## 2 Methodology
### Proposed ST-LSTM-RR Ensemble Deep Learning Model  
To address the problem of the single-step prediction of a short-term building heat load, this paper proposes and implements a multi-component fusion LSTM Ridge Regression Ensemble Model (hereafter referred to as the ST-LSTM-RR model). This ensemble model is a type of ensemble deep learning model that employs parallel ensemble technology. The integration strategy involves training different structures of the same basic model with datasets containing various features, thereby introducing diversity into the basic model. According to the results of the data analysis, the heat load dataset used in the study exhibits clear trends, seasonality, and randomness, making the pattern of data changes extremely complex. The proposed ensemble model cleverly incorporates trend and seasonal components to allow the LSTM model to better capture the patterns of heat load changes; by integrating three diverse LSTM models, it further enhances the overall predictive performance and stability of the model, enabling it to counteract the randomness of heat load changes to a certain extent. The ST-LSTM-RR model structure is divided into three main parts, with the specific structure and principles illustrated in Figure 7.  
The first part involves decomposing to construct diverse datasets. Using the additive mode of the STL seasonal decomposition algorithm, the original building heat load values \(Y\) are decomposed into three components: the trend \(T_{t}\), seasonality \(S_{t}\), and residual \(R_{t}\). Since the residual component is difficult to predict, it is discarded, and only the seasonal and trend components are added as features to the original data features. This process creates a trend dataset \(D_{T}\) and a seasonal dataset \(D_{s}\), plus the original dataset \(D_{O}\), resulting in three diverse datasets. These datasets are independent of each other, with each being used to train a separate basic model. \[D_{T}=\{X_{1},\dots,X_{n},T_{t},Y\} \tag{17}\]  
\[D_{\text{S}}=\{X_{1},\dots,X_{n},S_{t},Y\} \tag{18}\]  
\[D_{O}=\{X_{1},\dots,X_{n},Y\} \tag{19}\]  
where \(X_{i}\) represents the features, the trend \(T_{t}\) and seasonality \(S_{t}\) are also considered as features, and n is the number of features in the original dataset, excluding the heat load. The second part involves training diverse basic models. The basic model employed is the LSTM model, which is the preferred model for predicting time series data, and building heat load data also fall into the category of time series data. Parallel training is utilized, employing the trend dataset, seasonal dataset, and original dataset to train the Trend LSTM (abbreviated as TLSTM), Seasonal LSTM (abbreviated as SLSTM), and Original LSTM (abbreviated as OLSTM) models, respectively. These three models are independent of each other, possessing different structures and parameters. The Original LSTM provides the basic prediction, the Trend LSTM is better at predicting changes in peaks, and the Seasonal LSTM excels at capturing cyclical variations. This part ultimately produces three diverse basic models. After training is completed, a validation set (val) is input into the three models to obtain the prediction values \(P_{t}\), \(P_{s}\), and \(P_{o}\), which will be used to train the metamodel. \[P_{S}=SLSTM(val) \tag{20}\]  
\[P_{T}=TLSTM(val) \tag{21}\]  
Figure 7: Schematic Diagram of the Ensemble Deep Learning Model Structure. \[P_{\text{O}}=OLSTM(val) \tag{22}\]  
The third part involves the training and prediction of the meta-model. When training deep learning models, if only a single model is used, it can take a considerable amount of time to adjust parameters to find the optimal model for a given dataset. However, a meta-model, which learns based on the experience of basic models, can integrate the prediction results of multiple basic models, reducing the overall model tuning time and offering better stability.