131 lines
4.5 KiB
Python
131 lines
4.5 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""测试多场景聚类模块"""
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import sys
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import os
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sys.path.append(os.path.join(os.path.dirname(__file__), 'src'))
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from multi_scenario import MultiScenarioAnalyzer
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import numpy as np
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def test_multi_scenario_basic():
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"""测试基本功能"""
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print("测试多场景聚类基本功能...")
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# 生成简单的测试数据（24小时）
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hours = 24
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solar_output = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0,
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5.0, 4.0, 3.0, 2.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
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wind_output = [2.0, 3.0, 4.0, 3.0, 2.0, 1.0, 1.0, 2.0, 3.0, 4.0, 3.0, 2.0,
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2.0, 3.0, 4.0, 3.0, 2.0, 1.0, 1.0, 2.0, 3.0, 4.0, 3.0, 2.0]
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load_demand = [3.0, 4.0, 5.0, 6.0, 8.0, 10.0, 12.0, 14.0, 16.0, 18.0, 20.0, 18.0,
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16.0, 14.0, 12.0, 10.0, 8.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0, 2.0]
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# 创建分析器
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analyzer = MultiScenarioAnalyzer(n_clusters=3, random_state=42)
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try:
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# 执行聚类分析
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result = analyzer.fit_predict(solar_output, wind_output, load_demand)
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print("✅ 基本聚类测试通过")
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print(f" - 识别场景数: {result.n_scenarios}")
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print(f" - 轮廓系数: {result.silhouette_score:.3f}")
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print(f" - 场景名称: {result.scenario_names}")
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return True
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except Exception as e:
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print(f"❌ 测试失败: {str(e)}")
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import traceback
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traceback.print_exc()
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return False
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def test_multi_scenario_yearly():
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"""测试年度数据聚类"""
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print("\n测试年度数据聚类...")
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# 生成模拟的8760小时数据
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np.random.seed(42)
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# 简单的模拟数据
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solar_output = []
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wind_output = []
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load_demand = []
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for day in range(365):
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# 光伏：白天有出力，夜间为0
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daily_solar = [0.0] * 6 + list(np.random.uniform(1, 6, 12)) + [0.0] * 6
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solar_output.extend(daily_solar)
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# 风电：相对随机
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daily_wind = np.random.exponential(2.5, 24).tolist()
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wind_output.extend(daily_wind)
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# 负荷：日内变化，夜间低，白天高
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daily_load = [3.0, 4.0, 5.0, 6.0, 8.0, 10.0, 12.0, 14.0, 16.0, 18.0, 20.0, 18.0,
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16.0, 14.0, 12.0, 10.0, 8.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0, 2.0]
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load_demand.extend(daily_load)
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try:
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analyzer = MultiScenarioAnalyzer(n_clusters=5, random_state=42)
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result = analyzer.fit_predict(solar_output, wind_output, load_demand, find_optimal_k=True)
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print("✅ 年度数据聚类测试通过")
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print(f" - 最优聚类数: {result.n_scenarios}")
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print(f" - 轮廓系数: {result.silhouette_score:.3f}")
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# 输出前3个场景的统计
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for i in range(min(3, result.n_scenarios)):
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stats = result.scenario_stats[f'scenario_{i}']
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print(f" - 场景{i+1}: 频率{stats['frequency']:.1%}, "
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f"光伏{stats['solar_mean']:.1f}MW, "
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f"风电{stats['wind_mean']:.1f}MW, "
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f"负荷{stats['load_mean']:.1f}MW")
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# 测试图表生成
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print(" - 测试图表生成...")
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analyzer.plot_scenario_analysis(result, solar_output, wind_output, load_demand,
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save_path="test_scenario_analysis.png")
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print(" ✅ 图表生成成功")
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return True
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except Exception as e:
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print(f"❌ 年度数据测试失败: {str(e)}")
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import traceback
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traceback.print_exc()
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return False
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def test_data_validation():
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"""测试数据验证"""
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print("\n测试数据验证...")
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# 测试数据长度不一致
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try:
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analyzer = MultiScenarioAnalyzer(n_clusters=2)
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analyzer.fit_predict([1, 2, 3], [1, 2], [1, 2, 3]) # 长度不一致
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print("❌ 应该检测到数据长度不一致")
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return False
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except ValueError as e:
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print("✅ 正确检测到数据长度不一致")
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return True
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if __name__ == "__main__":
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print("=== 多场景聚类模块测试 ===\n")
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success = True
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success &= test_multi_scenario_basic()
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success &= test_multi_scenario_yearly()
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success &= test_data_validation()
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print(f"\n{'='*50}")
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if success:
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print("🎉 所有测试通过！多场景聚类模块工作正常。")
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else:
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print("💥 部分测试失败，请检查代码。")
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print(f"{'='*50}") |