Advancing ensemble learning techniques for residential building electricity consumption forecasting: Insight from explainable artificial intelligence.

ABSTRACT: Accurate electricity consumption forecasting in residential buildings has a direct impact on energy efficiency and cost management, making it a critical component of sustainable energy practices. Decision tree-based ensemble learning techniques are particularly effective for this task due to their ability to process complex datasets with high accuracy. Furthermore, incorporating explainable artificial intelligence into these predictions provides clarity and interpretability, allowing energy managers and homeowners to make informed decisions that optimize usage and reduce costs. This study comparatively analyzes decision tree-ensemble learning techniques augmented with explainable artificial intelligence for transparency and interpretability in residential building energy consumption foreca

SUBMITTER: Moon J 

PROVIDER: S-EPMC11563398 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature