Are seasons defined correctly in energy sector? Evaluating seasonal classification impacts on utility analysis and forecasting using data from a residential building in Tokyo

Global warming is altering seasonal lengths and characteristics, challenging the validity of traditional fixed-calendar classifications for climate-sensitive analyses. Accurate seasonal boundaries are critical for energy and resource consumption analysis, yet conventional fixed three-month frameworks may inadequately capture actual consumption patterns under changing climatic conditions. This study compares the traditional calendar-based method (TRD) with a novel Sinusoidal Temperature Curve-based approach (STC) that aligns seasonal boundaries with actual outdoor temperature patterns. Using two years of electricity, gas, and water consumption data from 340 apartments in a Japanese condominium, we evaluated both methods on their ability to create statistically distinct seasonal groups and improve forecasting accuracy. Statistical analysis revealed that the STC method creates significantly more distinct seasonal categories for electricity and gas consumption, achieving 67.3% and 4.8% higher F-values respectively from F- test compared to TRD. Water consumption exhibited negligible seasonal influence under both methods, indicating independence from thermal cycles. Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) forecasting models trained on STC-defined seasons consistently outperformed TRD-based models across all utilities, achieving substantial reductions in Root Mean Squared Error (−10.6%) and Mean Absolute Error (−11.9%), demonstrating enhanced predictive reliability. These findings provide compelling evidence for adopting climate-responsive seasonal frameworks for load forecasting, policy design, and behavioral analysis. Beyond energy applications, this methodology benefits thermal comfort assessment, agricultural planning, and public health research. This study represents a paradigm shift from static calendar-centric classifications toward dynamic, climate-intelligent frameworks essential for effective utility management and adaptation of sustainable strategies in a warming world.