Buildings In Cold Regions Research Articles

Research on the Operation Optimization of Public Building Systems in Extremely Cold Areas Based on Flexible Loads

The heating energy consumption in public buildings in cold regions is notably significant, presenting substantial scope for energy savings and emission reductions. Flexible loads can actively participate in controlling the operation of the power grid, improving the energy utilization and the economy of the system. This study introduces flexible loads into the operation optimization of energy systems, establishing mathematical models for flexible thermal and electrical loads. A two-stage operation optimization method is proposed: the first stage simulates the starting and stopping control conditions of equipment at varying temperatures and times, selecting the optimal time period to regulate the thermal loads; the second stage employs a multi-objective particle swarm optimization algorithm to optimize the scheduling of the system’s electrical load. Finally, an empirical analysis is carried out in a public building in Shenyang City as an example, and the results indicate that optimal scheduling of flexible thermal and electrical loads reduces the daily operating cost of the energy supply system by RMB 124.12 and decreases carbon emissions by 22.7%.

The influence of occupant control behaviour on thermal comfort under different heating charging modes in residential buildings in cold regions

In China's central heating, there are two modes for calculating heating costs, which are divided into Charging by flow mode which charges according to the amount of use and Charging by area mode which charges according to the floor area. The Charging by flow mode has been increasingly adopted by numerous urban central heating buildings. Thus it is worth investigating whether occupants experience varying levels of thermal comfort under these two modes. To address this, a field test and subjective questionnaire survey were conducted on residential buildings in cold regions of China during the heating season. The study assessed 134 residential occupants utilizing radiator heating, comprising 66 in Charging by area and 68 in Charging by flow modes. A collection of 1206 valid data points was obtained, with 609 in Charging by area mode and 597 in Charging by flow mode. The findings reveal noteworthy disparities in the duration, area, and strength of heating equipment usage between the two modes. While there are no marked variances in the interior and exterior environmental conditions under both modes, residents in the Charging by flow mode experience enhanced thermal comfort, acceptability and expectation, as well as better air quality satisfaction. Perceived control can greatly enhance individuals' thermal sensation in temperatures below 18 °C and above 24 °C. The impact of perceived control on thermal expectation is linear with temperature adjustments. The heightened degree of sensing control in Charging by flow mode lowers residents' expectations of high temperatures, broadens the range of acceptable low temperatures and accomplishes energy conservation and carbon reduction while ensuring optimal comfort.

The building application of foam concrete combined with PCM in cold regions and its energy efficiency

Building performance in cold regions greatly affects the quality of people living in the building. With the further development of the construction industry, how to improve the energy efficiency of buildings in cold regions has become one of the key issues. The study introduces paraffin phase change capsules as aggregate components on the basis of foam concrete, and then designs a matching energy efficiency analysis method to regulate the concrete parameters, and finally tests the performance of the research method and conducts an application analysis. The experimental results show that in performance simulation testing, the internal surface temperature decreases to 288.2 K at the minimum when the external temperature is 0°C to 4°C. It shows that the research method can effectively analyze the building performance, and the analysis method can optimize the performance of the foam concrete materials proposed in the research, and obtain building materials with better thermal insulation performance than the existing conventional materials. Research can provide certain technical references for building energy conservation in cold regions.

Research on the Design of Recessed Balconies in University Dormitories in Cold Regions Based on Multi-Objective Optimization

