Accurate prediction of photovoltaic and wind power generation is essential for maintaining stable energy supply in integrated energy systems. However, the strong stochasticity and complex fluctuations in these energy sources pose significant challenges to forecasting. Traditional methods often fail to handle the non-stationary characteristics of the generation series effectively. To address this, we propose a novel hybrid prediction framework that integrates variational mode decomposition, the Pearson correlation coefficient, and a benchmark prediction model. Experimental results demonstrate the outstanding performance of the proposed method, achieving an R² value exceeding 0.995 along with minimal MAE and RMSE. The approach effectively mitigates hysteresis issues during prediction. Furthermore, the model shows strong adaptability; even when substituting different benchmark models, it maintains an R² above 0.99. When applied in a building heating system, accurate predictions help reduce indoor temperature fluctuations, enhance energy supply stability, and lower energy consumption, highlighting its practical value for improving energy efficiency and operational reliability.

Comprehensive energy systems can synergize multiple forms of energy to meet user-side load demand, making full use of renewable energy for energy supply. However, the system often suffers from high instability in renewable energy supply or high variability in demand during operation, resulting in a mismatch between system supply and demand. This mismatch directly affects the energy supply efficiency of the system. The traditional single optimization approach, e.g., the two-stage co-optimization approach, has limitations in achieving both economic and energy savings, particularly as it does not consider the time scale. To address these issues, this study proposes a design framework for a two-layer collaborative optimization approach that incorporates multiple time scales and demand response coordination. The upper layer optimizes the capacity of the energy storage system, while the lower layer optimizes the coordinated operation of the energy supply facilities. In the lower layer optimization, the day-ahead scheduling phase considers tariff-based demand response to shift and curtail hot and cold electricity loads. The intraday optimization stage adjusts the results of the day-ahead scheduling to further optimize energy distribution and utilization, enhancing system economics and environmental friendliness. Analyzed in conjunction with practical cases, the results demonstrate that the optimization method improves the operational stability of the system and can reduce the total annual operating cost by 7.61%. Increasing the use of hybrid energy storage in the integrated energy system reduces total annual operating costs by 4.01%. If the use of demand response is added to the integrated energy system, the total annual operating cost can be reduced by 5.38%. This paper provides a theoretical reference for integrated energy system operation optimization studies.

This study presents a deep learning framework designed to accelerate Large Eddy Simulation (LES) of airflow in urban environments. The framework leverages physics constrained conditional Generative Adversarial Networks (GANs) trained on instantaneous velocity snapshots from a synthetically generated dataset comprising 130 high-fidelity CFD simulations of simple building configurations. By learning the mapping from early-stage flow fields to their statistically steady-state counterparts, the framework allows the simulation to bypass the lengthy transient averaging phase and predict the final time-averaged fields directly. Two GAN-based architectures are explored: a conventional convolutional model operating on structured uniform grids (Grid-GAN), and a graph-based model (Graph-GAN) that utilizes Graph Neural Networks (GNNs), specifically Graph Attention Networks (GATs), to process unstructured CFD mesh data while preserving native spatial connectivity. Both approaches are integrated into a fully automated pipeline built exclusively on open-source tools, including OpenFOAM for CFD simulations, FreeCAD and ParaView for preprocessing, and PyTorch for deep learning model development and training. Results demonstrate that the proposed models can significantly reduce LES computational costs while retaining accuracy in predicting turbulent flow characteristics. The Graph-GAN, in particular, shows enhanced adaptability and physical consistency due to its ability to exploit mesh refinements in critical regions. This work lays the foundation for the development of robust, physics-informed surrogate models and supports the growing integration of deep learning with scientific simulations in fluid mechanics.

