Typical Building Research Articles

Forecasting demand for air conditioning in the Brazilian residential sector

Brazil is among the ten largest air-conditioning consumers in the world. The estimated electricity demand for air conditioners in the residential sector has more than tripled in the last twelve years, while household air conditioner ownership increased by 9 % per year. This trend is expected to continue in the upcoming decades as a result of population growth rate, higher incomes, and individual preferences for thermal comfort. This work assess the current use of air conditioning in the Brazilian residential sector and attempts to forecast its future demand. Forecasting is based on a proposed methodology which includes weather data monitored by 265 meteorological stations during eight years. Empirical correlations are used to determine daily average indoor temperatures for typical residential buildings and the ANSI/ASHRAE standard for indoor thermal comfort is applied to estimate the average number of cooling days (CD) and heating days (HD). These two parameters indicate the yearly average number of days in which thermal comfort condition is not reached and consequently the use of air conditioning is necessary. Northern and Northeastern Brazil are the regions with the highest CD for masonry residential buildings, approximately 318 days annually. Efforts are made to project the electricity consumption by space cooling in the residential sector until 2035 considering several effects, such as population growth, ownership of ACs, cooling capacity, and equipment efficiency. Southeastern Brazil had the highest electricity consumption for space cooling in 2022, at 12.06 TWh. The research forecasts a rise to 44.77 TWh by 2035, though improved equipment efficiency could lower this to 33.51 TWh. Future energy policies should emphasize efficient AC usage and stricter EER standards.

Large-scale energy cost optimization and performance analysis for dedicated outdoor air system: simulation results from ASHRAE RP-1865

This is the first journal article from the ASHRAE research project RP 1865, “Optimizing Supply Air Temperature Control for Dedicated Outdoor Air Systems,” focusing on the large-scale energy performance evaluation of the optimal control of dedicated outdoor air systems (DOAS). DOAS is a type of air handling system that is installed in buildings to ensure proper ventilation for the built environment. It is acknowledged that DOAS with proper operation strategies can provide sufficient ventilation and dehumidification while achieving energy efficiency. In this regard, there have been efforts to develop advanced control sequences (e.g., optimal control) for DOAS. Still, limited studies have demonstrated the effectiveness of such DOAS controls on a larger scale under varying climate zones with different DOAS configurations. To fill the research gap, this study aims to quantify the energy-saving potentials of applying an optimal supply air temperature (SAT) control strategy for DOAS considering diverse configurations of HVAC systems in various climate conditions across the United States. For this study, we first developed a total of 180 EnergyPlus models accommodating different types of buildings (i.e., detailed medium-sized office and primary school) and HVAC systems (i.e., DOAS types, DOAS sizes, and terminal units) under varying ASHRAE climate zones (i.e., 2A, 3B, 4A, 4C, 6A, and 6B). Next, genetic algorithm (GA)-based optimizations were conducted to determine the optimal DOAS SAT control that would minimize the energy cost of the entire HVAC system operation. Our study of comparing the optimal control to a typical rule-based control (i.e., outdoor air temperature-based reset control) revealed approximately 4–34% of energy cost saving potential by optimizing the DOAS SAT control sequence.

On the Necessity for Improving Water Efficiency in Commercial Buildings: A Green Design Approach in Hot Humid Climates

Water, a fundamental and indispensable resource necessary for the survival of living beings, has become a pressing issue in numerous regions worldwide due to scarcity. Urban areas, where the majority of the global population resides, witness a substantial consumption of blue water, particularly in commercial buildings. This study investigates the potential for enhancing water efficiency within an ongoing high-rise office building construction situated in a tropical climate. The investigation utilizes the green building guidelines of leadership in energy and environmental design (LEED) through a case-study-based research approach. Strategies included using efficient plumbing fixtures (such as high air–water ratio fixtures and dual-flush toilets), the selection of native plants, implementing a suitable irrigation system, introducing a rainwater harvesting system (RWHS) and improving the mechanical ventilation and air conditioning (MVAC) system. The results showed a 55% reduction in water use from efficient fixtures, a 93% reduction in landscaping water needs and a 73% overall water efficiency with a RWHS from the baseline design. Additionally, efficient cooling towers and the redirection of condensed water into the cooling tower make-up water tank improved the overall water efficiency to 38%, accounting for the water requirements of the MVAC system. The findings of this study can contribute to more sustainable and water-efficient urban development, particularly in regions facing water scarcity challenges. The significance of these findings lies in their potential to establish industry standards and inform policymakers in the building sector. They offer valuable insights for implementing effective strategies aimed at reducing blue water consumption across different building types.