Thermal comfort and daylighting are vital components of dormitory environments. However, enhancing indoor lighting conditions may lead to increased annual energy consumption and decreased thermal comfort. Therefore, it is crucial to identify methods to reduce buildings’ energy costs while maintaining occupants’ thermal comfort and daylighting. Taking the dormitory building of Songyuan No. 2 at Shandong Jianzhu University of Architecture, which is located in a cold region, as an example, a field measurement analysis was conducted on the recessed balconies within the dormitory. The measured data were analyzed and utilized to simulate the annual energy consumption, thermal comfort predicted mean vote (PMV), and useful daylight illuminance (UDI) values of the dormitory units using the Grasshopper platform with the Ladybug and Honeybee plugins. The different depths of the balconies and window-to-wall ratios have a significant impact on the indoor physical environment and energy consumption, leading to the design of independent variables and the construction of a simplified parametric model. The simulation results underwent multi-objective optimization using genetic algorithm theory through the Octopus platform, resulting in a Pareto optimal solution set. Comparisons between the final-generation data and simulations of the original Song II dormitory unit indicate potential energy savings of up to 2.5%, with a 25% improvement in indoor thermal comfort satisfaction. Although there was no significant improvement in the UDI value, all the solution sets meet the minimum requirement of 300 lux specified by relevant regulations, according to the simulated average illuminance levels on the indoor work plane. Finally, the 60 optimal solution sets were further screened, filtering out sets deviating excessively from certain objectives, to identify 6 optimal solutions that are more balanced and exhibit a higher overall optimization rate. These findings offer detailed data references to assist in the design of dormitory buildings in cold regions.

Short-term energy consumption prediction for university dormitory buildings based on energy consumption grouping by considering meteorological factors and teaching schedules

Accurate short-term energy consumption prediction is essential for studying renewable energy utilization in building technologies. University dormitory energy consumption is affected by meteorological factors and teaching schedules, presenting a more complex time series compared to residential buildings, rendering traditional methods unsuitable. Therefore, in the present study, we developed a model called energy consumption grouping-Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise-Bi-directional Long Short-Term Memory (ECG-ICEEMDAN-BILSTM) for predicting short-term energy consumption in university dormitory buildings. Model was tested using electricity consumption data from university dormitory buildings in cold regions of China. Based on the energy consumption characteristics, we identified two consumption patterns comprising high power energy consumption (HPEC) and low power energy consumption (LPEC). Using the ECG method, data with similar daily energy consumption distributions for HPEC and LPEC were grouped. Subsequently, individual prediction models were constructed based on ICEEMDAN-BILSTM for each specific energy consumption group. The experimental results showed the ECG-ICEEMDAN-BILSTM model obtained more accurate predictions compared with other neural network models. In addition, the model was more effective at forecasting the complex distribution of the HPEC pattern, with reductions of at least 16.21% and 15.82% in MAPE and PRMSE, respectively. Thus, study still can obtain valuable insights to address the problem of short-term prediction using complex time series.

Research on optimization of the heating system in buildings in cold regions by energy-saving control

Building heating is an indispensable part of people's winter life in cold regions, but energy conservation and emission reduction should also be taken into account during the heating process. This paper provides a concise overview of the heating system based on air-source heat pump radiant floor and its control strategy. It also optimizes a control system based on thermal comfort and energy efficiency ratio, and analyzes a room in Xining City, Qinghai Province, to test the heating system performance under two control strategies. The final results show that under the traditional control strategy, the cumulative working time of the heating system within a day was 15 hours, the average indoor temperature was 17.36℃, the temperature standard deviation was 2.08℃, and the average power consumption was 189.6 kWh. Under the improved control strategy, the cumulative working time of the heating system within a day was reduced to 10 hours, the average indoor temperature was 18.56℃, the temperature standard deviation was 0.92℃, and the average power consumption was 132.5 kWh.

Multi-Stage Sensitivity Analysis of the Energy Demand for the Cooling of Grain Warehouses in Cold Regions of China

