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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.

With the increasing energy demand and environmental protection requirements of heating systems, natural gas (NG), as an efficient and clean alternative energy source for coal, plays an increasingly important role in district heating systems (DHSs). The accurate prediction of the DHS consumption of NG in the whole city is helpful in formulating an efficient energy scheduling plan. However, because the DHS is a complex non-linear system with time-space delay effect and multi-factors, the traditional algorithms have deficiencies in the prediction accuracy of the natural gas consumption for city-level DHS. To achieve accurate prediction, a gas consumption prediction algorithm based on the attention gated recurrent unit (AGRU) model is proposed, which can obtain high-level features of historical data that affect gas consumption prediction. In addition, the attention mechanism can help to select more critical feature inputs and improve the prediction accuracy of gas consumption. Detailed comparative experiments were performed between the proposed AGRU and state-of-art NG consumption prediction algorithms, such as the recurrent neural network (RNN), long short-term memory (LSTM), GRU, attention RNN (ARNN), attention LSTM (ALSTM), support vector regression (SVR), random forest regression (RFR), gradient boosting regression (GBR) and decision tree regression (DTR). An analysis of the experimental results shows that the proposed AGRU algorithm has a prediction accuracy of 95.3%, which is significantly better than other algorithms. In addition, the hyperparameters of the AGRU algorithm are also tested in detail to optimize the selection.

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.

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.

The energy efficiency monitoring is an essential precondition for ground source heat pump system’s controlling and energy saving operation. Based on the data monitoring applied in the school building, this work is focused on the parameters acquisition and operation analysis of the GSHP system in Tangshan. Results show the average COPs (coefficient of performance) are 2.85 and 2.70 in summer and winter, respectively, and heat(cold) unbalance underground existed after whole year operation. The analysis of data also indicates that the direct borehole air-conditioning saved some power consumption obviously in the early stage of summer and energy saving of the GSHP system depended remarkably on its operation and management level. Besides the observation points of ground temperature are laid for a large-scale GSHP system, and the hydraulic balance of the pipes group needs to be concerned specially in safeguarding better reliability.

Compared with conventional central circulating pumps (CCCPs) in the district heating system (DHS), the DHS with distributed variable speed pumps (DVSPs) shows a great potential for energy saving. In this paper, a hybrid control scheme both using electric control valves (ECVs) and DVSPs is applied to the district heating system in Shenyang, China. This new hybrid control system results in reduction of the boiler outlet pressure from 1.27 MPa to 0.81 MPa, which ensures safe operation of the heating network. In addition, the hydraulic imbalance of the primary pipelines is effectively reduced by using the DVSPs. It is found that the relative error between the designed and the measured total flow rates is 7.71%. Results show that the annual average value of electricity consumption by using the DVSPs is 28.52% smaller than that in the CCCP system. Therefore, there is a great potential for building energy saving if the DVSPs are installed into the DHS. In order to evaluate the heating quantity and guide the optimal operation for the DHS, indoor temperature acquisition devices will be installed in the near future.

Thermal response test (TRT) is crucial for the determination of the ground thermal conductivity and the evaluation of the thermal performance of borehole heat exchangers (BHEs) of ground source heat pump (GSHP) system. This paper presented a novel constant heating-temperature method (CHTM) for TRT. Further, a type of improved TRT equipment was developed and the mathematical model to deal with test data was presented. Based on the measurement of the natural ground temperature distribution, an in situ TRT case was carried out. The experimental results showed that, compared with the conventional TRT with constant heating-flux method (CHFM), CHTM has an obvious advantage of reducing the time period reaching a steady heat-transfer state between the BHE and its surrounding soils. This improved TRT equipment can effectively operate under both heat-injection and heat-extraction modes, and its test data can accurately reflect the thermal properties of the soils as well as the thermal performance of the BHE under different operation conditions. Finally, the advantages and disadvantages between CHTM and CHFM were compared, which can provide a useful reference for the design of GSHP system.

This paper presents a case study of underground thermal storage in a solar-ground coupled heat pump system (SGCHPS) for residential buildings. Based on the experimental results, the operation performance is simulated by the unit modelling. The results show that the performance of underground thermal storage of SGCHPS depends strongly on the intensity of the solar radiation and the matching between the water tank volume and the area of solar collectors. Compared with the solar radiation, the variations of the water tank temperature and the ground temperature raise lag behind and keep several peaks during the day time. In the present study, the experimental efficiency of underground thermal storage based on the absorbed solar energy by the collectors reaches 76%. For the similar design of SGCHPS, it is suggested that the optimal ratio between the tank volume and the area of solar collectors should range from 20 to 40 L/m2.

This paper is performed to analyze the performance of underground thermal storage in a solar-ground coupled heat pump system (SGCHPS) for residential building. Based on the experimental results, the system performance during a longer period is simulated by the unit modeling, and its parametric effects are discussed. The results show that the performance of underground thermal storage of SGCHPS depends strongly on the intensity of solar radiation and the matching between the water tank volume and the area of solar collectors. Compared with the solar radiation, the variations of the water tank temperature and the ground temperature rise lag behind and keep several peaks during the day time. For the case of Tianjin, the efficiency of underground thermal storage based on the total solar radiation and absorbed solar energy by the collectors can reach over 40% and 70%, respectively. It is suggested that the reasonable ratio between the tank volume and the area of solar collectors should be in the range of 20–40 L/m2.