Predicting the long-term CO2 concentration in classrooms based on the BO–EMD–LSTM model

Predicting the carbon dioxide (CO2) concentration in classrooms is important for undertaking preventive measures before it could harm health and learning efficiency of students. Existing studies have primarily focused on short-term predictions, and it is challenging to make long-term predictions of classroom CO2 concentrations. In this study, a novel divide-and-conquer method namely BO–EMD–LSTM is proposed, which integrates Bayesian optimization (BO), empirical mode decomposition (EMD) and long-short term memory (LSTM). In this model, the EMD algorithm is first applied to decompose the complex CO2 sequence into several subsequences, and the BO algorithm is then employed to determine the hyperparameters of the LSTM models. Our results indicate that the BO–EMD–LSTM method could improve the accuracy of long-term CO2 concentration predictions. Compared with traditional methods, such as LSTM network, back propagation neutral network, and random forest algorithm, this method could reduce the mean absolute error by more than 55% when making predictions 30 min ahead. Such mean absolute error reduction exceeded 60% when the prediction time was 5–15 min. Moreover, when making long-term predictions, the R-squared of BO–EMD–LSTM remained above 95%; however, it dropped to less than 70% when applying other methods. When the error variation is within the acceptable range, merging subsequences with similar frequencies can effectively reduce time consumption and computational burden. The proposed BO–EMD–LSTM method provides an effective tool to predict long-term CO2 concentrations and could aid decision makers implement CO2 control measures. •A hybrid model for the long-term prediction of CO2 concentration is proposed.•The BO–EMD–LSTM model is more accurate and robust compared to other models.•Merging the subsequences can reduce the training time and computational burden.•The CO2 concentration in the last 3 min contributes the most to the results.