The early design parameters exert a considerable influence on the cooling energy demand of a granary building in operation. In order to investigate the impact of various parameters on energy use, a grain warehouse energy model was constructed using the Ladybug + Honeybee tools on the Grasshopper platform. Three global energy sensitivity methods were used to analyze the model, and the sizes of the influential parameters were determined and ranked. The study uncovered that the cooling energy demand of the grain warehouse was primarily influenced by factors such as the cooling set-point temperature, roof solar absorptance, roof and exterior wall insulation thickness, window type, and orientation. On this basis, a local sensitivity analysis was conducted for the highly sensitive parameters to identify their influence trend and optimal design range. The results showed that the cooling energy demand of the grain warehouse increases faster as the cooling set-point temperature decreases, with the highest growth rate occurring at a temperature below 18 °C. Lower solar absorptance of the roof is conducive to reducing the cooling energy demand of the grain warehouse. When the thickness of the roof thermal insulation is less than 120 mm and the thickness of the external wall thermal insulation is less than 60 mm, energy use decreases more quickly with greater insulation thickness. It is advisable to use traditional or new windows with thermal insulation and shuttered windows. Furthermore, the optimal position of the long side of the granary was between 10° west and 10° east of north. This research could provide guidance for the energy-saving design and renovation of granary buildings in cold regions of China.

An optimisation method for energy efficiency of residential buildings in cold regions based on genetic algorithm

An optimisation method for energy efficiency of residential buildings in cold regions based on genetic algorithm

Optimized design research on daylighting performance of cold land buildings based on improved neural network

Abstract This study delves into optimizing daylighting in buildings in cold regions, employing an innovative neural network approach to enhance natural lighting efficiency. Cold climates present unique challenges for daylighting, making it essential to improve indoor lighting conditions, reduce energy usage, and enhance occupant comfort. Traditional design methods fall short in optimizing daylighting due to their inability to effectively navigate complex environmental factors and building configurations. We introduce an advanced neural network model that pioneers efficiency and innovation in the daylighting design of cold buildings. This model leverages the GA-PSO-BP framework, integrating Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Back-Propagation (BP) neural networks to create a potent optimization tool. Our approach focuses on refining key design parameters such as building orientation, floor height, plan depth, and external window design. Notably, specific adjustments to building orientation and floor height significantly boost daylight autonomy (DA) and helpful daylight illuminance (UDI) while maintaining the daylight glare probability (DGP) within optimal limits. Our findings reveal that optimizing building orientation can elevate DA and DGP values by 4.756% and 0.037325, respectively. Similarly, adjustments to floor height can enhance DA, UDI, and DGP values to 51.833%, 51.278%, and 0.361377, respectively. This refined neural network model demonstrates a robust capability to improve daylighting performance in cold-region buildings, offering fresh perspectives and methodologies toward the sustainable evolution of architectural design.

Energy and exergy analysis of a novel dual-source heat pump system with integrated phase change energy storage

In order to improve the application of renewable energy in cold regions and overcome the drawback of the low performance of traditional air source heat pumps (ASHP) in a low temperature environment, a novel type of dual-source heat pump system is proposed, which includes a heat pump, photovoltaic–thermal (PVT) modules, an air heat exchanger, and phase-change energy storage equipment. In response to different outdoor environmental parameters, the heat pump can use the PVT, ambient air, or solidification latent heat of water in the ice tank as the heat source. This study focuses on analyzing the applicability of this novel system in residential buildings. Firstly, the thermodynamic performance of the system under typical operating conditions is experimentally analyzed, proving that the system can effectively utilize solar energy under low solar irradiation. Then, the operational strategy of the system is optimized with the optimization goal of system exergy efficiency. Finally, the continuous operation performance of the system in winter is simulated and analyzed. The results show that the utilization ratio of solar energy reaches 2/3 in winter. Compared with the traditional ASHP system, the energy-saving rate of the novel system is 73.6 %, which can reduce the carbon emissions by 69 %. These results provide technical guidance for the application of the novel system in residential buildings in cold regions.

Model predictive control for demand response in all-electric school buildings

This paper presents predictive control strategies for all-electric school buildings in cold regions to activate energy flexibility based on changes in electricity prices. A fully electric school building near Montreal, Canada, is used as a case study. This study investigates three scenarios: 1) Reference case with a proportional–integral controller and flat rate electricity price, 2) Model predictive control with flat rate electricity price, and 3) Model predictive control with dynamic electricity price. These scenarios are modelled using the resistance-capacitance thermal networks model, and energy performance is determined and compared over a typical heating season. The proposed approach takes into account the physical parameters of the building, weather predictions, and thermal comfort constraints to maintain optimal energy consumption. A building energy flexibility index is used to quantify the building energy flexibility with a focus on peak demand reduction when the electricity prices are higher than usual. The results show that the MPC strategy can reduce peak power demand by up to 100% and minimize the cost of electricity during demand response events while maintaining acceptable comfort conditions.

