Short-term prediction model of building energy consumption based on genetically optimized decision tree

doi:10.19678/j.issn.1000-3428.0051781

Computer Engineering ›› 2019, Vol. 45 ›› Issue (6): 280-289,296. doi: 10.19678/j.issn.1000-3428.0051781

Short-term prediction model of building energy consumption based on genetically optimized decision tree

DING Feihong^1a,1b,2,LIU Peng^1a,1b,2,LU Tun^1a,1b,2,GU Ning^1a,1b,2,DING Xianghua^1a,1b,2,YANG Baoming³,DAI Wenqi³,ZOU Chaojun³

1a.School of Computer Science; 1b.Shanghai Key Laboratory of Data Science,Fudan University,Shanghai 201203,China;2.Shanghai Institute of Intelligent Electronics and System Research,Shanghai 201203,China; 3.Shanghai Luban Software Co.,Ltd.,Shanghai 200433,China

Received:2018-06-11 Online:2019-06-15 Published:2019-06-15

基于遗传优化决策树的建筑能耗短期预测模型

丁飞鸿^1a,1b,2,刘鹏^1a,1b,2,卢暾^1a,1b,2,顾宁^1a,1b,2,丁向华^1a,1b,2,杨宝明³,戴文祺³,邹超君³

1.复旦大学 a.计算机科学技术学院; b.上海数据科学重点实验室,上海 201203;2.上海智能电子与系统研究院,上海 201203; 3.上海鲁班软件股份有限公司,上海 200433

作者简介:丁飞鸿(1995—),男,硕士研究生,主研方向为协同计算、数据分析;刘鹏,博士;卢暾(通信作者),副教授、博士;顾宁,教授、博士、博士生导师;丁向华,副教授、博士;杨宝明,高级工程师、博士;戴文祺、邹超君,学士。
基金资助:
上海市科委项目“信息化技术与调适技术的集成研究与示范”(16DZ1202402)。

Abstract

Abstract: When using linear regression models to predict public building energy consumption data,there are uncertainties and precision deviations.Therefore,a Genetically Optimized Decision Tree(GODT) model is established.The Genetic Algorithm(GA) is used to optimize the subtree generation process of the Gradient Boosting Decision Tree (GBDT).The R-Square value predicted by the model is used as the evaluation criterion of the iteration to achieve the purpose of energy consumption prediction.Experimental results show that the prediction accuracy of this model is higher than that of the traditional regression prediction model.

Key words: building energy consumption, regression prediction, Gradient Boosting Decision Tree(GBDT), Genetic Algorithm(GA), Genetically Optimized Decision Tree(GODT)

摘要： 使用线性回归模型预测公共建筑能耗数据时，存在不确定性影响因素和精度偏差问题。为此，建立一种遗传优化决策树模型。采用遗传算法优化梯度提升决策树的子树生成过程，以模型预测的R-Square值作为迭代的评估标准，从而达到能耗预测的目的。实验结果表明，与传统的回归预测模型相比，该模型预测精度较高。

关键词: 建筑能耗, 回归预测, 梯度提升决策树, 遗传算法, 遗传优化决策树

CLC Number:

TP399

DING Feihong,LIU Peng,LU Tun,GU Ning,DING Xianghua,YANG Baoming,DAI Wenqi,ZOU Chaojun. Short-term prediction model of building energy consumption based on genetically optimized decision tree[J]. Computer Engineering, 2019, 45(6): 280-289,296.

丁飞鸿,刘鹏,卢暾,顾宁,丁向华,杨宝明,戴文祺,邹超君. 基于遗传优化决策树的建筑能耗短期预测模型[J]. 计算机工程, 2019, 45(6): 280-289,296.

