Predicting Daily Heating Energy Consumption in Residential Buildings through Integration of Random Forest Model and Meta-Heuristic Algorithms

随机森林 能源消耗 启发式 元启发式 算法 能量(信号处理) 消费(社会学) 计算机科学 数学优化 环境科学 数学 工程类 机器学习 统计 社会学 社会科学 电气工程
作者
Weiyan Xu,Jielei Tu,Ning Xu,Zuming Liu
出处
期刊:Energy [Elsevier BV]
卷期号:301: 131726-131726 被引量:7
标识
DOI:10.1016/j.energy.2024.131726
摘要

This research utilizes a sophisticated hybrid model integrating the Random Forest algorithm with meta-heuristic optimization techniques to estimate heating energy consumption in residential buildings. The study addresses key variables including architectural characteristics, occupancy, and ambient temperature. The primary objective is to enhance the prediction accuracy of heating energy consumption using a novel approach combining Random Forest with various meta-heuristic algorithms. The study employs six combinations of the Random Forest algorithm and meta-heuristic optimizers. To mitigate overfitting, K-Fold cross-validation is implemented during model training. The model's performance is evaluated using five statistical indices: coefficient of determination (R2), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Relative Absolute Error (RAE), and Theil Inequality Coefficient (TIC). Results demonstrate the hybrid model's high predictive accuracy, with the Arithmetic Optimization Algorithm enhancing Random Forest's performance significantly. Notable statistical achievements include R2 = 0.977201, RMSE = 0.1179, MAE = 0.0573, RAE = 0.0930, and TIC = 0.0187. Additionally, the Ant Lion Optimizer shows excellent convergence, achieving a TIC value of 0.014986 after 101 iterations. The proposed hybrid model significantly outperforms traditional methods in predicting residential heating energy consumption. The integration of Random Forest with advanced meta-heuristic algorithms offers a robust framework for enhancing prediction accuracy in energy consumption modeling.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
1秒前
GooJohn发布了新的文献求助10
1秒前
TaoBijiang发布了新的文献求助10
2秒前
3秒前
张二娃完成签到,获得积分20
4秒前
5秒前
量子星尘发布了新的文献求助10
5秒前
5秒前
6秒前
6秒前
JamesPei应助科研通管家采纳,获得10
6秒前
6秒前
科研通AI5应助科研通管家采纳,获得10
6秒前
wanci应助科研通管家采纳,获得10
6秒前
隐形曼青应助科研通管家采纳,获得10
6秒前
Akim应助科研通管家采纳,获得10
6秒前
jyy应助科研通管家采纳,获得10
6秒前
小二郎应助科研通管家采纳,获得10
6秒前
思源应助科研通管家采纳,获得10
6秒前
6秒前
7秒前
YPP完成签到,获得积分10
7秒前
王汉韬完成签到,获得积分20
8秒前
奥一奥完成签到,获得积分10
9秒前
欣喜沛芹发布了新的文献求助10
9秒前
纾缓发布了新的文献求助10
9秒前
WW完成签到,获得积分10
9秒前
zlttt完成签到,获得积分20
10秒前
852应助momo采纳,获得10
11秒前
此间少年发布了新的文献求助10
11秒前
王汉韬发布了新的文献求助10
11秒前
哈哈完成签到 ,获得积分10
12秒前
英俊的铭应助xn201120采纳,获得10
12秒前
闾丘惜萱完成签到,获得积分10
12秒前
baonali发布了新的文献求助10
14秒前
yyyhhh完成签到,获得积分10
16秒前
ZH完成签到 ,获得积分10
20秒前
20秒前
20秒前
ll完成签到,获得积分10
21秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3989263
求助须知:如何正确求助?哪些是违规求助? 3531418
关于积分的说明 11253814
捐赠科研通 3270066
什么是DOI,文献DOI怎么找? 1804884
邀请新用户注册赠送积分活动 882084
科研通“疑难数据库(出版商)”最低求助积分说明 809136