From 1a8cbb5f063967fa24d6530a9b0d70457326d3fb Mon Sep 17 00:00:00 2001
From: haotian <2421912570@qq.com>
Date: Tue, 8 Apr 2025 17:09:13 +0800
Subject: [PATCH] =?UTF-8?q?=E5=AE=8C=E6=88=90--=E9=A2=84=E6=B5=8B=E9=A3=8E?=
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---
 doc/各领域应用(附带数据集).txt                |  46 +++++++++++++++++-
 .../__pycache__/data_manager.cpython-39.pyc   | Bin 18228 -> 18254 bytes
 2 files changed, 45 insertions(+), 1 deletion(-)

diff --git a/doc/各领域应用(附带数据集).txt b/doc/各领域应用(附带数据集).txt
index 5523c48..8ede445 100644
--- a/doc/各领域应用(附带数据集).txt
+++ b/doc/各领域应用(附带数据集).txt
@@ -44,10 +44,54 @@
     (1)基于天气与历史数据预测风力发电量
     (2)XGBoost,LSTM,Prophet时间序列模型
     (3)Wind Turbine Scada Dataset
+        https://www.kaggle.com/datasets/berkerisen/wind-turbine-scada-dataset/discussion/520518
+        
+    数据集解析:
+        数据集路径:dataset/dataset_raw/WindTurbine/wind_turbine_scada.csv
+        列:
+            Date/Time,LV ActivePower (kW),Wind Speed (m/s),Theoretical_Power_Curve (KWh),Wind Direction (°)
+            注: LV ActivePower 为实际电机功率, Theoretical_Power_Curve为理论上的电机功率.
+                只有 Wind Speed 和 Wind Direction 有用. LV ActivePower 作为数据真值使用.
+
+        数据集处理:
+            重命名列, 将真值放到最后,去除 date和 theoretical 两列.
+            去除真值为0的行.
+            wind_0.csv
+
+            标准缩放--结果分析:
+                RandomForestRegressor:mae 123.
+                XGBRegressor:mae 116
+                LGBMRegressor: mae 113
+                AdaBoostRegressor mae:277
+            不进行缩放--结果分析
+                RandomForestRegressor:mae 123.861827
+                XGBRegressor:mae 116.247
+                LGBMRegressor: mae 112.53
+                AdaBoostRegressor mae:217.83
+
+            在上述处理基础上,计算相邻两天风速的差值gust,把gust作为新的特征列.
+            wind_1.csv
+
+            标准缩放--结果分析:
+                RandomForestRegressor:mae 122.
+                XGBRegressor:mae 121.9
+                LGBMRegressor: mae 115.50
+                AdaBoostRegressor mae:321.77
+            原始数据--结果分析:
+                RandomForestRegressor:mae 122.64
+                XGBRegressor:mae 121.93
+                LGBMRegressor: mae 115.50
+                AdaBoostRegressor mae:253.43
+
+
+
+
+
 3.化工行业-过程异常检测
     (1)分类化工生产过程中的异常状态(泄露,温度失控)
     (2)SVM,孤立森林,Autoencoder
-    (3)ennessee Eastman Process (TEP)：模拟化工厂故障的多变量时序数据。
+    (3)Tennessee Eastman Process (TEP)：模拟化工厂故障的多变量时序数据。
+        https://github.com/camaramm/tennessee-eastman-profBraatz
 4.物流与供应链-需求预测
     (1)预测未来产品需求量以优化库存
     (2)ARIMA,LightGBM, Transformer
diff --git a/function/__pycache__/data_manager.cpython-39.pyc b/function/__pycache__/data_manager.cpython-39.pyc
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