A Framework for Adaptive Façade Optimization Design Based on Building Envelope Performance Characteristics

The adaptive façades serve as the interface between the indoor and outdoor energy of the building. Adaptive façade optimization design can improve daylighting performance, the thermal environment, view performance, and solar energy utilization efficiency, thus reducing building energy consumption. However, traditional design frameworks often neglect the influence of building envelope performance characteristics on adaptive façade optimization design. This paper aims to reveal the potential functional relationship between building façade performance characteristics and adaptive façade design. It proposes an adaptive façade optimization design framework based on building envelope performance characteristics. The method was then applied to a typical office building in northern China. This framework utilizes a K-means clustering algorithm to analyze building envelope performance characteristics, establish a link to adaptive façade design, and use the optimization algorithm and machine learning to make multi-objective optimization predictions. Finally, Pearson’s correlation analysis and visual decision tools were employed to explore the optimization potential of adaptive façades concerning indoor daylighting performance, view performance, and solar energy utilization. The results showed that the optimized adaptive façade design enhances useful daylight illuminance (UDI) by 0.52%, quality of view (QV) by 5.36%, and beneficial solar radiation energy (BSR) by 14.93% compared to traditional blinds. In addition, each office unit can generate 309.94 KWh of photovoltaic power per year using photovoltaic shading systems. The framework provides new perspectives and methods for adaptive façade optimization design, which helps to achieve multiple performance objectives for buildings.

Data-driven analysis of heating and cooling base temperatures for buildings: Case studies in South Korea

To investigate statistical differences in the heating (Tbh) and cooling base temperatures (Tbc) across various building types, vintages, and sizes, we conducted case studies in Seoul, South Korea, based on data from more than 10,000 buildings. To assess the relative impacts of internal and external loads on Tbh and Tbc, we used a core-to-perimeter zone ratio approximated by the average floor area, defined as the floor area per building floor. Employing the energy signature method, change-point regression, and calendarization, we determined the best-fit change-point model and the corresponding Tbh and Tbc for each building. Our findings revealed statistically significant differences in Tbhs and Tbcs, with variations of up to 6 and 4 °C, respectively, across six building types. Notably, for most building types, Tbh and Tbc decreased as the average floor area increased, possibly influenced by a higher core zone to total area ratio. This highlights the predominant impact of internal loads over external loads. The influence of the average floor area on Tbh and Tbc outweighed that of building vintage, particularly for lodging, medical, and sales facilities, where Tbh and Tbc decreased in the more recent vintage buildings. However, this influence was less pronounced for facilities for senior citizens and children and education and research facilities, possibly due to factors such as diverse building shapes and the thermal preferences of occupants. Although this study focuses on South Korea, our data-driven approach provides statistical evidence for refining base temperature and improving traditional degree-day methods. Our findings suggest the need to establish base temperatures that consider various building types, vintages, and average floor areas, especially for large metropolitan cities where these factors vary widely.