Improving building resilience in the face of future climate uncertainty: A comprehensive framework for enhancing building life cycle performance

This research introduces an innovative resilient design framework, addressing gaps in building performance optimization by considering a holistic life cycle perspective and factoring in climate projection uncertainties from the 2020s to 2090s. The study concludes that in the case study, future climate scenarios in different Shared Socio-Economic Paths significantly impact building life cycle performance, with wall U-value, windows U-value, and wall density identified as major factors affecting building performance under future climate uncertainty. The ensemble learning model achieves high fidelity in predicting life cycle carbon emissions (LCCE), life cycle cost (LCC), and indoor discomfort hours (IDH) through input feature screening and hyperparameter optimization. Comparing optimization algorithms, Two-Archive Evolutionary Algorithm for Constrained multi-objective optimization (C-TAEA) demonstrates better convergence degree and optimization effect. Among the tested multi-criteria decision making methods, the VIKOR method selects the best building resilient design scheme from the Pareto data set, effectively reducing LCCE, LCC, and IDH, and leading to an overall improvement in building life cycle performance. Applying this framework, the finalized scheme for an office building in cold region optimizes 44.0% of LCCE, 8.2% of LCC, and 4.3% of IDH. This framework develops a new approach to improve building life cycle performance under future climate uncertainty and supports informed decision-making for resilient building design. To promote carbon neutrality in construction, it is urged that future studies incorporate additional sources of uncertainty, such as occupant behavior, into the proposed framework.

Research on green building design methods in cold regions

In response to the growing severity of the energy crisis, buildings in cold regions are faced with many challenges due to their unique geographical limitations and characteristics. This paper focuses on the design method of Green Buildings in cold climate, aiming to solve the poor thermal insulation performance, high energy consumption and environmental pollution. Firstly, the definition and background of Green Building in cold areas are introduced. The practical application effect of Green Building design in cold regions is discussed from different angles, and the development trend and suggestions of Green Building design in cold regions are put forward. In addition, in order to ensure that these measures achieve the desired effect, there is also a need for a sound evaluation system to monitor and evaluate the sustainability and practical effects of Green Buildings. The paper concludes with a summary of the future trend of Green Building design development in cold areas and suggestions. The research findings in this paper are of great importance in promoting sustainable building development in cold regions.

Dynamic Simulation Analysis of Influencing Factors of Energy Consumption of Public Building Envelope in Central China

It difficult for buildings in cold regions to reasonably meet ultra-low energy consumption requirements. To explore the influencing factors of building energy consumption in this type of climatic region, this paper takes a typical public building in Zhengzhou, China as a case study to dynamically simulate an entire building. The influence of the window glass, window-to-wall ratio (WWR), thermal insulation material thickness and other factors on building energy consumption is considered. The sensitivity of envelope design factors to energy consumption is analyzed by the orthogonal test. The results indicate that the effect of changing the technical parameters of the external envelope structure on building energy efficiency is significant. Moreover, range analysis is used to determine the primary and secondary factors with regard to their influence on the energy consumption of the building. The WWR, the type of external window glass and the thickness of the external wall insulation layer should be considered first in energy-saving design; the energy-saving potential of the roof insulation thickness is not as significant as these factors. The results of the study can provide a reference for the energy-saving design of public buildings in Central China.

