An Image-Based Disaggregation Study of Time Series Energy Data Using Gramian Angular Field

Non-intrusive Load Monitoring (NILM) proves effective in disaggregating energy consumption data for individual household appliances. Traditional NILM techniques rely on one-dimensional (1D) time series signal (TSS) analysis to predict the energy usage of distinct appliances. Nonetheless, these approaches have limitations in handling data with non-linear behavior and complex patterns. To address this challenge, prior studies have proposed converting 1D TSS into two-dimensional (2D) images using techniques like Gramian Angular Field (GAF), Markov Transition Field (MTF), and Recurrence Plot (RP). Recent research underscores the effectiveness of the Gramian Angular Summation Field (GASF) and Gramian Angular Difference Field (GADF), both variants of GAF, in the NILM domain. This study assesses the performance of three distinct 1D to 2D conversion approaches for energy disaggregation: GADF, GASF, and an enhanced hybrid method integrating GADF and GASF. Performance evaluation utilizes matrices, including accuracy, precision, recall, and the F1 score. The experiments are conducted on a publicly accessible real-world energy dataset encompassing different appliances. The results demonstrate that the combined GADF and GASF method introduced in this study outperforms their individual counterparts with better performance and faster convergence, particularly in the context of energy disaggregation. The findings offer valuable insights for advancing the development of more accurate and efficient NILM techniques.