风力发电、光伏发电数据、用电负荷数据集，用于仿真分析，预测等
。
[1]光伏数据，光伏出力
年光伏数据集，包含光伏发电功率、多种类型光照辐射强度数据（DNI，DHI，GHI），附带多种数据，
可用于光伏场景生成与缩减、光伏特性分析、光伏优化调度等。
[2]光伏预测功率数据2007-2020
光伏预测功率数据，包括预测辐射强度和预测出力，步长15分钟。
[3]风电机组和风电场数据2019 1.1-1.15
年风电机组出力数据集，5分钟一个点，可用于风电场景生成与缩减，优化配置。
年地区风电场数据集，步长5分钟。
负荷数据2016-2018
年负荷数据集，步长15分钟，可用于负荷预测、聚类、仿真分析。
以下内容及代码仅供参考：
好的，您提供了多个不同类型的数据集，这些数据集可以用于仿真分析、预测等多种应用场景。下面我将详细介绍如何应用这些数据集，并提供一些示例代码和步骤来帮助您进行数据分析和建模。
数据集概述
-
光伏数据（光伏发电功率、多种类型光照辐射强度数据）
- 包含：光伏发电功率、DNI（直接法线辐照度）、DHI（漫射水平辐照度）、GHI（全球水平辐照度）
- 应用场景：光伏场景生成与缩减、光伏特性分析、光伏优化调度
-
光伏预测功率数据（2007-2020）
- 包含：预测辐射强度和预测出力
- 步长：15分钟
- 应用场景：光伏功率预测
-
风电机组和风电场数据（2019 1.1-1.15）
- 包含：年风电机组出力数据（步长5分钟）、地区风电场数据（步长5分钟）
- 应用场景：风电场景生成与缩减、优化配置
-
负荷数据（2016-2018）
- 包含：用电负荷数据
- 步长：15分钟
- 应用场景：负荷预测、聚类、仿真分析
环境准备
首先，我们需要安装必要的库。您可以使用以下命令来设置环境：
pip install pandas numpy matplotlib scikit-learn statsmodels pyarrow prophet
数据加载与预处理
我们将使用 pandas 来加载和预处理数据。以下是数据加载和预处理的脚本 data_preprocessing.py:
[<title="Data Preprocessing Script for Wind, Solar, and Load Data">]
import pandas as pd
import os
# Define paths
base_path = 'datasets'
solar_data_path = os.path.join(base_path, 'solar_power_data.csv')
solar_forecast_path = os.path.join(base_path, 'solar_forecast_data.csv')
wind_turbine_path = os.path.join(base_path, 'wind_turbine_data.csv')
load_data_path = os.path.join(base_path, 'load_data.csv')
# Load solar power data
solar_power_df = pd.read_csv(solar_data_path, parse_dates=['timestamp'], index_col='timestamp')
print("Solar Power Data Loaded:")
print(solar_power_df.head())
# Load solar forecast data
solar_forecast_df = pd.read_csv(solar_forecast_path, parse_dates=['timestamp'], index_col='timestamp')
print("\nSolar Forecast Data Loaded:")
print(solar_forecast_df.head())
# Load wind turbine data
wind_turbine_df = pd.read_csv(wind_turbine_path, parse_dates=['timestamp'], index_col='timestamp')
print("\nWind Turbine Data Loaded:")
print(wind_turbine_df.head())
# Load load data
load_data_df = pd.read_csv(load_data_path, parse_dates=['timestamp'], index_col='timestamp')
print("\nLoad Data Loaded:")
print(load_data_df.head())
# Save preprocessed data
preprocessed_base_path = 'preprocessed_datasets'
os.makedirs(preprocessed_base_path, exist_ok=True)
solar_power_df.to_csv(os.path.join(preprocessed_base_path, 'solar_power_data.csv'))
solar_forecast_df.to_csv(os.path.join(preprocessed_base_path, 'solar_forecast_data.csv'))
wind_turbine_df.to_csv(os.path.join(preprocessed_base_path, 'wind_turbine_data.csv'))
load_data_df.to_csv(os.path.join(preprocessed_base_path, 'load_data.csv'))
print("\nPreprocessed data saved to:", preprocessed_base_path)
数据可视化
接下来，我们可以通过可视化来更好地理解数据。以下是数据可视化的脚本 data_visualization.py:
[<title="Data Visualization Script for Wind, Solar, and Load Data">]
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# Define paths
preprocessed_base_path = 'preprocessed_datasets'
solar_power_path = os.path.join(preprocessed_base_path, 'solar_power_data.csv')
solar_forecast_path = os.path.join(preprocessed_base_path, 'solar_forecast_data.csv')
wind_turbine_path = os.path.join(preprocessed_base_path, 'wind_turbine_data.csv')
load_data_path = os.path.join(preprocessed_base_path, 'load_data.csv')
# Load preprocessed data
solar_power_df = pd.read_csv(solar_power_path, parse_dates=['timestamp'], index_col='timestamp')
solar_forecast_df = pd.read_csv(solar_forecast_path, parse_dates=['timestamp'], index_col='timestamp')
wind_turbine_df = pd.read_csv(wind_turbine_path, parse_dates=['timestamp'], index_col='timestamp')
load_data_df = pd.read_csv(load_data_path, parse_dates=['timestamp'], index_col='timestamp')
# Plot solar power data
plt.figure(figsize=(14, 7))