Analysis of the Influence of Office Building Operating Characteristics on Carbon Emissions in Cold Regions

Reducing buildings’ operational carbon dioxide emissions has become a crucial element in China’s efforts to achieve carbon peak and carbon neutrality targets. This study focus on the influence of office building operating characteristics on carbon emissions in cold regions. By utilizing DesignBuilder v7.0.0.096 to conduct numerical simulations of 10 different operating conditions for heating, ventilation and air-conditioning (HVAC) and lighting systems, this study solves the problems in the past of poor comfort and high energy consumption with manual management and achieves a win-win situation for health and environmental protection. The study shows that by implementing a mixed mode of mechanical ventilation and natural ventilation based on outdoor climate conditions and design requirements, unsatisfied hours can be reduced by 202 h compared to the traditional air condition heating operation mode for both winter and summer seasons. Furthermore, compared to a year-round HVAC operation mode, the air-conditioning energy consumption can be reduced by 19%, resulting in a carbon emissions reduction of 1.45 kg CO2/(m2·a). Additionally, for every 2 °C increase in the outdoor temperature, the cooling energy consumption decreases by 2–5%. In terms of lighting, the intelligent lighting mode can reduce energy consumption by 31.04%, leading to a carbon emissions reduction of 3.04 kg CO2/(m2·a). The coupling operation characteristics of mixed mode, intelligent lighting, and energy-saving lamps can achieve a maximum saving of 83.46 MWh of electricity and approximately CNY 72,000 every year, with a static payback period of approximately 2.7 years. This operational strategy, which fully considers the utilization of natural ventilation and daylighting in conjunction with traditional design approaches, improves indoor air quality and ventilation conditions, while also maximizing the energy-saving and carbon reduction potential. The study results provide valuable design and operational guidance for new and existing office buildings in cold regions, to effectively reduce carbon emissions, while offering significant investment returns.

Energy-Saving and Ecological Renovation of Existing Urban Buildings in Severe Cold Areas: A Case Study

High-rise buildings in cold regions have a requirement of ecological improvement due to the continuous response to climate change throughout the year. This study evaluates wind environment, light environment, thermal environment, and energy consumption environment using Phoenics, Ecotect, and DesignBuilder tools, utilizing a high-rise residential building in an intensely cold place as an example. With the goal of repairing the buildings, green energy-saving measures are applied from the perspectives of form, structure, system, and equipment strategy. The energy-saving rates and carbon dioxide emission reduction rates of the renovated buildings were predicted. The results reveal that, in the building performance diagnostic, the wind speed clearly rise at the building’s corner, particularly on the outdoor level and the top floor; meanwhile, the inside lighting is insufficient, and there is a glare hazard adjacent to the window. The performance of the target building has unquestionably increased following the repair of 12 measures, including the bay windows, exterior walls, and solar energy. The influence of strong winds in winter and tranquil winds in summer greatly decreased in terms of the wind environment. In the light environment, indoor lighting is more uniform; the range of (Universal Design index) UDI100–2000 increased from 9.2% to 32.7%; and UDI2000, which may cause glare, decreased by 28.4%. Energy savings and pollution reduction rates were as high as 19.8% and 38.8%, respectively, due to the installation of solar photovoltaic panels. Based on all the measures, the overall energy saving rate of the target building was 63.8%, and the CO2 emission reduction rate was 90.3%.

Multi-objective Evolutionary Algorithm Supported Construction of Architectural Window Design Model Based on Visual Comfort - A Case Study of Office Buildings in Cold Regions

