本文目录导读：
这是一个非常有意思的综合性Python案例！天气确实可以量化计算，而且通过Python可以构建一个相当完整的能源影响评估系统。
核心思路
天气对能源的影响可以通过多维度模型来量化：
- 温度影响 → 供暖/制冷需求
- 太阳辐射 → 光伏发电效率
- 风速风向 → 风力发电量
- 降水/湿度 → 空气质量与设备效率
完整Python实现案例
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from datetime import datetime, timedelta
import seaborn as sns
from dataclasses import dataclass
from typing import Dict, List, Tuple
# ==================== 数据模型定义 ====================
@dataclass
class WeatherData:
"""天气数据类"""
temperature: float # 温度 (℃)
humidity: float # 相对湿度 (%)
wind_speed: float # 风速 (m/s)
wind_direction: float # 风向 (度)
solar_radiation: float # 太阳辐射 (W/m²)
precipitation: float # 降水量 (mm)
pressure: float # 气压 (hPa)
@dataclass
class EnergyImpact:
"""能源影响结果类"""
cooling_load: float # 制冷需求 (kWh)
heating_load: float # 供暖需求 (kWh)
solar_output: float # 光伏发电量 (kWh)
wind_output: float # 风电发电量 (kWh)
efficiency_factor: float # 整体能效系数
def get_total_load(self):
return self.cooling_load + self.heating_load
def get_total_generation(self):
return self.solar_output + self.wind_output
# ==================== 天气数据生成器 ====================
class WeatherDataGenerator:
"""模拟生成天气数据"""
def __init__(self, city_lat: float = 35.0, city_lon: float = 110.0):
self.lat = city_lat
self.lon = city_lon
def generate_daily_series(self, days: int = 365) -> pd.DataFrame:
"""生成日天气数据序列"""
dates = pd.date_range(start='2024-01-01', periods=days, freq='D')
# 季节性温度模型
annual_temp = 15 + 20 * np.sin((np.arange(days) / 365.0) * 2 * np.pi - np.pi/2)
# 加入随机波动
temperature = annual_temp + np.random.normal(0, 3, days)
# 湿度与温度相关
humidity = 60 + 10 * np.sin((np.arange(days) / 365.0) * 2 * np.pi) + \
np.random.normal(0, 5, days)
humidity = np.clip(humidity, 30, 95)
# 风速模型
wind_speed = 5 + 3 * np.abs(np.sin((np.arange(days) / 20) * 2 * np.pi)) + \
np.random.normal(0, 1.5, days)
wind_speed = np.clip(wind_speed, 0, 30)
# 太阳辐射模型
# 与季节和云量相关
cloud_factor = 1 - 0.5 * (humidity - 30) / 65
solar_radiation = 100 + 800 * np.cos((np.arange(days) / 365.0) * 2 * np.pi) * \
cloud_factor + np.random.normal(0, 50, days)
solar_radiation = np.clip(solar_radiation, 0, 1000)
# 降水量
precipitation = np.zeros(days)
for i in range(days):
if np.random.random() < 0.3: # 30%概率下雨
precipitation[i] = np.random.exponential(5)
# 风机方向（0-360度）
wind_direction = np.random.uniform(0, 360, days)
# 气压
pressure = 1013 + np.random.normal(0, 8, days)
df = pd.DataFrame({
'date': dates,
'temperature': temperature,
'humidity': humidity,
'wind_speed': wind_speed,
'wind_direction': wind_direction,
'solar_radiation': solar_radiation,
'precipitation': precipitation,
'pressure': pressure
})
return df
# ==================== 能源影响计算引擎 ====================
class EnergyImpactCalculator:
"""综合能源影响计算"""
def __init__(self):
# 建筑参数
self.building_area = 2000 # 平方米
