###### Abstract
Common statistical models include Autoregressive Moving Averages (ARMA) [7], Autoregressive Composite Moving Average (ARIMA) [8], Exponential smoothing [9], Multiple Nonlinear Regression (MNR) [10], Recursive Least Squares (RLS) [11], and the classic Box-Jenkins methodology models [12]. Although these statistical models can consider the temporal relationship of the data, it is difficult to capture the nonlinear pattern of heat load data and the nonlinear relationship with other relevant influencing factors, which leads to the prediction accuracy being insufficient to meet the actual work requirements. With the progress of computer hardware technology and artificial intelligence technology, artificial intelligence models based on machine learning and deep learning are gradually popularized and applied [13]. The most common machine learning models are Decision Trees (DT) [14], Random Forest (RF) [15], Support Vector Machine (SVM) [16], Extreme Gradient Boost (XGBoost) [17] and so on. These machine learning models can be used for regression prediction and are capable of effectively forecasting the patterns of change in building heat load data. However, it is difficult to take into account the time dependence of data, and feature engineering is more complicated. The deep learning model is an end-to-end model and does not need to carry out complex feature engineering. It can not only predict the nonlinear pattern of heat load data but also consider the time dependence of the data and capture the relationship between different influencing factors and the heat load so as to reduce the impact of the randomness of the heat load. Among the deep learning models suitable for time-series prediction are Deep Neural Networks (DNNs) [18], Recurrent Neural Networks (RNNs) [19], Long Short-Term Memory Neural Networks (LSTMs) [20; 21], Gated Recurrent Units (GRUs) [22], Convolutional Neural Networks (CNNs) [23; 24], Transfer Learning [25; 26], Reinforcement Learning [27], and so on. LSTM is a variant of RNN that optimizes the issues of vanishing and exploding gradients in RNNs, making it the preferred model for sequence prediction. [PERSON] et al. [28]developed a Bayesian optimization-based LSTM model for short-term heat load forecasting. This model is trained and tested using real-time data from the heat exchange stations in Changchun City, and its predictive performance is verified through multiple evaluation metrics. The results indicate that this model surpasses other methods in terms of prediction accuracy, especially in the 72 h forecast step. [PERSON] et al. [29] proposed a multi-layer GRU model based on an attention mechanism, which performs excellently in multi-step short-term load forecasting, particularly in dealing with long input sequences. It can effectively focus on important variables, thereby improving the model's predictive performance. However, single deep learning models have certain limitations in terms of prediction accuracy and robustness. Hybrid models, which combine multiple base models, aim to leverage their strengths comprehensively to enhance predictive performance. Hybrid modeling has gradually been widely studied and applied by scholars. [PERSON] et al. [30] proposed a novel hybrid deep reinforcement learning integrated optimization model, aimed at effectively predicting the heat load in District Heating Systems (DHS). This model integrates a similar sample selection method, a short-term prediction model pool, and a deep reinforcement learning integration strategy, and it has been validated using a dataset from a heat exchange station in Tianjin. The experimental results show that the model can accurately predict heat load variations, achieving energy savings of 5.33%, 5.31%, and 5.07% across different prediction periods. [PERSON] et al. [31] developed a novel regression tree ensemble model in their study, which combines decision trees with the LS-boosting algorithm and is optimized by the SFLA algorithm. This model effectively predicted the heating and cooling loads of residential buildings.