Effects of Operational-State Features on One-Week-Ahead Building Electricity Demand Forecasting Using a Temporal Fusion Transformer

Abstract

Accurate one-week-ahead building electricity demand forecasting is essential for building energy management, yet representing future building operational characteristics remains challenging because such information is generally unavailable in advance. This study investigates the effectiveness of representing building operational characteristics using cluster labels derived from daily electricity consumption patterns for medium-term electricity demand forecasting. Cluster labels obtained by k-means clustering were incorporated as operational-state features into a Temporal Fusion Transformer (TFT) together with historical electricity consumption, meteorological variables, and calendar information. Forecasting performance was evaluated for a training facility and three university buildings using walk-forward validation under different feature reference periods. Under an idealized information condition in which meteorological variables and cluster labels corresponding to the forecasting period were provided as known future inputs, this forecasting pattern achieved the highest accuracy for all investigated buildings. Under the same idealized condition, variable importance analysis indicated that the cluster label exhibited the highest importance among the known future inputs, exceeding that of calendar variables and most meteorological variables. In addition, the TFT outperformed Long Short-Term Memory (LSTM) and Multi-Layer Perceptron (MLP) models. These findings indicate the potential value of the proposed operational-state representation for improving one-week-ahead building electricity demand forecasting and provide interpretable insights into the contribution of operational-state features.