Seismic amplification of Late Quaternary paleovalley systems: 2D seismic response analysis of the Pescara paleovalley (Central Italy)

Robust site characterization and ground response analysis require a thorough understanding of subsurface features, including geophysical properties and geometries of sediment bodies. Late Quaternary paleovalley systems, often overlooked in seismic hazard assessments, represent a potential threat due to their unconsolidated infill (with shear wave velocities <200 m/s) and sharp contrast with the adjacent substrate. Through an integrated approach that combined geophysical and stratigraphic data, we characterized the subsurface of the Pescara paleovalley system. Geostatistical interpolation of microtremor measurements enabled mapping resonance frequencies, highlighting abrupt changes and delineating the paleovalley boundaries. High-resolution core descriptions were then correlated with resonance frequencies, enabling the reconstruction of a 3D geophysical depth model of the buried paleovalley morphology. Furthermore, analyzing velocity profiles from down-hole tests led to the identification of five main seismic/stratigraphic layers within the valley fill. The geometry and facies architecture were reconstructed through a cross-section transversal to the paleovalley axis and then implemented into a 2D finite element model. Seismic response was computed, revealing significant amplification factors at frequencies closely matching the direct observations. Amplification factors peaked at frequencies between 0.9 and 1.1 Hz in the paleovalley center and up to 5.5 Hz towards the flanks, reaching a factor of 4.6. These findings suggest a notable increase in amplification amplitude compared to simpler geological contexts and emphasize the potential impact on common building types. Response spectra show strong amplifications in the paleovalley system, potentially leading to an underestimation of spectral accelerations compared to NTC18 guidelines. The comparisons of 1D and 2D modeling approaches revealed minimal differences, indicating that the generally flat geometry of the valley may not exhibit clear 2D effects. However, local subsurface stratigraphy strongly influences lateral changes in seismic response, emphasizing the importance of detailed subsurface knowledge for realistic seismic response estimates.

Path to Net Zero: Understanding the Building Energy Efficiency in Different Climates across Various Building Types

This study analyses the determinants of building energy efficiency in different climate zones and user types. The energy consumption of buildings in different climate zones can be affected by well-known determinants in different ways. So do the buildings with different user types. The primary aim of this study is to investigate how building energy efficiency is determined in five major climate zones and four main property types. This study uses the global building data (Points Achieved dataset) from the Leadership in Energy and Environmental Design (LEED) rating system 2010 to conduct three cross-sectional tests with logit regression models. The results confirm that the determinants of building energy efficiency are the location of the building, adoption of Building Energy Codes (BECs), climate zones, building types, regional economic development level (namely Gross National Income—GNI, Purchasing Power Parity—PPP) and population density. However, the impact of the determinants varies considerably in different climate zones or for different building types. This is the first empirical study exploring building energy efficiency and how it is determined in different climate conditions and user types. The findings are helpful for the stakeholders, such as policymakers, developers, and local authorities, when they hope to implement measures to improve building energy efficiency and the policy/regulation to boost it. Each building requires specific measures that suit its different climate zones or building types to enhance energy efficiency.

Advanced co-simulation framework for assessing the interplay between occupant behaviors and demand flexibility in commercial buildings

With buildings contributing significantly to electricity usage, enabling demand flexibility becomes a challenge, especially when accounting for occupant comfort. This study introduces an innovative co-simulation framework integrating multiple models: heating, ventilation, and air conditioning (HVAC) system, building zone load, indoor airflow, supervisory control, and occupant comfort and behavior. Uniquely, this framework allows for a comprehensive and dynamic analysis of building systems and occupant interactions in demand response events. Using this framework, we conducted a case study using a typical small office building model. Specifically, we focused on three areas: (1) the impact of indoor airflow modeling on energy use, occupant comfort, and behaviors forecasting, (2) the impact of occupant behaviors on demand flexibility, and (3) occupant comfort and behaviors under demand response events. Key performance indicators such as energy use, flexibility factor, durations of occupant discomfort and occupant behaviors were analyzed. Our findings indicated variations in energy usage and occupant comfort within demand flexibility events, marked by uncertainty boundaries, with variability in demand shedding up to 57.9%. We concluded that this framework is suitable for analyzing typical commercial buildings and their HVAC systems in terms of demand flexibility potential under the impact of occupant behaviors.

