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Accurate heat load prediction algorithm provides important support for the stable and efficient operation of smart district heating system (SDHS) and helps to realize energy saving and consumption reduction. However, previous researches on heat load prediction are mostly carried out on various regression analyses and modeling prediction, without considering the inherent time delay and spatial dependence between heat exchange stations during regulation. Therefore, a novel heat load prediction model based on the hybrid spatial-temporal attention long short-term memory (STALSTM) is proposed. The STALSTM model introduces the spatial dependence characteristics of heating pipe network into the heat load prediction model, and the influencing factors of heat consumption are considered comprehensively from the time and space dimensions. Then, the LSTM algorithm is used to memory the information of historical data sequence, and the attention mechanism is used to realize the adaptive estimation of the characteristic weight of each influencing factor, which improves the prediction accuracy. And in order to verify the effectiveness of the proposed model, a detailed experimental comparison is made between the STALSTM model and the state-of-the-art algorithms. The results show that the STALSTM model has the best prediction accuracy, and the correctness of introducing the spatial-temporal characteristics and the attention mechanism is also proved.

The energy demand of the district heating system (DHS) occupies an important part in urban energy consumption, which has a great impact on the energy security and environmental protection of a city. With the gradual improvement of people&rsquo, s economic conditions, different groups of people now have different demands for thermal energy for their comfort. Hence, heat metering has emerged as an imperative for billing purposes and sustainable management of energy consumption. Therefore, forecasting the heat load of buildings with heat metering on the demand side is an important management strategy for DHSs to meet end-users&rsquo, needs and maintain energy-saving regulations and safe operation. However, the non-linear and non-stationary characteristics of buildings&rsquo, heat load make it difficult to predict consumption patterns accurately, thereby limiting the capacity of the DHS to deliver on its statutory functions satisfactorily. A novel ensemble prediction model is proposed to resolve this problem, which integrates the advantages of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and support vector regression (SVR), called CEEMDAN-SVR in this paper. The proposed CEEMDAN-SVR algorithm is designed to automatically decompose the intrinsic mode according to the characteristics of heat load data to ensure an accurate representation of heat load patterns on multiple time scales. It will also be useful for developing an accurate prediction model for the buildings&rsquo, heat load. In formulating the CEEMDAN-SVR model, the heat load data of three different buildings in Xingtai City were acquired during the heating season of 2019&ndash, 2020 and employed to conduct detailed comparative analysis with modern algorithms, such as extreme tree regression (ETR), forest tree regression (FTR), gradient boosting regression (GBR), support vector regression (SVR, with linear, poly, radial basis function (RBF) kernel), multi-layer perception (MLP) and EMD-SVR. Experimental results reveal that the performance of the proposed CEEMDAN-SVR model is better than the existing modern algorithms and it is, therefore, more suitable for modeling heat load forecasting.

For district heating systems, prediction of the heat load is a very important topic for energy storage and optimized operation. For large and complex heating systems, most prediction models in previous publications only considered the influence of outdoor temperature, whereas the indoor temperature and thermal inertia of buildings were not included. For an energy-efficient residential building in Shijiazhuang (China), the heat load prediction is investigated using various prediction models, including a wavelet neural network (WNN), extreme learning machine (ELM), support vector machine (SVM) and back propagation neural network optimized by a genetic algorithm (GA-BP). In these models, the indoor temperature and historical loads are considered as influencing factors. It is found that the prediction accuracies of the ELM and GA-BP are slightly higher than that of WNN, so the ELM and GA-BP models provide feasible methods for the heat load prediction. The SVM shows smaller relative errors in the model prediction compared with three neural network algorithms.

Phase change material (PCM) used in building can enhance the thermal inertia of building and improve the indoor thermal comfort. A novel shape-stabilized phase change material wallboard (PCMW), in which paraffin was as the PCM and expanded perlite (EP) was the supporting material, has been prepared through a horizontal vacuum absorption rotate roller (HVARR) in this study. Its melting point and freezing point were measured to be 27.60 °C and 23.56 °C, respectively, and the melting and freezing latent heats reached to be 67.13 J/g and 67.06 J/g, respectively. PCMW was experimentally and numerically studied to analyze the thermal performance. The experimental result in 5 days, which was a demonstration in a container subject to weather conditions typical for the north China, showed that PCMW used in the building can maximally reduce the indoor temperature of 2.53 K. For the numerical study, based on the equivalent heat capacity method, a one-dimensional heat transfer model of PCMW was developed and combined with TRNSYS. This model was validated by the experimental data, and the numerical data agreed well with the experiment data. Under the summer condition, the numerical study of PCMW used in a typical office building during two months showed PCMW can averagely reduce the temperature of 9.22 K in the building operation time (7:00–18:00). Besides, according to the numerical model, an optimal configuration of PCMW in the cold zone of China has been obtained. The studied method provides a comprehensive guide to the PCMW design and application.

