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Short-Term Load Forecasting by Artificial Intelligent Technologies
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Energies2018,11, 1253 Table2.Actual loadandforecastingresults in30November2017 (Unit:MW). Time/h ActualData NILA-CNN LA-CNN CNN SVM TS 0:00 0.374 0.384 0.387 0.361 0.364 0.354 0:30 0.408 0.398 0.422 0.399 0.427 0.432 1:00 0.282 0.282 0.277 0.272 0.292 0.302 1:30 0.262 0.255 0.254 0.271 0.247 0.245 2:00 0.402 0.411 0.414 0.418 0.381 0.431 2:30 0.330 0.321 0.341 0.342 0.315 0.353 3:00 0.269 0.267 0.260 0.258 0.280 0.284 3:30 0.247 0.242 0.244 0.241 0.257 0.261 4:00 0.251 0.254 0.243 0.242 0.257 0.240 4:30 0.253 0.245 0.245 0.262 0.265 0.267 5:00 0.246 0.252 0.255 0.256 0.233 0.226 5:30 0.269 0.276 0.277 0.259 0.254 0.285 6:00 0.503 0.510 0.519 0.510 0.515 0.537 6:30 0.696 0.715 0.719 0.668 0.721 0.743 7:00 0.850 0.832 0.824 0.882 0.889 0.910 7:30 1.003 1.013 0.987 1.038 0.957 1.059 8:00 1.560 1.518 1.507 1.615 1.521 1.653 8:30 1.999 2.055 2.066 2.071 1.901 2.109 9:00 2.100 2.159 2.170 2.025 2.185 1.980 9:30 2.316 2.374 2.387 2.283 2.396 2.450 10:00 3.757 3.687 3.628 3.618 3.932 3.995 10:30 3.761 3.671 3.784 3.806 3.598 4.000 11:00 3.612 3.519 3.486 3.752 3.780 3.928 11:30 3.821 3.923 3.706 3.971 3.883 4.120 12:00 2.635 2.679 2.595 2.736 2.760 2.503 12:30 2.882 2.955 2.783 2.985 3.004 3.043 13:00 3.354 3.403 3.470 3.220 3.153 3.582 13:30 3.832 3.930 3.707 3.686 4.008 4.094 14:00 4.335 4.225 4.189 4.487 4.531 4.643 14:30 3.867 3.876 3.897 4.013 4.028 4.136 15:00 4.063 3.942 3.931 4.121 3.889 4.330 15:30 4.559 4.688 4.707 4.741 4.363 4.879 16:00 4.654 4.708 4.799 4.830 4.438 4.988 16:30 3.819 3.710 3.936 3.906 3.593 4.079 17:00 3.498 3.472 3.379 3.623 3.566 3.303 17:30 2.959 2.886 2.858 2.856 3.081 3.170 18:00 2.647 2.710 2.686 2.595 2.762 2.829 18:30 2.695 2.753 2.783 2.591 2.551 2.846 19:00 2.795 2.773 2.890 2.898 2.651 2.950 19:30 3.158 3.068 3.253 3.044 3.020 3.003 20:00 3.479 3.407 3.594 3.396 3.565 3.684 20:30 4.271 4.381 4.130 4.114 4.449 4.511 21:00 3.577 3.673 3.454 3.437 3.752 3.829 21:30 2.605 2.583 2.625 2.697 2.489 2.787 22:00 2.059 2.006 1.988 2.136 1.980 2.200 22:30 1.831 1.876 1.891 1.904 1.754 1.958 23:00 1.135 1.165 1.170 1.091 1.101 1.071 23:30 0.447 0.438 0.462 0.463 0.428 0.478 365
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Short-Term Load Forecasting by Artificial Intelligent Technologies
Titel
Short-Term Load Forecasting by Artificial Intelligent Technologies
Autoren
Wei-Chiang Hong
Ming-Wei Li
Guo-Feng Fan
Herausgeber
MDPI
Ort
Basel
Datum
2019
Sprache
englisch
Lizenz
CC BY 4.0
ISBN
978-3-03897-583-0
Abmessungen
17.0 x 24.4 cm
Seiten
448
Schlagwörter
Scheduling Problems in Logistics, Transport, Timetabling, Sports, Healthcare, Engineering, Energy Management
Kategorie
Informatik
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Short-Term Load Forecasting by Artificial Intelligent Technologies