Research Article
Short-Term Load Forecasting Based on Frequency Domain Decomposition and Deep Learning
Table 1
The forecasting error calculation results of four methods.
| Time sampling point (15 min) | HFDA-LSTM | EMD-LSTM | LSTM | RNN | MAPE (%) | RMSE (MW) | MAPE (%) | RMSE (MW) | MAPE (%) | RMSE (MW) | MAPE (%) | RMSE (MW) |
| 1–96 | 0.95 | 7.38 | 2.23 | 14.93 | 2.36 | 21.36 | 3.01 | 22.23 | 97–192 | 0.92 | 6.27 | 2.09 | 13.72 | 2.55 | 22.31 | 3.24 | 23.18 | 193–288 | 0.82 | 5.68 | 2.26 | 14.02 | 2.13 | 20.06 | 2.56 | 19.73 | 289–384 | 0.80 | 5.88 | 2.24 | 15.12 | 2.40 | 21.47 | 2.85 | 22.97 | 385–480 | 0.88 | 6.08 | 2.06 | 13.59 | 2.11 | 19.98 | 2.78 | 21.56 | 481–576 | 1.55 | 8.47 | 1.77 | 10.99 | 3.77 | 29.66 | 3.22 | 21.37 | 577–672 | 1.35 | 7.81 | 2.43 | 13.87 | 3.56 | 28.30 | 3.54 | 24.89 | 1–672 | 1.04 | 6.87 | 2.15 | 13.81 | 2.70 | 23.31 | 3.03 | 22.33 |
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