Household Energy Consumption Prediction Using the Stationary Wavelet Transform and Transformers
In this paper, we present a new method for forecasting power consumption. Household power consumption prediction is essential to manage and plan energy utilization. This study proposes a new technique using machine learning models based on the stationary wavelet transform (SWT) and transformers to forecast household power consumption in different resolutions. This approach works by leveraging self-attention mechanisms to learn complex patterns and dynamics from household power consumption data. The SWT and its inverse are used to decompose and reconstruct the actual and the forecasted household power consumption data, respectively, and deep transformers are used to forecast the SWT subbands. Experimental findings show that our hybrid approach achieves superior prediction performance compared to the existing power consumption prediction methods.
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