Energy Consumption Prediction in Building Design Management: Mathematical Modeling and Algorithm Analysis

In recent years, accurate energy consumption prediction in the building design stage has become a core challenge in achieving low-carbon goals. Existing methods mostly rely on historical data statistics or single physical models, which makes it difficult to dynamically quantify the complex coupling effects of design parameters on energy consumption in the early stages of scheme iteration. To this end, this study proposes a mathematical modeling framework and lightweight algorithm system based on multi-dimensional parameter coupling. First, a dynamic time series model is constructed to deconstruct the nonlinear correlation characteristics of 12 types of design variables such as building form, enclosure structure, and equipment system. Secondly, an improved LSTM (Long Short-Term Memory) network algorithm is introduced to optimize the stable propagation of the gradient of long-period energy consumption series through the gating mechanism, and the Monte Carlo method is combined to perform probabilistic simulation of parameter sensitivity in typical climate zones. Finally, an energy consumption prediction plug-in based on BIM (Building Information Modeling) is developed to achieve seamless integration of algorithm models and design tool chains. Experiments show that compared with the EnergyPlus method, the root mean square error (RMSE) of the improved LSTM algorithm is reduced to 42.3 kWh, and the mean absolute error (MAE) is reduced to 31.7 kWh. In addition, in terms of computational efficiency, LSTM has obvious advantages in processing large-scale data. After combining with the BIM tool chain, the application of the LSTM model not only improves the accuracy of energy efficiency prediction in the design stage but also significantly shortens the calculation time, verifying its feasibility and advantages in building design management.