plt.plot(solar_power_df.index, solar_power_df['power_output'], label='Power Output', color='orange')
plt.title('Solar Power Output Over Time')
plt.xlabel('Timestamp')
plt.ylabel('Power Output (kW)')
plt.legend()
plt.grid(True)
plt.show()
# Plot solar forecast data
plt.figure(figsize=(14, 7))
plt.plot(solar_forecast_df.index, solar_forecast_df['predicted_radiance'], label='Predicted Radiance', color='blue')
plt.plot(solar_forecast_df.index, solar_forecast_df['predicted_power'], label='Predicted Power', color='green')
plt.title('Solar Forecast Data Over Time')
plt.xlabel('Timestamp')
plt.ylabel('Value')
plt.legend()
plt.grid(True)
plt.show()
# Plot wind turbine data
plt.figure(figsize=(14, 7))
plt.plot(wind_turbine_df.index, wind_turbine_df['turbine_output'], label='Turbine Output', color='purple')
plt.title('Wind Turbine Output Over Time')
plt.xlabel('Timestamp')
plt.ylabel('Power Output (kW)')
plt.legend()
plt.grid(True)
plt.show()
# Plot load data
plt.figure(figsize=(14, 7))
plt.plot(load_data_df.index, load_data_df['load'], label='Electricity Load', color='red')
plt.title('Electricity Load Over Time')
plt.xlabel('Timestamp')
plt.ylabel('Load (kW)')
plt.legend()
plt.grid(True)
plt.show()
时间序列分解
我们可以使用时间序列分解来分析数据的趋势、季节性和残差部分。以下是时间序列分解的脚本 time_series_decomposition.py:
[<title="Time Series Decomposition Script for Wind, Solar, and Load Data">]
import pandas as pd
import matplotlib.pyplot as plt
from statsmodels.tsa.seasonal import seasonal_decompose
# Define paths
preprocessed_base_path = 'preprocessed_datasets'
solar_power_path = os.path.join(preprocessed_base_path, 'solar_power_data.csv')
solar_forecast_path = os.path.join(preprocessed_base_path, 'solar_forecast_data.csv')
wind_turbine_path = os.path.join(preprocessed_base_path, 'wind_turbine_data.csv')
load_data_path = os.path.join(preprocessed_base_path, 'load_data.csv')
# Load preprocessed data
solar_power_df = pd.read_csv(solar_power_path, parse_dates=['timestamp'], index_col='timestamp')
solar_forecast_df = pd.read_csv(solar_forecast_path, parse_dates=['timestamp'], index_col='timestamp')
wind_turbine_df = pd.read_csv(wind_turbine_path, parse_dates=['timestamp'], index_col='timestamp')
load_data_df = pd.read_csv(load_data_path, parse_dates=['timestamp'], index_col='timestamp')
# Resample data to daily frequency for decomposition
solar_daily = solar_power_df.resample('D').mean()
wind_daily = wind_turbine_df.resample('D').mean()
load_daily = load_data_df.resample('D').mean()
# Perform time series decomposition for solar power
decomposition_solar = seasonal_decompose(solar_daily['power_output'], model='additive', period=365)
fig = decomposition_solar.plot()
fig.suptitle('Solar Power Daily Decomposition')
plt.show()
# Perform time series decomposition for wind turbine
decomposition_wind = seasonal_decompose(wind_daily['turbine_output'], model='additive', period=365)
fig = decomposition_wind.plot()
fig.suptitle('Wind Turbine Daily Decomposition')
plt.show()
# Perform time series decomposition for load
decomposition_load = seasonal_decompose(load_daily['load'], model='additive', period=365)
fig = decomposition_load.plot()
fig.suptitle('Load Daily Decomposition')
plt.show()
负荷预测
我们可以使用Prophet来进行负荷预测。以下是负荷预测的脚本 load_forecasting.py:
[<title="Load Forecasting Script using Prophet">]
import pandas as pd
from fbprophet import Prophet
import matplotlib.pyplot as plt
# Define paths
preprocessed_base_path = 'preprocessed_datasets'
load_data_path = os.path.join(preprocessed_base_path, 'load_data.csv')
# Load preprocessed data
load_data_df = pd.read_csv(load_data_path, parse_dates=['timestamp'], index_col='timestamp')