Phase change material (PCM) applied on building envelope (e.g., roof, wall) has been proved to be effective for reducing both cooling and heating energy use. Recently, preliminary studies have applied high-emissivity radiative cooling material on PCM for enhancing cooling performance. However, since emissivity of the cooling material is static, it leads to a fast indoor heat dissipation rate at cold ambient and thus results in increased building heating loads. To this end, this study develops a dynamic-emissivity PCM that synchronizes adaptive emissivity regulation with PCM’s latent heat characteristics, thereby reducing both building cooling and heating loads. This composite material integrates a temperature-responsive Fabry-Perot resonator with PCM using facile and cost-effective method (mainly spin coating). The composite PCM can achieve low emissivity for heating in cold environment and high emissivity for cooling in hot environment in response to ambient temperature change, which matches the latent heat absorption and release of PCM for improving thermal performance. Outdoor experiments show that the composite PCM can reduce and increase the indoor temperature by more than 3.5 °C at hot ambient and 1.5 °C at cold ambient respectively in comparison with conventional PCM. Numerical simulations demonstrate the developed material can achieve 10.5–23.5 % year-round energy savings across diverse Chinese climate regions. At last, the color design of the composite material is also conducted to cater for users’ preferences. This work paves new way to develop advanced PCM for all-season building energy savings.

Identifying leakage sources in industrial factory production is crucial to improving air quality, ensuring people’s health and safety and preventing safety accidents. In this study, a method for leakage source identification in industrial factories combining with computational fluid dynamics (CFD) and correlation coefficient was proposed and validated. The study first experimentally validated the numerical methods, which were fundamental to the leakage identification method. Then impacts of leakage sources, sensor errors and number of sensors on the source identification results were evaluated. The results showed that the identification accuracy could be significantly improved by refining the step size of the coefficient ar in this method. When the number of leakage sources was unknown, the accuracy of this method in identifying the number and location of leakages was 93.5%. The computation time spent on source identification depended on the maximum number of leakage sources. Using four sensors with errors were enough to identify the number of unknown leakage sources. The number of leakage sources did not exceed three at the same time. Overall, coupled CFD and correlation coefficients method could effectively identify the number, location and intensity of leakages.

In the context of addressing global climate change and advancing the “carbon peak and carbon neutrality” goals, low-carbon transformation of block energy supply systems has become a key pathway to sustainable urban development. This study selects a block in Tianjin as a case study and proposes a multi-measure synergistic assessment method for block energy supply carbon neutrality, integrating load forecasting, energy-saving retrofitting, and renewable energy generation strategies. Firstly, the impacts of various energy-saving measures—such as load reduction, high-performance doors and windows (HDW) installation, energy substitution, and pipeline network planning—on block energy demand were analyzed. Secondly, the contributions of high-biogenic-content waste (HBW) power generation and photovoltaic (PV) power generation to block energy supply carbon neutrality were analyzed. Finally, a comprehensive assessment of carbon neutrality was conducted, along with the calculation of the economic payback period. The results indicate that the contribution of each measure to load reduction follows the order: PV power generation ≫ HBW power generation > HDW installation > energy substitution ≈ load reduction. Renewable energy generation not only offset the remaining carbon emissions during the operational phase of energy supply, but also fed surplus electricity into the grid, contributing an additional 427 tCO<inf>2</inf> of net carbon reduction. Based on the calculation of initial investment and annual net revenue of the multi-measure synergistic approach, the economic payback period of the block energy supply retrofit is 8.24 years. This study provides theoretical support and practical guidance for achieving block energy supply carbon neutrality.

The thermal environment and thermal comfort of an outdoor space have large spatial and temporal variations. To provide an overall picture, outdoor thermal comfort (OTC) should be analyzed on a yearly basic with high temporal-spatial resolution. The difficulty of annual OTC evaluation lies in the huge computational cost of wind simulations. Therefore, our study proposed a method to accelerate wind simulations through the use of Fast Fluid Dynamics (FFD), Proper Orthogonal Decomposition (POD) and Reynolds Number Independence (Re-independence). A case study of an actual urban building complex was employed to validate our study by comparing the integrated index Universal Thermal Climate Index (UTCI) results by our method with those by fully-resolved simulations. The average difference of UTCI was just 0.06 ∘C, indicating that the accuracy of our method is guaranteed. Besides, it only took 8 hours to complete the OTC assessment of this site with an area of 125,600 m2. The framework proposed in this study can be used to reveal the complete picture of OTC with affordable computational cost and reasonable accuracy.