Phase change material (PCM) used in buildings can enhance indoor thermal comfort and decrease building energy consumption. A kind of shape-stabilised PCM particle (PCMP) was prepared by liquid paraffin to be absorbed into the micropores of expanded perlite (EP) with a self-made vacuum heating rolling box. A novel composite phase change material wallboard (CPCMW) was fabricated by PCMP, styrene acrylic emulsion and glass fibers under a plate-shape mould. The properties including thermal property, internal microstructure and mechanical property have been characterized by differential scanning calorimeter(DSC), scanning electron microscope (SEM) and electronic universal testing machine. The cold storage performance of CPCMW was experimentally studied in two rooms with the same size, under the summer condition. CPCMW was incorporated with the inside surface of wall and ceiling in a room, which was called PCM room (PCMR); while the other one without PCM was addressed as reference room. Besides, three operation strategies, i.e. natural convection operation with time(NCTI), natural convection operation with temperature (NCTE), and cold storage in closed building were conducted in experiments. In DSC test, the melting point and latent heat of CPCMW were 25.22 °C and 85.63 J/g, respectively. From SEM and electronic universal testing machine analyses, it was found that CPCMW had a compact and firm microstructure, and good strength and toughness. Based on the comparison data of PCMR and reference room in the experiment of cold storage, CPCMW showed a satisfied thermal performance, especially with the operation strategy of NCTE. It can be concluded that the novel CPCMW has a significant opportunity for building application.

The smart district heating system (SDHS) is an important element of the construction of smart cities in Northern China, it plays a significant role in meeting heating requirements and green energy saving in winter. Various Internet of Things (IoT) sensors and wireless transmission technologies are applied to monitor data in real-time and to form a historical database. The accurate prediction of heating loads based on massive historical datasets is the necessary condition and key basis for formulating an optimal heating control strategy in the SDHS, which contributes to the reduction in the consumption of energy and the improvement in the energy dispatching efficiency and accuracy. In order to achieve the high prediction accuracy of SDHS and to improve the representation ability of multi-time-scale features, a novel short-term heating load prediction algorithm based on a feature fusion long short-term memory (LSTM) model (FFLSTM) is proposed. Three characteristics, namely proximity, periodicity, and trend, are found after analyzing the heating load data from the aspect of the hourly time dimension. In order to comprehensively utilize the data&rsquo, s intrinsic characteristics, three LSTM models are employed to make separate predictions, and, then, the prediction results based on internal features and other external features at the corresponding moments are imported into the high-level LSTM model for fusion processing, which brings a more accurate prediction result of the heating load. Detailed comparisons between the proposed FFLSTM algorithm and the-state-of-art algorithms are conducted in this paper. The experimental results show that the proposed FFLSTM algorithm outperforms others and can obtain a higher prediction accuracy. Furthermore, the impact of selecting different parameters of the FFLSTM model is also studied thoroughly.

Recycling heat from local industrial processes is one of the suitable and strategic opportunities for district heating all over the world. However, in northern China, the majority of low-grade energy has been wasted, the main difficulties are of an institutional and commercial character. The rapid development of renewables and constant promotion of time-of-use tariff policy has provided new chances for power-to-heat implementation and thermal energy storage. In this study, a general configuration optimization framework of additional waste heat recovery and storage (WHRS) devices is proposed, which is based on the cost-benefit analysis. Due to the implicit nature of such an optimization problem, conventional economic dispatch, two-stage branch evaluation, linear programming solver, and two kinds of advanced optimization algorithms (i.e., Genetic Algorithm and Pattern Search) have been integrated systematically. Moreover, case studies and sensitivity analyses are conducted to verify the feasibility of the proposed optimization model and to reveal the effects of uncertain economic parameters, respectively. The results show that the average time-consuming of Pattern Search is only 4.9% of the GA optimizations. When introducing additional WHRS devices with optimal configurations, the total operating costs decreased by 6.9% and the cost proportion of gas boiler declines remarkably from 8.6% to 2.0%.