# Prepare data for Prophet
df_prophet = load_data_df.reset_index().rename(columns={'timestamp': 'ds', 'load': 'y'})
# Initialize and fit the model
model = Prophet(daily_seasonality=False, weekly_seasonality=True, yearly_seasonality=True)
model.fit(df_prophet)
# Make future predictions
future = model.make_future_dataframe(periods=30*24*4, freq='15min') # Predict for the next month
forecast = model.predict(future)
# Plot the forecast
fig = model.plot(forecast)
plt.title('Load Forecast Using Prophet')
plt.xlabel('Date')
plt.ylabel('Load (kW)')
plt.show()
# Plot components
fig_components = model.plot_components(forecast)
plt.show()
光伏功率预测
我们可以使用Prophet来进行光伏功率预测。以下是光伏功率预测的脚本 solar_power_forecasting.py:
[<title="Solar Power Forecasting Script using Prophet">]
import pandas as pd
from fbprophet import Prophet
import matplotlib.pyplot as plt
# Define paths
preprocessed_base_path = 'preprocessed_datasets'
solar_power_path = os.path.join(preprocessed_base_path, 'solar_power_data.csv')
# Load preprocessed data
solar_power_df = pd.read_csv(solar_power_path, parse_dates=['timestamp'], index_col='timestamp')
# Prepare data for Prophet
df_prophet = solar_power_df.reset_index().rename(columns={'timestamp': 'ds', 'power_output': 'y'})
# Initialize and fit the model
model = Prophet(daily_seasonality=False, weekly_seasonality=True, yearly_seasonality=True)
model.fit(df_prophet)
# Make future predictions
future = model.make_future_dataframe(periods=30*24*4, freq='15min') # Predict for the next month
forecast = model.predict(future)
# Plot the forecast
fig = model.plot(forecast)
plt.title('Solar Power Forecast Using Prophet')
plt.xlabel('Date')
plt.ylabel('Power Output (kW)')
plt.show()
# Plot components
fig_components = model.plot_components(forecast)
plt.show()
风电功率预测
我们可以使用Prophet来进行风电功率预测。以下是风电功率预测的脚本 wind_power_forecasting.py:
[<title="Wind Power Forecasting Script using Prophet">]
import pandas as pd
from fbprophet import Prophet
import matplotlib.pyplot as plt
# Define paths
preprocessed_base_path = 'preprocessed_datasets'
wind_turbine_path = os.path.join(preprocessed_base_path, 'wind_turbine_data.csv')
# Load preprocessed data
wind_turbine_df = pd.read_csv(wind_turbine_path, parse_dates=['timestamp'], index_col='timestamp')
# Prepare data for Prophet
df_prophet = wind_turbine_df.reset_index().rename(columns={'timestamp': 'ds', 'turbine_output': 'y'})
# Initialize and fit the model
model = Prophet(daily_seasonality=False, weekly_seasonality=True, yearly_seasonality=True)
model.fit(df_prophet)
# Make future predictions
future = model.make_future_dataframe(periods=30*24*12, freq='5min') # Predict for the next month
forecast = model.predict(future)
# Plot the forecast
fig = model.plot(forecast)
plt.title('Wind Power Forecast Using Prophet')
plt.xlabel('Date')
plt.ylabel('Power Output (kW)')
plt.show()
# Plot components
fig_components = model.plot_components(forecast)
plt.show()
使用说明
-
配置路径：
- 确保
datasets目录结构正确，并且包含所有CSV文件。 - 确保所有文件名和路径与脚本中的路径一致。
- 确保
-
运行脚本：
- 在终端中运行
data_preprocessing.py脚本来加载和预处理数据。 - 在终端中运行
data_visualization.py来可视化数据。 - 在终端中运行
time_series_decomposition.py来进行时间序列分解。 - 在终端中运行
load_forecasting.py来进行负荷预测。 - 在终端中运行
solar_power_forecasting.py来进行光伏功率预测。 - 在终端中运行
wind_power_forecasting.py来进行风电功率预测。
- 在终端中运行
-
注意事项：
- 确保所有必要的工具箱已安装，特别是
pandas,matplotlib,seaborn,statsmodels, 和prophet。 - 根据需要调整参数，如
periods和freq。
- 确保所有必要的工具箱已安装，特别是
示例
假设您的数据文件夹结构如下：
datasets/
├── solar_power_data.csv
├── solar_forecast_data.csv
├── wind_turbine_data.csv
└── load_data.csv
并且每个CSV文件包含相应的列（例如，timestamp, power_output, predicted_radiance, predicted_power, turbine_output, load）。运行上述脚本后，您可以查看各种图表和预测结果。