Enhanced Cascade Schemes for Advancing Machine Learning-Based Prediction of Heating and Cooling Loads in Residential Buildings

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:

1. The proposed cascade correction schemes significantly enhance the prediction accuracy of ML models in the context of heating and cooling load forecasts.
2. The use of rational fractions and Wiener polynomial-based nonlinear expansion enables better relationship modeling between independent inputs.
3. The approach improves the overall precision of ML algorithms, contributing to more reliable energy load predictions for residential buildings.
4. 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.
5. The proposed modifications outperform existing methods, showcasing their practical applicability in smart city and building energy management applications.