###### Abstract  
Under the background of \"double carbon\", building carbon emission reduction is urgent, and improving energy efficiency through short-term building heat load forecasting is an efficient means of building carbon emission reduction. Aiming at the characteristics of the decomposed short-term building heat load data, such as complex trend changes, significant seasonal changes, and randomness, a single-step short-term building heat load prediction method driven by the multi-component fusion LSTM Ridge Regression Ensemble Model (ST-LSTM-RR) is designed and implemented. First, the trend and seasonal components of the heat load are decomposed by the STL seasonal decomposition algorithm, which are fused into the original data to construct three diversified datasets; second, three basic models, namely, the trend LSTM, the seasonal LSTM, and the original LSTM, are trained; and then, the ridge regression model is trained to fuse the predicted values of the three basic models to obtain the final predicted values. Finally, the method of this paper is applied to the heat load prediction of eight groups in a large mountain hotel park, and the root mean square error (RMSE) and mean absolute error (MAE) are used as the evaluation indexes. The experimental results show that the average RMSE and average MAE of the prediction results of the proposed method in this paper are minimized on the eight groups. building heat load prediction; ensemble deep learning; seasonal and trend decomposition using LOESS; long short-term memory neural network; ridge regression 202420243810399  
lighting). This data are then applied to machine learning models for predictive analysis, which in turn reduces energy consumption and enhances thermal comfort. The building heat load can guide the operation and regulation of the heating system, which is an important data basis for ensuring the stable and low-consumption operation of the heating system. If the heat load of a building can be predicted in advance, accurate heating can be realized. Building heat load forecasts can be categorized into ultra-short-term, short-term, medium-term, and long-term load forecasts according to the time granularity. Short-term building heat load forecasting is to forecast the heat load of a building for the next 1 h to 24 h or several days. According to the number of prediction steps, the short-term building heat load prediction can be further divided into single-step prediction and multi-step prediction, where single-step prediction refers to the prediction of the heat load in the next 1 h only, and multi-step prediction refers to the prediction of the heat load of the next multiple hours at one time by the model. In this paper, we study the single-step prediction method for short-term building heat loads. Short-term heat load data are characterized by seasonality, trends, randomness, volatility, and non-stationarity, which leads to an extremely complex pattern of change and a high difficulty in prediction. Therefore, the establishment of an accurate and stable prediction model becomes the key to solving the problem. With the continuous development of various Internet of Things (IoT), big data, and cloud computing technologies, it has become more convenient to obtain and store heating system operation data. This provides rich historical data for the establishment of data-driven models and satisfies the database required for data analysis and model training. Therefore, data-driven models are highly favored in the field of building heat load prediction at a national and international level and are gaining more research and application opportunities. Data-driven models fall into four main categories: statistical models, machine learning models, deep learning models, and hybrid models [4; 5]. Statistical models utilize mathematical and statistical knowledge to construct a model and produce predictions directly from the input data and the model. Because of its simple structure, it has a significant advantage in terms of time overhead [6].