Data-driven load prediction and analysis of demand-side response flexibility potential of campus domestic hot water systems

The energy consumption of domestic hot water in university dormitories is one of the main components of the energy consumption of campus heating systems, and it is important to predict the domestic hot water load and quantify its flexibility potential to optimize system operation and absorb renewable energy. Taking a university in Beijing as an example, based on the measured data of domestic hot water, this paper uses clustering analysis and statistical fitting to establish a hot water demand prediction model. The verification results show that the root mean square error of the clustering statistical model is 4.48 m3for the air conditioning season, 4.42 m3for the heating season, and 4.52 m3for the transition season, and the maximum relative errors of the delay curves are 5.47%, 5.00% and 6.46% for each respective season. Based on this, firstly, the study presents hot water time-sharing price adjustments and credit incentives, along with four student-focused demand response strategies: A, B, C, and D. Secondly, the study also develops a response probability calculation method, utilizing a binomial distribution function and integrating data on students’ willingness to participate. Finally, the study calculates the probability distribution of the flexibility potential of the hot water system,and uses the flexibility potential index to quantify the flexibility potential expectation of the hot water system. The results show that the peak shaving volume, valley filling volume and peak shaving rate increase gradually from strategy A to D, with the highest peak shaving volume being 34 921 m3/year and the rate at 62.66%. The ratio of peak shaving to valley filling varies from 39% to 53%,and the peak shaving rate is higher in the air conditioning season and summer vacation, lower in the heating season and winter vacation, and the transition season is between the two.