fly6516
6756de70cc
- 新增 energy-prediction.py 脚本，实现全球发电厂数据库的下载、处理和分析 - 使用随机森林回归模型预测发电量，并评估模型性能- 可视化预测结果与实际值的对比 - 设置中文字体支持，确保图表显示正常
95 lines
3.5 KiB
Python
95 lines
3.5 KiB
Python
import requests
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import zipfile
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import os
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import pandas as pd
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from sklearn.model_selection import train_test_split
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from sklearn.ensemble import RandomForestRegressor
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from sklearn.metrics import mean_squared_error
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from sklearn.preprocessing import LabelEncoder
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import matplotlib.pyplot as plt
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import matplotlib
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def main():
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# 设置支持中文的字体（例如：SimHei 字体）
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matplotlib.rcParams['font.sans-serif'] = ['SimHei'] # 或使用 'Microsoft YaHei'
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matplotlib.rcParams['axes.unicode_minus'] = False # 防止负号显示为方块
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# 获取数据资源 URL（选择最新版本的 ZIP 文件）
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url = "https://datasets.wri.org/private-admin/dataset/53623dfd-3df6-4f15-a091-67457cdb571f/resource/66bcdacc-3d0e-46ad-9271-a5a76b1853d2/download/globalpowerplantdatabasev130.zip"
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# 下载 ZIP 文件
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response = requests.get(url)
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zip_path = "global_power_plant_data.zip"
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with open(zip_path, 'wb') as file:
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file.write(response.content)
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# 解压 ZIP 文件
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with zipfile.ZipFile(zip_path, 'r') as zip_ref:
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zip_ref.extractall("data")
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# 确保解压后的文件名和路径
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csv_file = "data/global_power_plant_database.csv"
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if not os.path.exists(csv_file):
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print(f"错误: 文件 {csv_file} 不存在!")
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return
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# 加载解压后的 CSV 数据
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data = pd.read_csv(csv_file, dtype={'other_fuel3': str})
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# data = pd.read_csv(csv_file)
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# 数据预处理
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data.fillna(0, inplace=True) # 用 0 填充缺失值
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# 确保 'owner' 列的类型统一为字符串类型
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data['owner'] = data['owner'].astype(str)
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# 对分类列进行编码（例如 primary_fuel, owner）
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label_encoder = LabelEncoder()
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data['primary_fuel'] = label_encoder.fit_transform(data['primary_fuel'])
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data['owner'] = label_encoder.fit_transform(data['owner'])
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# 确保发电量列存在并计算总发电量
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generation_columns = ['generation_gwh_2013', 'generation_gwh_2014', 'generation_gwh_2015',
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'generation_gwh_2016', 'generation_gwh_2017']
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# 确保所有发电量列都存在
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missing_cols = [col for col in generation_columns if col not in data.columns]
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if missing_cols:
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print(f"警告: 缺少以下列: {', '.join(missing_cols)}")
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return
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# 聚合不同年份的发电数据
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data['total_generation'] = data[generation_columns].sum(axis=1)
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# 选择特征（X）和目标变量（y）
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X = data[['capacity_mw', 'latitude', 'longitude', 'primary_fuel', 'total_generation']] # 示例特征
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y = data['generation_gwh_2017'] # 预测目标：2017年的发电量
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# 将数据分割为训练集和测试集
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
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# 初始化并训练随机森林回归模型
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model = RandomForestRegressor(n_estimators=100, random_state=42)
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model.fit(X_train, y_train)
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# 在测试集上进行预测
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y_pred = model.predict(X_test)
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# 评估模型
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mse = mean_squared_error(y_test, y_pred)
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rmse = mse ** 0.5
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print(f"均方根误差（RMSE）：{rmse}")
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# 可视化预测值与实际值，使用不同颜色标记
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plt.scatter(y_test, y_pred, color='blue', label='预测发电量', alpha=0.6)
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plt.scatter(y_test, y_test, color='red', label='实际发电量', alpha=0.6)
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plt.xlabel('实际发电量 (GWh)')
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plt.ylabel('预测发电量 (GWh)')
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plt.title('实际 vs 预测发电量')
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plt.legend()
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plt.show()
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if __name__ == "__main__":
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main()
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