosis. Although the AI technology has led to satisfactory results in air conditioning system optimization, its practical applications still face challenges, such as the model accuracy and generalization ability, applicability, and integration with existing systems. The analysis conducted in this paper provides a foundation for the optimization of HVAC system operation.
Permalink: https://doi.org/10.1016/j.rineng.2024.103765

Abstract: Highlights:
• The novel analysis strategy developed to understand data normalization method.
• The significant influence of data normalization on the predictive capabilities of various ANN models.
• More effective combinations of ANN models with specific data normalization strategies
. • Evaluating the correlation between each data normalization method on the energy consumption.
Abstract:
The study investigates the impact of data normalization on the prediction of electricity consumption in buildings using four multilayer Artificial Neural Networks (ANN) algorithms: Long Short-Term Memory Networks (LSTM), Levenberg–Marquardt Back-propagation (LMBP), Recurrent Neural Networks (RNN), and General Regression Neural Network (GRNN). Four data normalization approaches, Min-Max Scaling, Mean, Z-score, and Gaussian function were assessed on experimental datasets. The LSTM algorithm, when combined with Min-Max normalization, showed the most favorable predictive capabilities, with a low Coefficient of Variation of the Root Mean Square Error (CVRMSE) of 10.3 and Normalized Mean Bias Error (NMBE) of 0.6. The remaining three normalization approaches showed satisfactory concordance with empirical data, but with slight disparities in precision. The LMBP model, when using Z-score normalization, had favorable performance in forecasting electricity consumption, but the discrepancies across the models were not significant. The Recurrent Neural Network (RNN) model, when used with Gaussian normalization, exhibited the most favorable performance, with the lowest Coefficient of Variation of Root Mean Square Error (CVRMSE) at 11.8 and Normalized Mean Biased Error (NMBE) at 0.6. The Generalized Regression Neural Network (GRNN) model, trained on unprocessed data, exhibited superior performance, with the lowest Coefficient of Variation of Root Mean Square Error (CVRMSE) at 19.2 and NMBE at 1.0. In conclusion, the study highlights the significant influence of data normalization on the predictive capabilities of various ANN models, suggesting that careful use of data normalization techniques can significantly improve the accuracy of electricity consumption forecasting in buildings.
Permalink: https://doi.org/10.1016/j.scs.2024.105570

Abstract: In Europe, where buildings are responsible for about 36% of total greenhouse gas emissions, largely due to their operational energy use, addressing climate change necessitates reducing buildings’ energy consumption, particularly for climatization. Despite its energy demands, climatization is crucial for a healthy indoor environment. Thus, efforts to enhance climatization efficiency must aim to both lower energy use and preserve indoor comfort. This research explores resilience of a hybrid ventilation strategy in a mixed-mode office building in a cold climate. The study evaluates the energy performance of two ventilation strategies—full mechanical ventilation and hybrid ventilation—under future conditions relative to contemporary scenarios. Two distinct emission scenarios, RCP 4.5 (mid-emission) and RCP 8.5 (high emission), are considered, spanning three periods: near future, far future, and a reference period. Oslo, the capital of Norway, serves as the selected case study because it exemplifies a relatively large city by Nordic standards, situated in a cold and humid continental climate. Weather data were compiled in accordance with EN ISO 15927-4 standards, using a 30-year period for reference. Subsequently, the Perez model was applied to separate global radiation into its direct and diffuse elements. Following this, simulations of the indoor climate and energy requirements were conducted using IDA ICE. The results indicate that adopting hybrid ventilation can lead to energy savings of up to 40% in scenarios of high emissions during the far future. This efficiency gain is primarily attributed to an extension of the window opening period, which is approximately 6% longer than the baseline period. Such an increase in window opening duration notably contributes to the reduction of indoor CO₂ levels, as illustrated by the case of Norway. These findings emphasize the critical role of incorporating passive design solutions, like hybrid ventilation through window openings, into both architectural design and urban planning practices in cold climates.
Permalink: https://doi.org/10.1007/978-3-031-69626-8_83

Abstract: Buildings account for a significant proportion of the total energy consumption in the global energy sector, with a value of about 40% of the total energy consumption and greenhouse gas emissions, and studying building energy can play a crucial role in shaping sustainable development goals (SDGs). Interest in achieving net-zero and positive-energy buildings has been growing in recent years. Recent research publications to this day exhibit a lack of focus on net-zero-energy buildings (NZEBs); a more comprehensive literature would include the challenges and opportunities of such buildings. For this reason, this chapter aims to provide a comprehensive up-to-date review of the recent progress, challenges, and opportunities in constructing net-zero-energy buildings in tropical climates. The findings show that the opportunities for constructing net-zero-energy buildings in tropical climates include, but are not limited to, the availability of solar energy, government policies and incentives promoting NZEBs, monetary benefits from increasing the demand for energy-efficient buildings, creating employment opportunities in research and development, reducing operational energy costs, and decreasing the carbon emissions of buildings. On the other hand, the challenges to constructing NZEBs in tropical climates are their high initial costs, regulations and policies, technological barriers to developing energy-efficient materials that are both cost-effective and readily available, connecting net-zero-energy buildings to smart grids, environmental and socioeconomic concerns, occupant behavior, and retrofitting existing buildings.
Permalink: https://doi.org/10.1007/978-3-031-69626-8_84

