Hourly Building Energy Consumption Prediction Using a Training Sample Selection Method Based on Key Feature Search

For the management of building operations, hourly building energy consumption prediction (HBECP) is critical. Many factors, such as energy types, expected day intervals, and acquired feature types, significantly impact HBECP. However, the existing training sample selection methods, especially during transitional seasons, are unable to properly adapt to changes in operational conditions. The key feature search selection (KFSS) approach is proposed in this study. This technique ensures a quick response to changes in the parameters of the predicted day while enhancing the model's accuracy, stability, and generalization. The best training sample set is found dynamically based on the similarity between the feature on the projected day and the historical data, and feature scenario analysis is used to make the most of the acquired data features. The hourly actual data in two years are applied to a major office building in Zhuhai, China as a case study. The findings reveal that, as compared to the original methods, the KFSS method can track daily load well and considerably enhance prediction accuracy. The suggested training sample selection approach can enhance the accuracy of prediction days by 14.5% in spring and 4.9% in autumn, according to the results. The proposed feature search and feature extraction strategy are valuable for enhancing the robustness of data-driven models for HBECP.