Efficient use of solar energy can effectively alleviate the problem of energy shortages. Currently, extensive researches have been carried out on photothermal conversion materials. However, factors such as photothermal conversion and thermal conductivity limit the practical implementation of photothermal materials. In this research, a novel flexible composite phase change material (CPCM) with melamine foam (MF) as the supporting skeleton, carbon nanotube (MWCNT)/polydopamine (PDA) as the light-absorbing coating and polyethylene glycol (PEG) as the energy storage material was successfully prepared. The results show that the MF-based CPCMs have good shape stability with a leakage of only 0.9 %, and high phase change enthalpies, the melting enthalpy was above 172.0 J/g. In addition, the thermal conductivity was improved due to the introduction of light-absorbing coatings into the CPCMs. Compared with MF/PEG, MWCNT/MF/PEG, and PDA/MF/PEG, MWCNT/PDA/MF/PEG were improved by 35.21 %, 8.6 % and 29.46 %, respectively. Finally, the average charging efficiency of MWCNT/PDA/MF/PEG is 96.26 %, which is 62.33 % and 20.1 % higher than that of MWCNT/MF/PEG and PDA/MF/PEG respectively, indicating that MWCNT and PDA nanofillers played a synergistic role in enhancing the performance of CPCMs. This study provides new and innovative ways for the design of photothermal materials.

This study aims to improve the accuracy and speed of predictions for thermal comfort and air quality in built environments by creating a coupled framework between computational fluid dynamics (CFD) simulations and deep learning models. The coupling approach is showcased by the development of a data-driven turbulence model. The new turbulence model is built using a deep learning neural network, whose mapping structure is based on a zero-equation turbulence model for built environment simulations, and is coupled with the CFD software OpenFOAM to create a hybrid framework. The neural network is a standard shallow multi-layer perceptron. The number of hidden layers and nodes per layer was optimized using Bayesan optimization algorithm. The framework is trained on an indoor environment case study, as well as tested on an indoor office simulation and an outdoor building array simulation. Results show that the deep learning based turbulence model is more robust and faster than traditional two-equation Reynolds average Navier-Stokes (RANS) turbulence models, while maintaining a similar level of accuracy. The model also outperforms the standard algebraic zero-equation model due to its superior ability to generalize to various flow scenarios. Despite some challenges, namely the mapping constraint, the limited training dataset size and the source of generation of training data, the hybrid framework demonstrates the viability of the coupling technique and serves as a starting point for future development of more reliable and advanced models.

Accurate turbulence modeling is essential for simulation studies of urban physics. In this study, the comprehensive atmospheric boundary layer (ABL) model involving a variable model coefficient and an additional turbulent dissipation source term was implemented using the open-source software OpenFOAM. Combined with consistent inlet wind profiles and rough wall functions of different turbulence variables based on the aerodynamic roughness, the model maintained the horizontal homogeneity well. Then, a hybrid approach was introduced to consider buildings immersed in ABL flows, enabling automatic transformation of the turbulence model between the region around the buildings and the free flow region away from any building. Finally, the effects of applying different model forms to the airflow field around buildings were evaluated in detail through three-dimensional building cases representing six urban prototypes based on three wind tunnel databases. Our findings indicated that all modified k–ε models perform well in reproducing the flow data of the CEDVAL and Architectural Institute of Japan (AIJ) experiments consisting of a single building, an array of buildings, and an isolated high-rise building. However, the modified k–ε model with an additional correction term performed poorly in the database of Niigata Institute of Technology and the case of complex terrain and urban building configurations, because the correction term inhibited the generation of turbulent kinetic energy. In addition, from the comparison between the experimental data of all cases, the model with the original formulation of the coefficient performed the best in terms of prediction accuracy. The root mean square errors of the normalized velocity were 0.1250, 0.0879, 0.1145, 0.1350, and 0.1492 in different cases, which proved the reliability of this turbulence model.