Abstract: The topic on how to reduce energy use by close monitoring system, such as Energy Monitoring System (EMS), has attracted more and more interests nowadays. However, there is a noticeable lack of practical case studies illustrating the concrete steps, the potential challenges, and actionable solutions along with the process. This study is based on a pilot project for Vestfold and Telemark County municipality in Norway, aiming to reduce energy use by tracking and analyzing energy consumption patterns on a weekly basis. Deployment of EMS system offers the real-time monitoring capabilities, providing an invaluable tool for energy use optimization. By studying the interactive measurements and illustrating trends through interactive diagrams, EMS system producing relevant results parallelly facilitates precise energy optimization, enhancing sustainability and facilitating the efficiency of energy management. This study underscores the contribution of the EMS system and its practical barriers and their respective resolutions, emphasizing the necessity of these insights toward zero carbon footprint and energy-efficient future. Notably, the close monitoring and optimization of energy usage in this case study culminated in savings of 9.8 million Norwegian kroner in 2022.
Permalink: https://doi.org/10.1007/978-3-031-69626-8_96

Abstract: It is known that the built environment is one of the largest contributors to greenhouse gas emissions. In the building sectors, significant efforts have been made to reduce emission associated with energy use in buildings and construction materials. However, the process related to integrated technical building installations (ITB) is not well addressed. The study includes a case study for zero-emission educational building. By examining case studies, best practices, and emerging problems, this research sheds light on the transformative power of integrated technical building installations in optimizing energy efficiency and consequently reducing carbon footprints. The findings underscore the need for a multidisciplinary approach, collaboration among stakeholders, and the integration of best practices in the realization of zero-emission buildings should be applied at the very beginning of building process.
Permalink: https://doi.org/10.1007/978-3-031-69626-8_88

Abstract: In the current complexity of the design process, it is important to clarify the information flow for the creation of smart architectures. This clarity ensures coherence at each design scale, up to the definition of construction elements that make them effectively realizable and manageable throughout their entire life cycle. This paper presents a definition of sustainable metamodules using homogeneous sets of smartness indicators to support the design workflow. It specifically focuses on façades with a transparent base skin that can be digitalized through a BIM-based approach, promoting interoperability and multidisciplinary control. In analyzing present societal demands and the features of smart architectures, a range of smartness indicators will be established as parameters that contemporary architecture should consider to be classified as smart. The information cores formed by the combination of the predetermined parameters and feasible design solutions will be stated at every level of design detail. Following BIM regulations and leveraging the customization capabilities of open-format files (IFC), the earlier identified metamodules will be stated at various Levels of Detail (LOD), crafting suitable property sets within a BIM authoring software. This process will generate a structured matrix capable of supporting designers’ decisions in the realization of smart architectures.
Permalink: https://doi.org/10.1007/978-3-031-69626-8_86

Abstract: The present study investigates experimental cooling from the ceiling using phase change materials (PCMs) in Yazd, a city characterized by a hot and dry climate. A one-fourth scale model of a real room, measuring 4 m × 3 m × 3 m, was employed for the analysis. To evaluate system performance and the impact of PCM on energy consumption reduction, three configurations were considered: a simple PCM system, a PCM system with a fan (PCM-F), and a PCM system with a mini cooler (PCM-C). Additionally, to assess the influence of window configurations on ventilation, temperature, and comfort conditions within the model, three scenarios were examined: two open windows, one open window, and no windows. The economic analysis compared the two systems with the fan and mini cooler against a full mechanical cooling system without PCMs. Furthermore, CO₂ emissions and environmental impacts associated with the systems were also evaluated. The results indicate that the presence of PCMs in the ceiling, due to heat absorption during phase change, leads to a temperature reduction of 5 to 10 °C in the ceiling and a 3.2 °C reduction in the average room temperature compared to the scenario without PCMs. The findings demonstrate that ceiling cooling with PCMs significantly contributes to energy consumption reduction during peak hours of cooling demand. Specifically, the PCM-F system results in a 92% reduction, and the PCM-C system leads to a 71% reduction in total cost compared to the reference mechanical cooling system. Additionally, the PCM-F system achieves approximately a 36% reduction, and the PCM-C system results in a 34% reduction in environmental impact relative to the reference full mechanical cooling system.
Permalink: https://doi.org/10.3390/buildings15020198

Abstract: The offshore wind (OW) energy plays a crucial role in the transition to a clean energy future aligned with the European Union’s Green Deal and Net Zero 2050 strategy and Norway’s commitment and contribution to achieving the climate goals. According to the International Energy Agency (IEA), the building- and construction sector represented 39% of energy and process-related CO₂ emissions in 2018—of which 11% was related to steel and concrete. This work aims to assess the environmental impact of floating offshore wind turbine (FOWT) structures, comparing steel and concrete hulls through life cycle assessment (LCA). While OW offers a low-carbon alternative to fossil fuels, the manufacturing and transportation of FOWT structures contribute to greenhouse gas (GHG) emissions. We address the knowledge gap in LCA studies for FOWT structures by comparing two scenario objectives, (1) Steel hull, produced in China and shipped to Norway and and (2) Concrete hull, produced and installed near the Norwegian deployment site. We performed a literature review, and LCA study, to find CO₂-equivalent emissions per ton, by breaking down the calculations in 30 FOWT units in steel versus concrete, by using material data to perform an assessment resulting in the Global Warming Potential (GWP) stated in tons of CO2e/T. The study reveals the importance of material selection and local production in reducing the environmental impact of FOWT structures, and concrete hulls exhibit significantly lower carbon footprint compared to steel hulls, with calculated emissions of 0.404 tons CO₂-equivalent (CO2e) per ton of concrete and 2.76 tons CO2e per ton of steel. The study encourages further research in this area, highlighting the need for transparent data on the embodied carbon of materials and the potential benefits of incorporating recycled materials. By choosing FOWT structures with lower carbon footprints, decision-makers in the OW industry can contribute significantly to achieving the UN SDGs.
Permalink: https://doi.org/10.1007/978-3-031-69626-8_144