CorrDQN-FS: A two-stage feature selection method for energy consumption prediction via deep reinforcement learning

Accurate energy consumption prediction is crucial for optimizing energy usage and achieving sustainable development goals, especially in the field of building engineering. However, constructing accurate prediction models faces challenges in feature selection, given the large pool of candidate features and their intricate relationships. To address this issue, we propose a novel two-stage feature selection method called CorrDQN-FS for energy consumption prediction. CorrDQN-FS leverages the Pearson correlation coefficient to rank features and eliminates features with obvious irrelevant coefficients. Subsequently, the method integrates deep reinforcement learning techniques to optimize the feature selection process, considering the complex nonlinear relationships among features. By combining correlation analysis and deep reinforcement learning, CorrDQN-FS aims to enhance the accuracy of energy consumption prediction models. To evaluate the effectiveness of CorrDQN-FS, we conduct experiments using real-world data. We compare the performance of CorrDQN-FS combined with different prediction methods, including DF-DDPG, DF-DQN, LSTM, RNN, MLR and DT, to access its ability to improve energy consumption prediction accuracy. Additionally, we compare CorrDQN-FS with other feature selection methods, including the Pearson correlation coefficient, Recursive Feature Elimination (RFE), Mutual Information, and Gradient Boosting Decision Trees (GBDT), to highlight its advantages in feature selection. The experimental results demonstrate that CorrDQN-FS outperforms other feature selection techniques, effectively enhancing the accuracy of energy consumption prediction. This advancement holds significant potential for optimizing energy management and furthering sustainable building engineering.