/ / Recommend / Download Citations

URL: http://www.ecice06.com/EN/10.19678/j.issn.1000-3428.0051781

http://www.ecice06.com/EN/Y2019/V45/I6/280

References 25

［1］	LI Weifeng,XU Baowen,YANG Hongji,et al.Application of genetic algorithm in search engine［C］//Proceedings of International Conference on Micro-electronic Systems Education.Washington D.C.,USA:IEEE Computer Society,2000:366.
［2］	DEB K,PRATAP A,AGARWAL S,et al.A fast and elitist multi-objective genetic algorithm:NSGA-II［J］.IEEE Transactions on Evolutionary Computation,2002,6(2):182-197.
［3］	GONG Yue,LUO Xiaoqin,WANG Dianhai,et al.Urban travel time prediction based on gradient boosting regression tress［EB/OL］.［2018-05-10］.http://en.cnki.com.cn/Article_en/CJFDTotal-ZDZC201803006.html.
［4］	BORGES C E,PENYA Y K,FERNNDEZ I.Evaluating combined load forecasting in large power systems and smart grids［J］.IEEE Transactions on Industrial Informatics,2013,9(3):1570-1577.
［5］	BIANCO V,MANCA O,NARDINI S.Electricity consumption forecasting in Italy using linear regression models［J］.Energy,2009,34(9):1413-1421.
［6］	DING N,BESANGER Y,WURTZ F,et al.Time series method for short-term load forecasting using smart metering in distribution systems［C］//Proceedings of Trondheim PowerTech.Washington D.C.,USA:IEEE Press,2011:1-6.
［7］	SOOD R,KOPRINSKA I,AGELIDIS V G.Electricity load forecasting based on autocorrelation analysis［C］//Proceedings of International Joint Conference on Neural Networks.Washington D.C.,USA:IEEE Press,2010:1-8.
［8］	PENG Liling,FAN Guofeng,HUANG Minliang,et al.Hybridizing DEMD and quantum PSO with SVR in electric load forecasting［J］.Energies,2016,9(3):221.
［9］	FAN Guofeng,PENG Liling,HONG Wei-chiang,et al.Electric load forecasting by the SVR model with differential empirical mode decomposition and auto regression［J］.Neurocomputing,2016,173:958-970.
［10］	HUANG Ya,LU Tun,DING Xianghua,et al.Campus building energy usage analysis and prediction:a SVR approach based on multi-scale RBF kernels［C］//Proceedings of International Conference on Human Centered Computing.Berlin,Germany:Springer,2014:441-452.
［11］	QUAN Hao,SRINIVASAN D,KHOSRAVI A.Short-term load and wind power forecasting using neural network-based prediction intervals［J］.IEEE Transactions on Neural Networks and Learning Systems,2014,25(2):303-315.
［12］	POLIKAR R.Ensemble learning［M］.Berlin,Germany:Springer,2012.
［13］	DUFFY N,HELMBOLD D.Boosting methods for regression［J］.Machine Learning,2002,47(2/3):153-200.
［14］	ZIEBA M,TOMCZAK S K,TOMCZAK J M.Ensemble boosted trees with synthetic features generation in application to bankruptcy prediction［J］.Expert Systems with Applications,2016,58(C):93-101.
［15］	秦姣龙,王蔚.Bagging组合的不平衡数据分类方法［J］.计算机工程,2011,37(14):178-179.
［16］	FRIEDMAN J H.Greedy function approximation:a gradient boosting machine.［J］.Annals of Statistics,2001,29(5):1189-1232.
［17］	HAZRA A,GOGTAY N.Biostatistics series module 6:correlation and linear regression［J］.Indian Journal of Dermatology,2016,61(6):593-601.
［18］	YU Chunnan,MIROWSKI P,HO T K.A sparse coding approach to household electricity demand forecasting in smart grids［J］.IEEE Transactions on Smart Grid,2017,8(2):738-748.
［19］	李广野,李伟,田小蕾,等.基于XGBoost算法的短期电力负荷预报［J］.智能电网,2017,7(4):274-285.
［20］	周志华,王珏.机器学习及其应用［M］.北京:清华大学出版社,2011.
［21］	韩生廉,武晓今,倪萌.组合优化问题中遗传算法的局限性及其改进模式［J］.控制与决策,2002,17(2):219-222.
［22］	万本庭,全小凤.基于遗传算法的移动传感节点路径规划策略研究［J］.计算机工程,2017,43(8):144-150.
［23］	HOLLAND J H.Adaptation in natural and artificial systems［M］.Cambridge,USA:MIT Press,1992.
［24］	王芳,邱玉辉.一种引入轮盘赌选择算子的混合粒子群算法［J］.西南师范大学学报(自然科学版),2016,31(3):93-96.
［25］	GUO Chonghui,LU Yuchang,TANG Huanwen.Modified evolutionary algorithm for global optimization［J］.Journal of Systems Engineering and Electronics,2004,15(1):1-6.

