Analyzing different household energy use patterns using clustering and machine learning

As the residential sector in the United States holds significant energy-saving potential, identifying and analyzing different energy use patterns is essential for households to assess their energy use intensity levels and understand their energy consumption practices. Most studies fail to differentiate between household energy use intensity levels when analyzing energy consumption practices and require actual long-term energy use data. To address these gaps, this study clustered households in the United States from the 2020 Residential Energy Consumption Survey into groups with different energy use intensity levels, then developed a classification model based on this energy use intensity level-labeled dataset for predicting household energy use intensity levels. K-means performed best among the five clustering approaches, which categorized households into low, medium, and high energy use patterns. Statistical analysis revealed significant differences in energy use intensity across the three energy use patterns, showing that high patterns had a median energy use intensity 1.88 times higher than medium patterns and 3.58 times higher than low patterns. Three machine learning algorithms were used to develop classification models, where the eXtreme Gradient Boosting-based model performed best, with an average accuracy of 0.85. This classification model can reliably assess household energy use intensity levels within seconds using only a few household features, enhancing household energy-saving motivation and addressing challenges in long-term data collection. Finally, SHapley Additive exPlanations was applied to the classification model to analyze different energy use patterns, providing targeted energy-saving priorities for three energy use patterns, and enabling more effective energy management strategies.