UB-FineNet: Urban building fine-grained classification network for open-access satellite images

Fine classification of city-scale buildings using satellite imagery is a crucial research area with significant implications for urban planning, infrastructure development, and population distribution analysis. However, the task faces great challenges due to low-resolution overhead images acquired from high-altitude space-borne platforms and the long-tailed sample distribution of fine-grained urban building categories, leading to a severe class imbalance problem. To address these issues, we propose a deep network approach to the fine-grained classification of urban buildings using open-access satellite images. A Denoising Diffusion Probabilistic Model (DDPM) based super-resolution method is first introduced to enhance the spatial resolution of satellite images, which benefits from domain-adaptive knowledge distillation. Then, a new fine-grained classification network with Category Information Balancing Module (CIBM) and Contrastive Supervision (CS) technique is proposed to mitigate the problem of class imbalance and improve the classification robustness and accuracy. Experiments on Hong Kong data set with 11 distinct building types revealed promising classification results with a mean Top-1 accuracy of 60.45%, which is on par with street-view image based approaches. A comprehensive ablation study demonstrates that the CIBM and CS modules improve Top-1 accuracy by 2.6% and 3.5%, respectively, over the baseline approach. In addition, these modules can be easily integrated into other classification networks, achieving similar performance improvements. This research advances urban analysis by providing an effective solution for detailed classification of buildings in complex mega-city environments using only open-access satellite imagery. The proposed technique can serve as a valuable tool for urban planners, aiding in the understanding of economic, industrial, and population distribution within cities and regions, ultimately facilitating informed decision-making in urban development and infrastructure planning. Data and code will be publicly available at https://github.com/ZhiyiHe1997/UB-FineNet.

Stakeholder-driven development of a decision support framework targeting sustainable water supply systems

The energy and water demand are expected to increase as a consequence of industrial development. The water streams in a typical building could however be used in a more energy and water efficient way to mitigate these challenges. Unfortunately, there are limited ways to evaluate and compare these system solutions. Thus, this study aims to identify the potential of different water streams within a typical building, their energy and volumetric potential and the areas of interest that can serve as a basis for a framework used for the evaluation of a system solution. A literature study will be performed to identify possible aspects of interest and to identify prevailing water stream within a building. Semi-structured interviews will also be conducted with stakeholders operating within the field which will work as a basis to develop the framework of areas and parameters of interest. Eight water streams were identified within a building where the stream with the largest volume was cold water, greywater and blackwater and the streams containing the largest energy content where cold water, rainwater and condensate. The interview produced several issues where, after processing, three different general aspects and 10 areas of interest were identified. Rainwater has shown to have the potential to work as an energy source for evaporative cooling due to the volume and high quality. By separating the sensible energy from the grey water and utilizing both, both resources enable proper use for higher acceptance amongst tenants.

Challenges of Integrating ICD 11 into Automatic Alerting Systems.

Automatic alerting systems (AASs) can identify adverse health events but emergency communication relies on human operators and natural languages. For complete automation, we need to code the diversity of adverse events in a granularity that supports optimal dispatches. Hence, AAs shall integrate with the International Classification of Diseases (ICD). The ICD-11 coding system includes chapters for external causes of injury. However, ICD-11 supports coding injury incidents in electronic health records (EHRs) after they have occurred, while disregarding integrating real-time injury reporting within its framework. We explore the potential challenges associated with integrating ICD-11 into AAS by analyzing external causes of morbidity or mortality and the dimensions of external causes as potential areas of integration. We recognize the themes: (i) incident of injury, (ii) mode of transport, (iii) indoor location, (iv) outdoor location, and (v) type of building, and identify four challenges: (i) conceptual differences between the two systems, (ii) injury identification, (iii) presence of entities below the shoreline in ICD-11, and (iv) lack of specificity in certain ICD-11 codes related to AASs. For easy integration of ICD-11 into AASs, we recommend an AAS data dictionary and propose ICD-11 updates related to external causes of injury.

Generative design for complex floorplans in high-rise residential buildings: A Monte Carlo tree search-based self-organizing multi-agent system (MCTS-MAS) solution

This research aims to develop an innovative generative design algorithm for complex floorplan layout designs in high-rise residential buildings. Unlike conventional methods, which treat such a task as an optimization problem, we approach it as an adaption problem to be solved by combining a self-organized multi-agent system (MAS) and Monte Carlo tree search (MCTS) algorithm. The approach involves two steps. The first is design constraints pre-processing, which comprises (a) initial user input, and (b) pre-processing for initial setup. The second is building plan generation, involving (a) perception, (b) planning, and (c) action modules. Multiple illustrative examples are explored to verify the proposed generative design algorithm in terms of robustness, efficiency, and scale. We discover that the algorithm can handle complex combinations of input constraints (e.g., number of rooms, room areas, adjacency connections, and building boundaries) and generate plausible floorplans in a robust, efficient way that can be scaled up to bigger, more complex floorplan generative design problems. With proper adaptation, the algorithm can potentially be applied to other building types, such as office or hospital buildings.

Impact of building topologies on hill slope stability in Aizawl city

In hilly regions like Aizawl city, wherein houses are mostly built on slopes, the layout of building plays a crucial role in slope stability. Building failures in Aizawl often results from a) non-engineered alterations of slopes b) faulty foundation construction without soil investigation or design. Many residents build and reside on slopes without considering the soil characteristics. To address this, a study was conducted to examine the impact of two common building types - step-back buildings and step-back setback buildings on slope stability in Aizawl. Fifty different sites with varying slope geometries were analysed using Limit Equilibrium Method (LEM), considering soil parameters like cohesion, internal friction angle, unit weight, and slope geometry. The study evaluated slope stability under conditions such as free slope, slope with building loads under static and seismic conditions. The study found that sites with lower slope angles generally demonstrated greater stability, but there were cases where steeper slopes showed better stability due to superior shear strength values. Sites with higher cohesion values exhibited better stability in free slope analysis, while those with higher internal friction angles showed better stability under building loads of both static and seismic condition. To validate the above findings, a sensitivity analysis along with a comparative analysis between step-back and step-back setback building types were conducted. The results indicated that step-back setback buildings offered better stability compared to step-back buildings alone. This suggests that implementing step-back setback buildings could significantly improve slope stability in cities like Aizawl.

Research on the yingzao chi restoration of palace buildings sans dougong in Ming and Qing Dynasties: a case study of Chongqing

As a unit of measurement for Chinese ancient construction, the yingzao chi (yingzaochi 营造尺) is of great significance to study long-term transformation of culture, construction skills, and the scale design rule of Chinese regional architectural heritage. But few scholars study the restoration method of the yingzao chi of palace buildings sans dougong (斗拱) in Chinese Ming and Qing Dynasties. Based on the yingzao chi theory, this paper studies and improves a theoretical method to help restore the yingzao chi of palace buildings sans dougong in Ming and Qing Dynasties. This method is not limited by the lack of reference of vernacular chi (xiangchi 乡尺) and can restore the yingzao chi of a single building through surveying data, and there is a smaller error in the calculation process. Three typical palace buildings without dougong in Ming and Qing Dynasties in Chongqing are selected as examples. The results show that the restored yingzao chi of these three buildings is highly similar, most likely 320.7–323.6 mm, which can verify the reliability of this method. This study can help to improve the research of yingzao chi and provide theoretical support for the protection of this kind of architectural heritage. Besides, the restoration of the yingzao chi provides a potential opportunity to explore how the technological and cultural of palace architecture spread, develop and blend.

A novel multi-objective generative design approach for sustainable building using multi-task learning (ANN) integration

