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Highlights • Redefine the heating load patterns with people behavior and temperature related sub-patterns. • GMM clustering algorithm is used for heating load patterns in-depth identification. • A case study with four typical operation patterns is analyzed. • The prediction accuracy improvement with clustering result is proved. Abstract The identification of heating load patterns, also known as load profiles, is of vital importance to effective management and operation of district heating system (DHS). Clustering algorithms have been successfully applied in identifying heating load patterns. In this paper, we propose that the heating load patterns should be analyzed more specifically, and Gaussian Mixture Model (GMM) clustering is selected to extract sub-patterns. The novelty of this paper is that a new GMM clustering is applied to identify temperature related sub-pattern and people behavior related sub-pattern, and the clustering result is further utilized to improve the accuracy of prediction models. An energy station in Tianjin is used as case studies and four typical operation patterns are found with their characteristic of occur time and energy signature, which are defined as working pattern, on-duty pattern, daytime–nighttime pattern and nighttime–daytime pattern. The results reveal that this proposed method can make an in-depth identification of heating load patterns and also proves that the prediction accuracies of regression and artificial intelligence model are significantly improved by utilizing the GMM clustering results. [ABSTRACT FROM AUTHOR]

• An optimized operation strategy is proposed for ASHP heating of school buildings. • Effects of different combinations of start-up times and return water temperatures on power consumption are compared. • A longer warm-up time with a lower water temperature can save more energy. Air-source heat pump (ASHP) has been widely applied in heating systems of school buildings. In order to achieve building energy efficiency while ensuring indoor thermal comfort, the paper compared the effects of different operation strategies including intermittent heating mode in non-working hours and setpoint of return water temperature in working hours. A middle school building in Tianjin, China is chosen as a case study and its physical model is developed by using the EnergyPlus tool. After verifying the validity of the model, the article simulated 64 different operation conditions produced by combining 8 start-up times with 8 return water temperature setpoints to find a better operation mode of the intermittent heating. Results indicate that running at a lower water temperature with an earlier start-up time is more energy efficient compared with running at a higher water temperature with a later start-up time while meeting the thermal comfort requirement. In addition, by changing the return water temperature setpoints hourly based on the outdoor air temperatures, the situation of overheating and undercooling during working hours has been well improved and the energy saving rate is about 17% during the entire heating season. [ABSTRACT FROM AUTHOR]