self.heat_transfer_coeff = 0.4 # W/m²·K
self.solar_roof_area = 500 # 光伏面积平方米
self.wind_turbine_capacity = 100 # kW
def calculate_daily_impact(self, weather_row: pd.Series) -> EnergyImpact:
"""计算单日能源影响"""
temp = weather_row['temperature']
humidity = weather_row['humidity']
wind_spd = weather_row['wind_speed']
solar_rad = weather_row['solar_radiation']
# 1. 供暖/制冷负荷计算 (简化的Fanger模型)
# 舒适温度范围 18-26℃
if temp > 26:
# 制冷负荷
cooling_load = (temp - 26) * self.heat_transfer_coeff * self.building_area / 1000 # kWh
# 湿度对制冷的影响
cooling_load *= (1 + (humidity - 50) * 0.005)
heating_load = 0
elif temp < 18:
# 供暖负荷
heating_load = (18 - temp) * self.heat_transfer_coeff * self.building_area / 1000
cooling_load = 0
else:
cooling_load = 0
heating_load = 0
# 2. 光伏发电计算
# 光伏效率模型（考虑温度影响）
solar_eff = 0.2 # 基础效率 20%
temp_derate = 1 - 0.005 * max(0, temp - 25) # 温度衰减
radiation_factor = solar_rad / 1000 # 参考辐照度
solar_output = self.solar_roof_area * solar_eff * temp_derate * \
radiation_factor * 0.75 # 附加系统损耗
# 3. 风力发电计算
# 风机功率曲线模型
wind_cut_in, wind_nominal, wind_cut_off = 3, 12, 25 # m/s
if wind_spd < wind_cut_in or wind_spd > wind_cut_off:
wind_output = 0
elif wind_spd < wind_nominal:
wind_output = self.wind_turbine_capacity * (wind_spd / wind_nominal) ** 3
else:
wind_output = self.wind_turbine_capacity
# 4. 整体能效系数计算
# 综合考虑温度、湿度、风力等因素
comfort_temp = 22 # 理想温度
temp_efficiency = 1 - 0.01 * abs(temp - comfort_temp) / 10
humidity_effect = 1 - 0.002 * max(0, abs(humidity - 50) - 20)
wind_effect = 1 - 0.005 * max(0, wind_spd - 8) # 过大的风导致热损失
solar_effect = 1 + 0.001 * solar_rad / 100
efficiency_factor = temp_efficiency * humidity_effect * wind_effect * solar_effect
efficiency_factor = max(0.2, min(efficiency_factor, 1.5))
return EnergyImpact(
cooling_load=cooling_load,
heating_load=heating_load,
solar_output=solar_output,
wind_output=wind_output,
efficiency_factor=efficiency_factor
)
def calculate_yearly_impact(self, weather_df: pd.DataFrame) -> pd.DataFrame:
"""计算全年每天的能源影响"""
results = []
for _, row in weather_df.iterrows():
impact = self.calculate_daily_impact(row)
results.append({
'date': row['date'],
'cooling_load': impact.cooling_load,
'heating_load': impact.heating_load,
'solar_output': impact.solar_output,
'wind_output': impact.wind_output,
'efficiency_factor': impact.efficiency_factor
})
return pd.DataFrame(results)
# ==================== 分析与可视化 ====================
class EnergyAnalyzer:
"""能源影响分析器"""
@staticmethod
def analyze_seasonal_patterns(impact_df: pd.DataFrame) -> Dict:
"""分析季节性规律"""
impact_df['month'] = pd.to_datetime(impact_df['date']).dt.month
impact_df['season'] = pd.cut(impact_df['month'],
bins=[0, 3, 6, 9, 12],
labels=['Winter', 'Spring', 'Summer', 'Autumn'])
seasonal_stats = impact_df.groupby('season').agg({