[1]	BAI Zhixu, WANG Hengjun. Adversarial Example Generation Method Based on Improved Genetic Algorithm [J]. Computer Engineering, 2023, 49(5): 139-149.
[2]	SANG Yongxuan, WEI Jiangpo, WANG Bo, SONG Ying. Hybrid Heuristic Task Offloading Algorithm with Edge Caching Mechanism [J]. Computer Engineering, 2023, 49(4): 149-158.
[3]	GAN Hongnan, ZHANG Kai. Approximate Nearest Neighbor Search Based on Neighbor Graphs with Parameter Adaptation [J]. Computer Engineering, 2022, 48(9): 28-36.
[4]	YAN Jing, ZHANG Xueying, LI Fenglian, CHEN Guijun, HUANG Lixia. Regression Prediction Model Combining Stack Supervised AE and Variable Weighted ELM [J]. Computer Engineering, 2022, 48(8): 62-69,76.
[5]	WU Tiezhou, ZOU Zhi, JIANG Ben, ZHANG Xiaoxing. Short-Term Load Forecasting Based on TLBGA-GRU Neural Network [J]. Computer Engineering, 2022, 48(11): 69-76.
[6]	DU Xiuli, ZHOU Min, Lü Yana, QIU Shaoming. Effectiveness Evaluation of Equipment Support System Based on Optimized RBF Neural Network [J]. Computer Engineering, 2021, 47(9): 282-287,296.
[7]	WEI Xiuran, WANG Feng. Data Replication Strategy of Cloud Storage Based on Coordinator and Genetic Algorithm [J]. Computer Engineering, 2021, 47(8): 124-130,139.
[8]	ZHENG Juanyi, CUI Zhuo, SU Hailong, YIN Shuaishuai, LIU Yaoyao. Intelligent Method Based on Improved GA-Elman for Predicting the Wireless Propagation Loss [J]. Computer Engineering, 2021, 47(7): 155-160,167.
[9]	YANG Tian, YANG Jun. Offloading Decision and Resource Allocation Strategy in Mobile Edge Computing [J]. Computer Engineering, 2021, 47(2): 19-25.
[10]	LU Rongxiu, HE Quanheng, YANG Hui, ZHU Jianyong. Multi-Component Content Prediction of Rare Earth Mixed Solution Based on GA-ELM [J]. Computer Engineering, 2021, 47(1): 284-290,297.
[11]	WANG Yan, GE Haibo, FENG Anqi. Computation Offloading Strategy in Cloud-Assisted Mobile Edge Computing [J]. Computer Engineering, 2020, 46(8): 27-34.
[12]	LUO Binshen, LIU Limin, DONG Jian, LIU Jingqi. Radar New Jamming Identification Based on SAE-GA-SVM Model [J]. Computer Engineering, 2020, 46(6): 281-287.
[13]	ZHANG Chunfu, WANG Song, WU Yadong, WANG Yong, ZHANG Hongying. Diabetes Risk Prediction Based on GA_Xgboost Model [J]. Computer Engineering, 2020, 46(3): 315-320.
[14]	LI Suikai,LI Yitao,SUN Weiwei,QIN Tao,HUANG Xiusong. A storage space allocation algorithm for export containers in automated container terminals [J]. Computer Engineering, 2019, 45(5): 272-278,284.
[15]	ZHANG Yupei,ZHAO Zhijin,ZHENG Shilian. Design Algorithm of Target Channel Sequence for Spectrum Handoff Based on Multi-objective Genetic [J]. Computer Engineering, 2019, 45(2): 122-128.

Please choose a citation manager

Content to export

Short-term prediction model of building energy consumption based on genetically optimized decision tree

基于遗传优化决策树的建筑能耗短期预测模型

PDF

Knowledge

Cited

Abstract

Cite this article

share this article

References 25

Related Articles 15

Recommended Articles

Metrics

Comments

模态框（Modal）标题

Please choose a citation manager

Content to export

Short-term prediction model of building energy consumption based on genetically optimized decision tree

基于遗传优化决策树的建筑能耗短期预测模型

PDF

Knowledge

Cited

Abstract

Cite this article

share this article

References 25

Related Articles 15

Recommended Articles

Metrics

Comments