BIM-Based Machine Learning Application for Parametric Assessment of Building Energy Performance

被引:0
作者
Tsikas, Panagiotis [1 ]
Chassiakos, Athanasios [1 ]
Papadimitropoulos, Vasileios [1 ]
Papamanolis, Antonios [1 ]
机构
[1] Univ Patras, Dept Civil Engn, Patras 26500, Greece
关键词
building; energy; building information modeling; building energy modeling; machine learning; random forest; artificial neural network; DATA-DRIVEN; PREDICTION; CONSUMPTION;
D O I
10.3390/en18010201
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
The energy performance of buildings has become a main concern globally in response to increased energy demand, the environmental impacts of energy production, and the reality of energy poverty. To improve energy efficiency, proper building design should be secured at the early design phase. Digital tools are currently available for performing energy assessment analyses and can efficiently handle complex and technically demanding buildings. However, alternative designs should be checked individually, and this makes the process time-consuming and prone to errors. Machine learning techniques can provide valuable assistance in developing decision support tools. In this paper, typical residential buildings are considered along with eleven factors that highly affect energy performance. A dataset of 337 instances of such parameters is developed. For each dataset, the building energy performance is estimated based on BIM analysis. Next, statistical and machine learning techniques are implemented to provide artificial models of energy performance. They include statistical regression modeling (SRM), decision trees (DTs), random forests (RFs), and artificial neural networks (ANNs). The analysis reveals the contribution of each factor and highlights the ANN as the best performing model. An easy-to-use interface tool has been developed for the instantaneous calculation of the energy performance based on the independent parameter values.
引用
收藏
页数:24
相关论文
共 34 条
[11]   The promise of BIM for improving building performance [J].
Habibi, Shahryar .
ENERGY AND BUILDINGS, 2017, 153 :525-548
[12]   Machine-learning phase prediction of high-entropy alloys [J].
Huang, Wenjiang ;
Martin, Pedro ;
Zhuang, Houlong L. .
ACTA MATERIALIA, 2019, 169 :225-236
[13]   Optimal location and thickness of insulation layers for minimizing building energy consumption [J].
Ibrahim, Mohamad ;
Ghaddar, Nesreen ;
Ghali, Kamel .
JOURNAL OF BUILDING PERFORMANCE SIMULATION, 2012, 5 (06) :384-398
[14]   Energy demand forecasting in seven sectors by an optimization model based on machine learning algorithms [J].
Javanmard, Majid Emami ;
Ghaderi, S. F. .
SUSTAINABLE CITIES AND SOCIETY, 2023, 95
[15]   Improving the prediction of heating energy consumed at residential buildings using a combination of support vector regression and meta-heuristic algorithms [J].
Khajavi, Hamed ;
Rastgoo, Amir .
ENERGY, 2023, 272
[16]   Performance evaluation of short-term cross-building energy predictions using deep transfer learning strategies [J].
Li, Guannan ;
Wu, Yubei ;
Liu, Jiangyan ;
Fang, Xi ;
Wang, Zixi .
ENERGY AND BUILDINGS, 2022, 275
[17]   BIM AND ORTHOGONAL TEST METHODS TO OPTIMIZE THE ENERGY CONSUMPTION OF GREEN BUILDINGS [J].
Li, Xiaojuan ;
Lin, Mingchao ;
Jiang, Ming ;
Jim, C. Y. ;
Liu, Ke ;
Tserng, Huipin .
JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT, 2024, 30 (08) :670-690
[18]   Automated machine learning-based framework of heating and cooling load prediction for quick residential building design [J].
Lu, Chujie ;
Li, Sihui ;
Penaka, Santhan Reddy ;
Olofsson, Thomas .
ENERGY, 2023, 274
[19]   Bim-based energy analysis and optimization using insight 360 (case study) [J].
Maglad, Ahmed M. ;
Houda, Moustafa ;
Alrowais, Raid ;
Khan, Abdul Mateen ;
Jameel, Mohammed ;
Rehman, Sardar Kashif Ur ;
Khan, Hamza ;
Javed, Muhammad Faisal ;
Rehman, Muhammad Faisal .
CASE STUDIES IN CONSTRUCTION MATERIALS, 2023, 18
[20]   A Multi-Step Approach to Assess the Lifecycle Economic Impact of Seismic Risk on Optimal Energy Retrofit [J].
Mauro, Gerardo Maria ;
Menna, Costantino ;
Vitiello, Umberto ;
Asprone, Domenico ;
Ascione, Fabrizio ;
Bianco, Nicola ;
Prota, Andrea ;
Vanoli, Giuseppe Peter .
SUSTAINABILITY, 2017, 9 (06)