'cooling_load': 'mean',
'heating_load': 'mean',
'solar_output': 'mean',
'wind_output': 'mean',
'efficiency_factor': 'mean'
})
return seasonal_stats
@staticmethod
def plot_energy_visualization(impact_df: pd.DataFrame, weather_df: pd.DataFrame):
"""创建综合可视化图表"""
fig, axes = plt.subplots(3, 2, figsize=(15, 12))
fig.suptitle('Weather Impact on Energy Analysis', fontsize=16)
# 子图1：温度与能源需求
ax1 = axes[0, 0]
ax1.axhline(y=0, color='gray', linestyle='--')
ax1.plot(weather_df.index, weather_df['temperature'], color='red', label='Temperature')
ax1.set_ylabel('Temperature (℃)')
ax1_twin = ax1.twinx()
ax1_twin.plot(impact_df.index, impact_df['heating_load'] + impact_df['cooling_load'],
color='blue', alpha=0.6, label='Total Load')
ax1.set_title('Temperature vs Energy Load')
ax1.legend(loc='upper left')
ax1_twin.legend(loc='upper right')
# 子图2：光伏与太阳能
ax2 = axes[0, 1]
ax2.plot(weather_df.index, weather_df['solar_radiation'],
color='orange', label='Solar Radiation')
ax2_twin = ax2.twinx()
ax2_twin.plot(impact_df.index, impact_df['solar_output'],
color='green', alpha=0.7, label='Solar Output')
ax2.set_title('Solar Radiation vs Output')
ax2.legend(loc='upper right')
# 子图3：风速与风力发电
ax3 = axes[1, 0]
ax3.plot(weather_df.index, weather_df['wind_speed'],
color='cyan', label='Wind Speed')
ax3_twin = ax3.twinx()
ax3_twin.plot(impact_df.index, impact_df['wind_output'],
color='purple', alpha=0.7, label='Wind Output')
ax3.set_title('Wind Speed vs Output')
ax3.legend(loc='upper left')
# 子图4：能效系数
ax4 = axes[1, 1]
ax4.plot(impact_df.index, impact_df['efficiency_factor'],
color='brown', linewidth=2)
ax4.axhline(y=1.0, color='gray', linestyle='--')
ax4.set_ylim(0.5, 1.5)
ax4.set_title('Overall System Efficiency Factor')
ax4.fill_between(impact_df.index, 0.5, impact_df['efficiency_factor'], alpha=0.3)
# 子图5：季节性模式
ax5 = axes[2, 0]
seasonal = EnergyAnalyzer.analyze_seasonal_patterns(impact_df)
seasonal[['cooling_load', 'heating_load']].plot(kind='bar', ax=ax5)
ax5.set_title('Seasonal HVAC Loads')
# 子图6：能源平衡
ax6 = axes[2, 1]
total_load = impact_df['cooling_load'] + impact_df['heating_load']
total_gen = impact_df['solar_output'] + impact_df['wind_output']
ax6.scatter(total_load, total_gen, c=weather_df['temperature'], cmap='coolwarm')
ax6.set_xlabel('Energy Load (kWh)')
ax6.set_ylabel('Renewable Generation (kWh)')
ax6.set_title('Energy Balance - Color: Temperature')
ax6.colorbar = plt.colorbar(ax6.collections[0], ax=ax6)
plt.tight_layout()
return fig
# ==================== 主程序 ====================
def main():
print("="*60)
print("智能能源-天气综合影响分析系统")
print("="*60)
# 1. 生成模拟数据
print("\n[1] 生成模拟天气数据...")
generator = WeatherDataGenerator(lat=35.0, lon=110.0)
weather_df = generator.generate_daily_series(days=365)
# 2. 计算能源影响
print("[2] 计算能源影响...")
