Skip to Main Content

As mentioned earlier, for decomposition level 3 of an input variable, there are four probable component inputs (i.e., D1, D2, D3, and A3), which were used as model inputs in this study (as shown in Table 1). The probable inputs for decomposition levels 1 and 2 of an input variable are A1 and D1 (for level 1) and D1, D2, and A2 (for level 2). For Data #1, the PLC technique selected four inputs and thus there will be a total of 8, 12, and 16 model inputs for the three different levels of decomposition, respectively. The sensitivity analyses for the three levels of decomposition for Data #1 are shown in Table 3. As can be seen from this table, level 2 of decomposition produced the best forecasting performance based on the MSE and E values, whereas level 3 is slightly better than level 2 decomposition based on the MAPE value.

Table 3

Performance of the FFNN model on the testing dataset of Data #1 for the three levels of input decomposition

  Performance indicators for data #1
PLC-Wavelet inputshnMSEMAPEE
Level 1 (8 inputs) 12 0.021 81.4 0.743 
Level 2 (12 inputs) 20 0.018 78.9 0.786 
Level 3 (16 inputs) 16 0.022 77.8 0.732 
  Performance indicators for data #1
PLC-Wavelet inputshnMSEMAPEE
Level 1 (8 inputs) 12 0.021 81.4 0.743 
Level 2 (12 inputs) 20 0.018 78.9 0.786 
Level 3 (16 inputs) 16 0.022 77.8 0.732 

hn, number of hidden neurons.

Close Modal

or Create an Account

Close Modal
Close Modal