一个GA-堆叠组合方法预测智能家庭的能源消耗:组合方法的比较研究
Mahziyar Dostmohammadi1, Mona Zamani Pedram1, Siamak Hoseinzadeh2
1Energy System Division, Department of Mechanical Engineering, K.N, Toosi University, Tehran P.O.Box 19395-1999, Iran.
Journal of environmental management
|June 13, 2024
概括
使用整体机器学习模型 (包括GA-Stacking) 进行准确的建筑能源预测,可以显著提高预测的准确性. 这有助于环保设计,减少能源消耗.
科学领域:
- 建筑科学 建筑科学
- 能源效率 能源效率 能源效率
- 机器学习 机器学习
背景情况:
- 建筑物消耗大量的能源,造成环境挑战.
- 准确的能源使用预测对于效率和知情设计至关重要.
- 环保的建筑设计是减轻消费的关键.
研究的目的:
- 用先进的机器学习来预测建筑物的能源消耗.
- 为了比较各种合奏技巧的表现.
- 通过遗传算法优化预测准确度.
主要方法:
- 集群分析以确定消费模式 (低,高,周末).
- 集合模型:人工神经网络 (ANN),K-最近邻居 (KNN),决策树 (DT),随机森林 (RF),极端梯度增强 (XGB),梯度增强树 (GBT).
- 堆叠组合方法与遗传算法 (GA-Stacking) 结合用于优化.
主要成果:
- 堆叠组合的性能优于其他方法.
- GA-Stacking显著提高了预测准确性,超过90%的平均绝对百分比误差 (MAPE) 减少.
- 实现了高R2分数:0.983 (子集-1),0.985 (子集-2) 和0.999 (子集-3). 在此过程中,R2分数达到0.983 (子集-1),0.985 (子集-2) 和0.999 (子集-3).
结论:
- 在住宅建筑能源预测方面,GA-Stacking提供了实质性的进步.
- 该框架支持建筑设计师的知情决策.
- 优化的预测可以实现更好的设计管理和建筑前效率.
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