This research focuses on improving the accuracy of machine learning (ML) methods for predicting heating and cooling loads in residential buildings, a critical aspect of energy-efficient building design. The study introduces two enhancements to cascade correction schemes in ML algorithms, which aim to refine the modeling process. These enhancements involve establishing a cascade of two ML algorithms using rational fractions and employing Wiener polynomial-based nonlinear expansion to improve the relationship modeling between independent inputs. The method is tested on a real-world dataset, demonstrating significant improvements in prediction accuracy over existing methods.
Takeaways:
- The proposed cascade correction schemes significantly enhance the prediction accuracy of ML models in the context of heating and cooling load forecasts.
- The use of rational fractions and Wiener polynomial-based nonlinear expansion enables better relationship modeling between independent inputs.
- The approach improves the overall precision of ML algorithms, contributing to more reliable energy load predictions for residential buildings.
- The research underscores the potential of advanced ML techniques in the design phase of energy-efficient buildings, particularly in improving predictive modeling for climate control.
- The proposed modifications outperform existing methods, showcasing their practical applicability in smart city and building energy management applications.
