A short-term load forecasting framework for air conditioning system based on model stacking

Accurate short-term air conditioning load forecasting is very important for controlling air conditioning energy consumption, and is also a requisite for realizing intelligent control of air conditioning units. This paper proposes a short-term load forecasting framework for air conditioning based on the concept of model stacking, which combines six mature machine learning models, including Lasso regression, Ridge regression, Random Forest, Support Vector Regression, eXtreme Gradient Boosting and Long Short-Term Memory, and trains new prediction models through model stacking. In order to realize the short term forecast function of air conditioning load, this paper presents a complete forecast framework, which includes operational procedures for feature screening, algorithm hyperparameters optimization, and cross stacking of prediction models. A real air conditioning system is employed for prediction analysis, the prediction results showed that 28 out of 36 control simulations achieved better prediction accuracy, with an average increase in R² of 6.4%. Notably, simpler submodels in the meta-model yield better results in model stacking, whereas complex coupling models as the meta-model may degrade performance. The proposed framework not only improves prediction accuracy but also maintains reasonable training time and resource requirements, making it practical for real-world applications. These findings provide insights into implementation of model stacking and selecting the meta-model for short-term air conditioning load forecasting.