  Sustainability requires increased energy efficiency with minimal waste. The
future power systems should thus provide high levels of flexibility iin
controling energy consumption. Precise projections of future energy demand/load
at the aggregate and on the individual site levels are of great importance for
decision makers and professionals in the energy industry. Forecasting energy
loads has become more advantageous for energy providers and customers, allowing
them to establish an efficient production strategy to satisfy demand. This
study introduces two hybrid cascaded models for forecasting multistep household
power consumption in different resolutions. The first model integrates
Stationary Wavelet Transform (SWT), as an efficient signal preprocessing
technique, with Convolutional Neural Networks and Long Short Term Memory
(LSTM). The second hybrid model combines SWT with a self-attention based neural
network architecture named transformer. The major constraint of using
time-frequency analysis methods such as SWT in multistep energy forecasting
problems is that they require sequential signals, making signal reconstruction
problematic in multistep forecasting applications.The cascaded models can
efficiently address this problem through using the recursive outputs.
Experimental results show that the proposed hybrid models achieve superior
prediction performance compared to the existing multistep power consumption
prediction methods. The results will pave the way for more accurate and
reliable forecasting of household power consumption.