Double-skin facade (DSF), as one of modern architecture envelopes, is beautiful and transparent. However, as transparent building envelope, double-skin facade has shortcomings in thermal performance, such as direct sunlight in summer. In order to overcome these drawbacks, a year-round optimum operation strategy of respiration-type double-layer glass curtain wall, which is one kind of double-skin facade with natural air circulation and blind system, has been researched by an experimental way in an office building. The optimum operation strategies that included changing air circulation and adjusting blind sun shading angle and installation position has been studied through comparative experiments in two identical rooms of the office building. For a year-round using in the cold zone of China, the related experiments were conducted in three stages: summer, winter and transition season. It was found that the optimum operation strategies in three seasons were different, but all of them have obtained well results. First of all, with the sun shading of blinds and the air external circulation, temperatures of air flow duct and room were decreased in summer. And then, with blinds removing, the indoor room can be warmed up by “greenhouse effect” created by respiration-type double-layer glass curtain wall for winter using. At last, in transition season, respiration-type double-layer glass curtain wall has increased the indoor thermal comfort and supplied fresh air.

An accurate heating load prediction algorithm can play an important role in smart district heating systems (SDHS), which is helpful for realizing on-demand heating and fine control. However, most of the traditional heating load prediction algorithms neglect the indoor temperature feedback from the household and cannot form closed-loop control. This paper designs an intelligent sensor based on the Narrow band Internet of Thing (NB-IoT) to collect the indoor temperature of a typical household and proposes an algorithm based on attention long short term memory (ALSTM) to predict the heating load for an integrated "heat exchange station - heat user". The attention mechanism is designed to obtain more accurate nonlinear prediction models between the heating load and influencing factors, such as indoor temperature, outdoor temperature, and historical heat consumption. A performance comparison with other state-of-the-art algorithms shows that the proposed ALSTM algorithm has the best performance, achieving an accuracy of 97.9%. Besides, a Kalman filter is introduced to identify and remove outliers while reducing the random error of the measurement.

This study is focused on the preparation and performance of a building energy storage panel (BESP). The BESP was fabricated through a mold pressing method based on phase change material particle (PCMP), which was prepared in two steps: vacuum absorption and surface film coating. Firstly, phase change material (PCM) was incorporated into expanded perlite (EP) through a vacuum absorption method to obtain composite PCM; secondly, the composite PCM was immersed into the mixture of colloidal silica and organic acrylate, and then it was taken out and dried naturally. A series of experiments, including differential scanning calorimeter (DSC), scanning electron microscope (SEM), best matching test, and durability test, have been conducted to characterize and analyze the thermophysical property and reliability of PCMP. Additionally, the thermal performance of BESP was studied through a dynamic thermal property test. The results have showed that: (1) the surface film coating procedure can effectively solve the leakage problem of composite phase change material prepared by vacuum impregnation; (2) the optimum adsorption ratio for paraffin and EP was 52.5:47.5 in mass fraction, and the PCMP has good thermal properties, stability, and durability; and (3) in the process of dynamic thermal performance test, BESP have low temperature variation, significant temperature lagging, and large heat storage ability, which indicated the potential of BESP in the application of building energy efficiency.

Phase change material (PCM) used in buildings can reduce the building energy consumption and indoor temperature fluctuation. A composite PCM has been fabricated by the binary eutectic mixture of tetradecanol (TD) and lauric acid (LA) absorbed into the expanded perlite (EP) using vacuum impregnation method, and its thermal conductivity was promoted by aluminium powder (AP) additive. Besides, the styrene-acrylic emulsion has been mixed with the composite PCM particles to form the protective film, so as to solve the problem of leakage. Thus, a novel PCM panel (PCMP) has been prepared using compression moulding forming method. The thermal property, microstructure characteristic, mechanical property, thermal conductivity, thermal reliability and leakage of the composite PCM have been investigated and analysed. Meanwhile, the thermal performance of the prepared PCMP was tested through PCMPs installed on the inside wall of a cell under outdoor climatic conditions. The composite PCM has a melting temperature of 24.9 °C, a freezing temperature of 25.2 °C, a melting latent heat of 78.2 J/g and a freezing latent heat of 81.3 J/g. The thermal conductivity test exposed that the thermal conductivity has been enhanced with the addition of AP and the latent heat has been decreased, but it still remains in a high level. The leakage test result has proven that liquid PCM leaking has been avoided by the surface film method. The thermal performance experiment has shown the significant function of PCMP about adjusting the indoor temperature and reducing the heats transferring between the wall inside and outside. In view of the thermal performance, mechanical property and thermal reliability results, it can be concluded that the prepared PCMP has a promising building application potential.