Abstract With the accelerated advancement of big data Internet development, people gradually realize the urgency and importance of energy saving, so the multi-objective optimization research for the performance of office buildings in cold regions is significant, and this paper focuses on the construction of a building window design model based on visual comfort supported by multi-objective evolutionary algorithm- taking office buildings in cold regions as an example, firstly, by reviewing the literature to understand the principle, algorithm, and concept of multi-objective evolution, construct the building window design model based on visual comfort according to the objective function and extract the standard model of inner corridor slab space building in office buildings in cold regions of Harbin and Shenyang. The optimized design of building window parameters was carried out using the established joint simulation and optimization work platform. The results show that for Harbin, as the window heat transfer coefficient increases, the heating energy consumption increases, the cooling energy consumption increases more slowly, and the total energy consumption increases linearly. An increase in window heat transfer coefficient by 0.1W/ (m 2 ∙K) Increases are cooling energy consumption by 0.18%, heating energy consumption by 0.78%, and total energy consumption by 0.46%. For Shenyang, as the window heat transfer coefficient increases, the heating energy consumption increases, the cooling energy consumption increases more slowly, and the total energy consumption increases linearly. If the heat transfer coefficient of the window increases by 0.1W/ (m 2 ∙K), cooling energy consumption increases by 0.18%, heating energy consumption increases by 0.78%, and total energy consumption increases by 0.46%. This study provides a theoretical basis for extracting standard models for other building types while making the results more generalizable and improving the efficiency of sustainable office building design.

Association between Wind Environment and Spatial Characteristics of High-Rise Residential Buildings in Cold Regions through Field Measurements in Xi’an

Research on the correlation between wind and block spatial form focuses mainly on hot and humid cities. However, cold regions are also experiencing high summer temperatures due to global climate change. Enhancing wind speed in blocks through urban spatial control improves comfort. Existing research cannot be directly applied to cold regions due to natural differences. Using Xi’an as an example, this study explores the impact of high-rise residential block spatial form on internal and external wind environments through field measurements and simulations. Optimal strategies for block planning and architectural design are identified to improve the wind environment. Results show that blocks with high buildings on the south and north sides and low buildings in the middle achieve a more comfortable internal wind environment. Gradually increasing building height from south to north has minimal impact on downwind blocks. Reducing the angle between the main facade and dominant wind direction enhances the residential area’s wind environment. Specific spatial planning and design strategies are summarized for early-stage decision-making.

Investigating the Energy-Saving Effectiveness of Envelope Retrofits and Photovoltaic Systems: A Case Study of a Hotel in Urumqi

Many of China’s older buildings, built in the 1970s and 1980s, faces several indoor temperature and heating energy consumption problems. Based on an investigation and analysis of an old hotel in Urumqi, this paper proposes a renovation plan to improve the indoor temperature and reduce the heating energy consumption, thereby introduce a sustainable development strategy for the winter. The effects of different renovation plans on the hotel were simulated using DesignBuilder and PVsyst software. The results show that improving the insulation performance of the building envelope, including reforming the roof, exterior wall, doors, and windows and adjusting the window-to-wall ratio, is the primary way to improve indoor temperature and reduce heating energy consumption in winter. If economic conditions allow, we can also install photovoltaic systems on the roof to save a significant amount of energy. This paper puts forth specific reconstruction plans for old buildings in cold regions as well as some possible reconstruction paths for other structures according to the local economic development level to provide a reference for future related studies.

Smart Building Skin Design with Dynamic Climate Adaptability of Smart Cities Based on Artificial Intelligence

As the separation and carrier of indoor and outdoor energy and climate conditions, building skin plays an important role in indoor environment regulation and effective utilization of outdoor environmental resources. The traditional fixed skin of residential buildings in cold regions lacks the ability to respond to the external climate, so it is difficult to meet the dual requirements of building energy efficiency and indoor comfort. In the long river of architectural development, the most important thing of architectural design is how to meet the climate adaptability. Traditional architectural forms have long been unable to meet the current social development, climate conditions, and user needs. Based on the basic theory, this paper establishes a systematic understanding of inlay, studies the design method of complex skin with geometric algorithm as the operating tool, discusses the application of this method in architectural design in combination with practice, more systematically and comprehensively studies the building skin with dynamic climate adaptability, and makes a physical model of building skin with dynamic climate adaptability. The contrast experiments under different control modes were carried out using the climate chamber experimental system. This research focuses on taking geometric principles as the prototype, trying to break the common design idea of generating skin by overlapping cells, and providing a systematic skin design method with strong operability and modular structure, hoping to help expand creative thinking.