load_RNN
所属分类：界面编程
开发工具：Python
文件大小：7204KB
下载次数：23
上传日期：2020-04-12 10:50:06
上 传 者：
龙的独白式
说明： python 电力负荷预测，rnn版本，python环境
(Python power load forecasting, RNN version, python environment)
文件列表:
load_RNN\draw_data.py (220, 2020-01-01)
load_RNN\Figure_1.png (262436, 2020-01-01)
load_RNN\jianmo.py (5485, 2020-01-01)
load_RNN\log_history\2.log\events.out.tfevents.1501239969.songling-14Z970-G-AA52C (40487138, 2020-01-01)
load_RNN\log_history\4.log\events.out.tfevents.1501239965.songling-14Z970-G-AA52C (15118217, 2020-01-01)
load_RNN\log_history\supervisor.log\events.out.tfevents.1501241437.songling-14Z970-G-AA52C (45075180, 2020-01-01)
load_RNN\log_history\supervisor.log\events.out.tfevents.1501244672.songling-14Z970-G-AA52C (9007093, 2020-01-01)
load_RNN\log_history\supervisor.log\events.out.tfevents.1501244808.songling-14Z970-G-AA52C (16140864, 2020-01-01)
load_RNN\normalize.py (1471, 2020-01-01)
load_RNN\output\steps=1000-MAPE=0.0561.csv (18230, 2020-01-01)
load_RNN\output\steps=1000-MAPE=0.0580.csv (18231, 2020-01-01)
load_RNN\output\steps=1000-MAPE=0.0589.csv (18232, 2020-01-01)
load_RNN\output\steps=10000-MAPE=0.0449.csv (18221, 2020-01-01)
load_RNN\output\steps=10000-MAPE=0.0529 (18227, 2020-01-01)
load_RNN\output\steps=10000-MAPE=0.0542.csv (18223, 2020-01-01)
load_RNN\output\steps=10000-MAPE=0.0555.csv (18238, 2020-01-01)
load_RNN\output\steps=10500-MAPE=0.0462.csv (18235, 2020-01-01)
load_RNN\output\steps=10500-MAPE=0.0566.csv (18240, 2020-01-01)
load_RNN\output\steps=11000-MAPE=0.0465.csv (18238, 2020-01-01)
load_RNN\output\steps=11000-MAPE=0.0564.csv (18227, 2020-01-01)
load_RNN\output\steps=11500-MAPE=0.0502.csv (18233, 2020-01-01)
load_RNN\output\steps=11500-MAPE=0.0550.csv (18231, 2020-01-01)
load_RNN\output\steps=12000-MAPE=0.0495.csv (18233, 2020-01-01)
load_RNN\output\steps=12000-MAPE=0.0535.csv (18218, 2020-01-01)
load_RNN\output\steps=12500-MAPE=0.0514.csv (18236, 2020-01-01)
load_RNN\output\steps=12500-MAPE=0.0578.csv (18239, 2020-01-01)
load_RNN\output\steps=13000-MAPE=0.0531.csv (18223, 2020-01-01)
load_RNN\output\steps=13000-MAPE=0.0557.csv (18233, 2020-01-01)
load_RNN\output\steps=13500-MAPE=0.0519.csv (18221, 2020-01-01)
load_RNN\output\steps=13500-MAPE=0.0594.csv (18241, 2020-01-01)
load_RNN\output\steps=14000-MAPE=0.0529.csv (18233, 2020-01-01)
load_RNN\output\steps=14000-MAPE=0.0587.csv (18216, 2020-01-01)
load_RNN\output\steps=14500-MAPE=0.0538.csv (18255, 2020-01-01)
load_RNN\output\steps=1500-MAPE=0.0508.csv (18225, 2020-01-01)
load_RNN\output\steps=1500-MAPE=0.0523.csv (18246, 2020-01-01)
load_RNN\output\steps=1500-MAPE=0.0524.csv (18227, 2020-01-01)
load_RNN\output\steps=1500-MAPE=0.0530.csv (18215, 2020-01-01)
load_RNN\output\steps=1500-MAPE=0.0539.csv (18233, 2020-01-01)
load_RNN\output\steps=1500-MAPE=0.0567.csv (18213, 2020-01-01)
load_RNN\output\steps=1500-MAPE=0.0569.csv (18240, 2020-01-01)
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