feat(Test): 添加能源预测脚本
- 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
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Test/energy-prediction.py
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Test/energy-prediction.py
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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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Test/forecasting.py
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Test/forecasting.py
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import requests
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import zipfile
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import io
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import pandas as pd
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import os
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from sklearn.model_selection import train_test_split
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from sklearn.ensemble import RandomForestClassifier
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from sklearn.metrics import accuracy_score
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from sklearn.svm import SVR
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from sklearn.metrics import mean_squared_error
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from statsmodels.tsa.arima.model import ARIMA
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# 下载和解压ZIP文件
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def download_and_extract_zip(url, extract_to='.'):
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print(f"Downloading ZIP file from {url}")
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response = requests.get(url)
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if response.status_code == 200:
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with zipfile.ZipFile(io.BytesIO(response.content)) as z:
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z.extractall(extract_to)
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print(f"Extracted ZIP file to {extract_to}")
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else:
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print(f"Failed to download ZIP file. Status code: {response.status_code}")
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# 加载CSV数据
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def load_data_from_csv(directory):
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for filename in os.listdir(directory):
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if filename.endswith(".csv"):
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file_path = os.path.join(directory, filename)
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print(f"Loading data from {file_path}")
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return pd.read_csv(file_path)
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print("No CSV files found.")
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return None
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# 发电量预测模型（时间序列分析）
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def generate_forecast(df):
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# 确保使用正确的时间列（这里使用'year_of_capacity_data'列）
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df['year_of_capacity_data'] = pd.to_datetime(df['year_of_capacity_data'], format='%Y', errors='coerce')
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df = df.dropna(subset=['year_of_capacity_data']) # 删除无效的日期数据
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# 设置日期为索引，按年分组
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df.set_index('year_of_capacity_data', inplace=True)
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df = df.resample('A').sum() # 按年重采样并求和
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# 检查是否有生成数据列
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if 'generation_gwh_2013' in df.columns:
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model = ARIMA(df['generation_gwh_2013'], order=(5, 1, 0))
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model_fit = model.fit()
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forecast = model_fit.forecast(steps=30)
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print(f"Forecasted Generation for the next 30 years: {forecast}")
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else:
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print("Generation data not found for ARIMA model.")
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# 设备故障预测模型（分类模型）
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def device_fault_prediction(df):
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# 假设数据包含故障标签'fault'和其他特征
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if 'fault' in df.columns:
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X = df.drop(columns='fault')
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y = df['fault']
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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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model = RandomForestClassifier()
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model.fit(X_train, y_train)
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y_pred = model.predict(X_test)
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print("Device Fault Prediction Accuracy:", accuracy_score(y_test, y_pred))
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else:
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print("Fault column not found in the dataset.")
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# 能源效率优化模型（回归模型）
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def energy_efficiency_optimization(df):
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# 假设数据包含'energy_consumption'和'energy_output'列
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if 'capacity_mw' in df.columns and 'generation_gwh_2013' in df.columns:
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# 用capacity_mw作为能源消耗的代理，generation_gwh_2013作为能源输出
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X = df[['capacity_mw']]
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y = df['generation_gwh_2013']
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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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model = SVR(kernel='rbf')
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model.fit(X_train, y_train)
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y_pred = model.predict(X_test)
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print("Mean Squared Error of Energy Efficiency Prediction:", mean_squared_error(y_test, y_pred))
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else:
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print("Required columns for energy efficiency not found.")
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# 电力市场预测模型（回归模型）
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def power_market_prediction(df):
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if 'generation_gwh_2013' in df.columns and 'capacity_mw' in df.columns:
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X = df[['capacity_mw']]
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y = df['generation_gwh_2013']
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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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model = SVR(kernel='rbf')
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model.fit(X_train, y_train)
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y_pred = model.predict(X_test)
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print("Mean Squared Error of Power Market Price Prediction:", mean_squared_error(y_test, y_pred))
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else:
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print("Required columns for power market prediction not found.")
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# 主函数
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def main():
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url = "https://datasets.wri.org/api/3/action/package_show?id=global-power-plant-database"
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# 获取API响应
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response = requests.get(url)
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if response.status_code == 200:
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data = response.json()
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# 提取资源下载链接
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resources = data["result"]["resources"]
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latest_version_url = None
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for resource in resources:
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if resource["name"].startswith("Version 1.3.0"):
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latest_version_url = resource["url"]
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break
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if latest_version_url:
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# 下载并解压最新版本的ZIP文件
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download_and_extract_zip(latest_version_url, extract_to='./data')
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# 加载CSV文件并查看数据
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df = load_data_from_csv('./data')
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if df is not None:
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print("Loaded data:")
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print(df.head())
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print("Columns in the dataset:", df.columns)
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# 1. 发电量预测
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generate_forecast(df)
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# 2. 设备故障预测
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device_fault_prediction(df)
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# 3. 能源效率优化
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energy_efficiency_optimization(df)
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# 4. 电力市场预测
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power_market_prediction(df)
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else:
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print("No download link found for Version 1.3.0.")
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else:
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print(f"Failed to retrieve data. Status code: {response.status_code}")
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if __name__ == "__main__":
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main()
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