Building Performance Optimization (BPO) plays a pivotal role in enhancing building performance, guaranteeing comfort while reducing resource consumption. Existing performance-driven generative design is computational demanding and difficult to be generalized to other similar buildings with difficult to be generalized to other building types or climate conditions. To fill this gap, this paper introduces a novel framework, which integrates multitask learning (MTL), code compliance check, and multi-objective optimization through NSGA-III algorithm. This framework is able to identify Paratoo Optimal design solutions, which comply with building codes, at low computation costs. The framework begins with selecting key design variables that are critical to building energy, comfort performance and life cycle cost. It then employs MTL to enhance the model's accuracy while reducing computational costs. Next, we designed a code compliance checking module followed by the NSGA-III optimization process, with the objective of identifying solutions that comply with existing building codes. The results indicate that the proposed MTL network achieved an R2 score of 0.983–0.993 on the test set. In the particular case study where equal weights are preferred, this approach yielded noteworthy reductions of 27.65%, 19.55%, and 31.13% in Building Energy Consumption (BEC), Life Cycle Cost (LCC), and Residue of continuous Daylight Autonomy (RcDA), respectively, for a rural dwelling, and exclude solutions that fail to satisfy regulatory standards. This framework allows designer to input the weights of each objective based on their preference and can be applied to other building types and climate regions. Last, we develop a solution selection tool based on the results output by the framework we proposed, which can be found at https://github.com/LiMingchen159/Village-House-Design-Strategy-in-Hebei-Province-China.

Energy Consumption and Outdoor Thermal Comfort Characteristics in High-Density Urban Areas Based on Local Climate Zone—A Case Study of Changsha, China

This study aims to investigate the characteristics of energy consumption and outdoor thermal comfort within the high-density urban fabric of Changsha. Two different types of building (residential and office), as well as three building forms (point, slab, and enclosed) were analyzed under the local climate zone scheme. Utilizing the ENVI-met 5.6.1 and EnergyPlus 23.2.0 software, simulations were conducted to assess the thermal comfort and energy consumption of 144 architectural models. Then, multiple regression and spatial regression were applied to predict the energy consumption characteristics of the study area. The results showed the following: (1) In the high-density urban area of Changsha, the central business district and historic old town adjacent to the Xiangjiang River are identified as areas with high energy use intensity. (2) Among the residential categories, the point-types LCZ-3 and LCZ-6, as well as the slab-type LCZ-4, exhibit the lowest energy use intensity. In contrast, the enclosed office buildings, LCZ-2 and LCZ-5, are characterized by the highest energy use intensity. (3) Urban form parameters such as floor area ratio and building shape coefficient have a significant impact on EUIwinter, while EUIsummer is highly related to the normalized difference vegetation index and building shape coefficient (BSC). (4) The slab-type LCZ-4 stands out with its notably lower cooling and heating energy use intensity, coupled with excellent thermal comfort, making it particularly well-suited for the climatic conditions of Changsha.

Optimizing Room Temperature Control: A Comparative Analysis of Deep Reinforcement Learning and Contextual Bayesian Optimization Methods

Building temperature control presents significant modeling challenges due to the complexity of internal systems and varied usage patterns. Consequently, black box modeling has become a prevalent approach in this domain. This technique broadly encompasses deep learning and Bayesian optimization methods, each offering unique mechanisms for deriving optimal control parameters. Deep learning methods iteratively refine control parameters using extensive datasets, whereas Bayesian optimization employs Gaussian process regression and acquisition functions to pinpoint optimal solutions. This paper conducts a comparative analysis of two innovative methods within these branches: the application of Deep Reinforcement Learning (DRL) for integrated control of household temperature and electric vehicle charging, and the Primal-Dual Contextual Bayesian Optimization (PDCBO) method for adaptive thermal control in buildings. The study reveals that the DRL approach, integrated with electric vehicle charging systems, excels in responsiveness and reducing energy consumption. In contrast, the PDCBO method enhances control by allowing for the artificial definition and continuous optimization of constraints on energy usage and comfort during the operation, facilitated by algorithmic design. By synthesizing the strengths of both methods and considering electric vehicle integration and tailored adaptive control constraints, enhanced outcomes are achievable. This paper proposes that specific control strategies be developed for different building types to address diverse real-world requirements effectively.

