Household Energy Consumption Forecasting based on Adaptive Signal Decomposition Enhanced iTransformer Network

Abstract

With the presence of volatility and noise in the energy consumption data, existing energy consumption forecasting methods have difficulties in achieving satisfactory forecasting results from multiple perspectives, including accuracy, robustness, generalization, and efficiency. This study introduces a novel energy consumption forecasting framework that combines the adaptive signal decomposition method and iTransformer (ASSA-iTransformer) to tackle the above-mentioned difficulties. Each subsequence obtained by ASSA decomposition was fed into the iTransformer framework, and deep-level time series information was extracted. Compared to state-of-the-art methods across various evaluation dimensions, ASSA-iTransformer has improved the average mean absolute error (MAE) by 23.7%, root mean square error (RMSE) by 27.6%, and mean absolute percentage error (MAPE) by 16.4%. Moreover, the R2 values on five real-world household energy consumption datasets are close to 1, demonstrating overall outstanding performance.