推进住宅建筑电力消耗预测的集体学习技术:可解释的人工智能的洞察力
Jihoon Moon1,2, Muazzam Maqsood3, Dayeong So2
1Department of AI and Big Data, Soonchunhyang University, Asan, Republic of Korea.
PloS one
|November 14, 2024
概括
精确的住宅电力消耗预测得到了决策树集体学习和可解释的AI的增强. 这种方法通过揭示关键预测驱动因素,如温度湿度指数来提高能源效率和成本管理.
科学领域:
- 能源系统 能源系统
- 人工智能的人工智能
- 可持续能源 可持续能源
背景情况:
- 准确的电力消耗预测对于住宅建筑的能源效率和成本管理至关重要.
- 决策树组合学习为复杂的能源数据集提供了高准确度.
- 可解释的人工智能 (XAI) 提高了预测模型的透明度和可解释性.
研究的目的:
- 为了比较分析决策树,集体学习技术与XAI集成,用于住宅能源消耗预测.
- 提高短期负载预测模型的透明度和可解释性.
- 为优化能源管理确定关键影响变量.
主要方法:
- 利用了大学住宅综合体和设备能源预测数据集.
- 应用数据预处理和决策树包装和增强组合方法.
- 在XAI分析中采用了沙普利添加式解释 (SHAP) 方法.
主要成果:
- 确定了温度-湿度指数和风冷温度作为短期负载预测的重要预测指标.
- 在解释模型决策时,证明了用XAI进行集体学习的有效性.
- 在预测准确度方面表现优于温度,湿度和风速等传统参数.
结论:
- 与XAI结合的决策树组合学习为住宅能源预测提供了一种透明和准确的方法.
- XAI揭示了非传统的气象因素显著影响能源负载.
- 该研究促进了能源系统管理的提高精度和可复制性.
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