Abstract:

The heat load of integrated energy systems in parks is affected by multi-energy flows， and existing prediction models have insufficient feature extraction capabilities. To address this， a dual feature processing model for heat load prediction was proposed， integrating improved complete ensemble empirical mode decomposition with adaptive noise （ICEEMDAN） and multivariate phase space reconstruction. The heat load time series was decomposed using the ICEEMDAN method， and the components were reconstructed by calculating their sample entropy. These were then combined with input features such as air temperature to form multivariate time series datasets at different frequencies. The optimal delay time and embedding dimension of the series were determined using the C-C method， thereby obtaining the high-dimensional phase space of each dataset. The heat load components were predicted using a bidirectional long short-term memory neural network model with optimized parameters. The final heat load prediction value was obtained by summing the prediction results. The case study results showed that the proposed method achieved good prediction performance compared to other models.

Key words: mode decomposition, multivariate phase space reconstruction, heat load prediction, bidirectional long short-term memory neural network, integrated energy systems in parks