calculator = EnergyImpactCalculator()
impact_df = calculator.calculate_yearly_impact(weather_df)
# 3. 合并并显示基本信息
combined_df = pd.concat([weather_df, impact_df], axis=1)
print("\n数据维度:", combined_df.shape)
print("时间范围:", combined_df['date'].min(), "至", combined_df['date'].max())
# 4. 统计分析
print("\n[3] 统计分析结果:")
print("\n全年能源统计:")
print(f" 总制冷需求: {impact_df['cooling_load'].sum():.0f} kWh/年")
print(f" 总供暖需求: {impact_df['heating_load'].sum():.0f} kWh/年")
print(f" 光伏总发电量: {impact_df['solar_output'].sum():.0f} kWh/年")
print(f" 风电总发电量: {impact_df['wind_output'].sum():.0f} kWh/年")
total_load = impact_df['cooling_load'].sum() + impact_df['heating_load'].sum()
total_gen = impact_df['solar_output'].sum() + impact_df['wind_output'].sum()
self_sufficiency = (total_gen / total_load) * 100 if total_load > 0 else 0
print(f"\n 能源自给率: {self_sufficiency:.1f}%")
# 5. 季节分析
print("\n[4] 季节性分析:")
seasonal_stats = EnergyAnalyzer.analyze_seasonal_patterns(impact_df)
print(seasonal_stats.to_string())
# 6. 找到最差和最好的日子
print("\n[5] 极端天气分析:")
combined = pd.concat([weather_df, impact_df], axis=1)
worst_day = combined.loc[combined['cooling_load'] + combined['heating_load'] ==
(combined['cooling_load'] + combined['heating_load']).max()].iloc[0]
best_day = combined.loc[combined['cooling_load'] + combined['heating_load'] ==
(combined['cooling_load'] + combined['heating_load']).min()].iloc[0]
print(f"\n能源需求最大日:")
print(f" 日期: {worst_day['date']}")
print(f" 温度: {worst_day['temperature']:.1f}℃")
print(f" 总需求: {worst_day['cooling_load'] + worst_day['heating_load']:.1f} kWh")
print(f"\n能源需求最小日:")
print(f" 日期: {best_day['date']}")
print(f" 温度: {best_day['temperature']:.1f}℃")
print(f" 总需求: {best_day['cooling_load'] + best_day['heating_load']:.1f} kWh")
# 7. 可视化
print("\n[6] 生成可视化图表...")
fig = EnergyAnalyzer.plot_energy_visualization(impact_df, weather_df)
plt.savefig('weather_energy_analysis.png', dpi=300, bbox_inches='tight')
print("图表已保存: weather_energy_analysis.png")
# 8. 天气影响的经济评估
electricity_price = 0.8 # 元/kWh
fuel_price = 0.5 # 元/kWh (燃气供暖)
total_elec_cost = (impact_df['cooling_load'].sum() * electricity_price +
impact_df['heating_load'].sum() * fuel_price)
total_gen_income = impact_df['solar_output'].sum() * 0.5 + \
impact_df['wind_output'].sum() * 0.6
print("\n[7] 经济效益估算:")
print(f" 全年能源费用: {total_elec_cost:.0f} 元")
print(f" 光伏/风电潜在收益: {total_gen_income:.0f} 元")
print(f" 综合能源成本节约: {total_gen_income / total_elec_cost * 100:.1f}%")
print("\n" + "="*60)
print("分析完成！模型指标质量评估：")
print(f" R² (温度-负荷): {np.corrcoef(weather_df['temperature'], impact_df['cooling_load'] + impact_df['heating_load'])[0,1]:.3f}")
print(f" 相关系数 (辐射-光伏): {np.corrcoef(weather_df['solar_radiation'], impact_df['solar_output'])[0,1]:.3f}")
print(f" 相关系数 (风速-风电): {np.corrcoef(weather_df['wind_speed'], impact_df['wind_output'])[0,1]:.3f}")
if __name__ == "__main__":
main()
核心量化模型解析
供暖/制冷负荷模型
CoolingLoad = (T - 26) × U × Area × HumidityFactor
HeatingLoad = (18 - T) × U × Area光伏发电模型
SolarOutput = Area × Efficiency × TemperatureDerate × RadiationFactor- 温度衰减：每升高1℃，效率下降0.5%
- 辐射转换：实际辐射/标准辐射(1000W/m²)
风力发电模型
三段式功率曲线：
- 切入风速 < 3m/s: 不发电
- 3-12m/s: 立方曲线
- 12-25m/s: 满发
- > 25m/s: 安全停机综合能效系数
Efficiency = f(Temperature) × f(Humidity) × f(Wind) × f(Solar)实际应用场景
通过这个系统可以：
- 预测能耗：基于天气预报预测未来7天能耗
- 优化能源调度：平衡光伏、风电与传统能源
- 投资评估：评估安装光伏/风电的回报周期
- 需求响应：预测高峰负荷，提前调整用电策略
扩展建议
- 接入真实天气API（如和风天气、OpenWeatherMap）
- 添加机器学习预测（LSTM、随机森林）
- 考虑建筑朝向、保温材料等物理参数
- 加入电价动态计费模型
这个案例展示了天气数据如何通过物理模型和数据分析转化为可量化的能源指标,为建筑节能和可再生能源规划提供了科学依据！