Assessing the 3E performance of multiple energy supply scenarios based on photovoltaic, wind turbine, battery and hydrogen systems

Supplying buildings with cost-effective, eco-friendly and reliable energy by hybridizing sustainable renewable energy systems (RESs) and energy storage methods is becoming increasingly interesting. This study investigates the energy, environmental and economic (3E) effectiveness of three energy supply scenarios (ESSs) designed to meet the electricity and thermal needs of a typical residential building under the Mediterranean climate of Morocco. The first scenario aims to directly supply the energy produced by two RESs, namely photovoltaic and wind systems, to the building and inject the surplus electricity into the grid. In contrast, in the second scenario, the excess solar and wind energy is stored in batteries, while in the third one a proton exchange membrane electrolyzer is used to produce hydrogen and store it for later use through fuel cells. To examine the performance of these scenarios, numerous software, such as EnergyPlus and PVsyst, as well as numerical models are used. The findings indicate that the second and third scenarios can achieve an energy coverage ratio of up to 100 %, whilst the first scenario reaches lower ratio of 52.56 %. Environmentally, the first scenario exhibits the best outcomes with a carbon payback period of one year and a reduction in carbon emissions of 18.21 tCO2e/year. On the other side, the carbon payback period and carbon emissions reduction are 1.66 years and 15.13 tCO2e/year for the second scenario, and 1.14 years and 14.82 tCO2e/year for the third scenario. Economically, the first scenario reveals the best economic findings, followed by the second one, while the third scenario is not economically feasible. The obtained simple payback period values for the first, second and third scenarios are 5.80, 7.89 and 16.30 years, whereas their levelized cost of energy shares are 0.05, 0.09 and 0.21 $/kWh, sequentially. The outcomes of this research significantly contribute to the field by providing crucial information for building owners and professionals to help them in discerning the most efficient ESSs in the 3E prospects, and hence improving their decision-making and implementation processes.

Policy implications of challenges and opportunities for district heating – The case for a Nordic heating system

District heating (DH) is an efficient urban heating solution. Numerous external factors (energy efficiency, fuel-price increases, heat -pump competition and policy changes) are challenging. It is relevant to explore long-term cost-competitiveness of DH. A model has been developed testing three, future DH pathways: (i) phase out of biomass (aligned to the cascading principle of the EU Taxonomy), (ii) including waste heat in the fuel supply mix (aligned to the updated Energy Efficiency Directive) and (iii) lower system temperatures (aligned to the idea of more renewables and waste heat).The model considers the long-term development of the supply and demand sides together and simultaneously. This allows the model to determine the most cost-efficient heating solution for different building types in different points in time.The results confirm that a phaseout of biomass would erode -but that developments rendering lower system temperatures and increased waste heat utilization would strengthen-the DH business case. Additionally, it is confirmed that heat pumps constitute the main competition to DH. Hence, to remain a cost-efficient heating alternative in the future, DH systems need to revisit the tradition of centralized heat supply resorting to combustion for decentralized heat supply resorting to heat supply on demand.

Hybrid forecasting model of building cooling load based on EMD-LSTM-Markov algorithm

Precise forecasting of the cooling load (CL) of buildings is crucial for the efficient functioning of central air conditioning systems. The study proposed a new hybrid model based on empirical modal decomposition (EMD) and Markov chain improved LSTM neural network, denoted as EMD-LSTM-Markov. First, Pearson’s correlation coefficient (PCC) and Gradient Boosting Decision Tree (GBDT) are used to extract features with a high degree as the input data. Then, the LSTM is employed to establish a prediction model. Finally, to verify the validity and prediction accuracy of the proposed model, this paper selects the CL data of an office building in Guangzhou as the research sample. The results show that the Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) of the EMD-LSTM-Markov model are 4.53 and 0.84%. Compared with the other three predicting models, the RMSE and MAPE values of EMD-LSTM-Markov are decreased by 40% − 94% and 70% − 96%, which has better performance in accuracy and stability. It testifies that the proposed strategy is reliable and effective. This work successfully resolves the issue of delayed cold response. The hybrid model proposed demonstrates significant energy-saving potential for accurately predicting building CLs and enhancing the optimal control of cooling systems. Future research should explore the hybrid model’s robustness across different building types and climatic regions.