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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.

Indoor temperature is an important criterion for evaluating the operation quality of district heating systems (DHSs) and has a significant impact on improving the energy efficiency of heat exchange station regulation. Accurate prediction of indoor temperature is of great help to the precise control of heating systems. However, due to the thermal inertia and response delay characteristics of heating systems, there is a complex nonlinear relationship between indoor temperature and its many related factors. A hierarchical attention gated recurrent unit (HAGRU) neural network is innovatively proposed for predicting the indoor temperature of energy-saving buildings in which the indoor temperature is optimally regulated based on a newly designed smart on-off valve. The network is divided into two levels of attention model, which can realize the representation of a single influencing factor and the fusion of multiple feature inputs. Detailed simulation results show that the predictive accuracy of the proposed algorithm is 98.4%, which is significantly better than state-of-art algorithms, such as support vector machine (SVM), random forest regression (RFR), decision tree regression (DTR), gradient boosting regression (GBR), recurrent neural network (RNN), long short-term memory (LSTM) and gated recurrent units (GRU). Therefore, the proposed HAGRU algorithm has good nonlinear feature extraction and expression ability. The indoor temperature prediction results are used as feedback input to assist the control strategy of the heat exchanger station, which is conducive to the improvement of energy efficiency. In addition, the optimal selection of hyperparameters of the HAGRU algorithm is also analyzed in detail.

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.

Heat loads change dynamically with meteorological conditions and user demand, and the related accurate prediction algorithms are conducive to the realization of optimized automatic control and green heating. However, a traditional load forecasting algorithm exhibits low accuracy and is sensitive to data noise. Hence, it is difficult to satisfy the requirements of industrial applications via forecasting algorithms. The progress in big data technology leads to the construction of a smart district heating system (SDHS) with the next generation technology wherein artificial intelligence algorithms play the role of a chemical catalyst. The operation monitoring of heat exchange stations installed with various Internet of Things (IoT) sensors provides abundant data resources for heating load forecasting based on machine learning algorithms. A novel estimation model based on spatiotemporal hybrid convolution neural network long–short term memory (CNN–LSTM) is presented to predict the heating load more reasonably for the SDHS. This hybrid model deeply integrates the excellent feature extraction ability of CNN and LSTM in spatiotemporal two-dimensional feature space. Furthermore, CNN can extract the heating load's spatial characteristics and influencing factors, and LSTM can extract the heating load's temporal characteristics. Therefore, the CNN–LSTM algorithm can describe the complex change trend of the heating load and lead to a more accurate dynamic model of heating load with high nonlinearity and considerable thermal inertia delay. A detailed performance comparison was conducted between the CNN–LSTM and support vector machine (SVM) and ensemble learning algorithms, such as extra tree regression (ETR), random forest regression (RFR), gradient boosting regression (GBR), multiple layer perception (MLP). The experimental results showed the evident accuracy advantages of prediction performance based on the proposed CNN–LSTM algorithm. The MAPE evaluation indexes of the four heat exchange stations are distributed between 3.1% and 4.1%. Besides, this algorithm exhibits good adaptability for heating load data with different numerical ranges, which can satisfy the field engineering applications' requirements in a more enhanced manner.

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.