An ANN-based fast building energy consumption prediction method for complex architectural form at the early design stage

How to control the growth of building energy consumption and achieve the goal of energy saving and emission reduction while ensuring people’s growing demand for indoor comfort is of great practical significance in the new era. The rapid and accurate prediction of the building energy consumption at the early design stage can provide a quantitative basis for the energy-saving design. ANN (artificial neural network) model is the most widely used artificial intelligence model in the field of building performance optimization due to its high speed, high accuracy, and capability of handling nonlinear relationships between variables. In this paper, an ANN-based fast building energy consumption prediction method for complex architectural form for the early design stage was proposed. Under this method, the authors proposed an idea of architectural form decomposition, to eliminate the complexity of building shape at the early design stage, thus transforming the energy consumption prediction problem of one complex architectural form into several energy consumption prediction problems of multiple simple blocks: the method of characterization decomposition (MCD) and the method of spatial homogenization decomposition (MSHD). The ANN model was introduced to realize energy consumption prediction, which fully utilized the two advantages: high speed and good response to complicated relationships. Accuracy verification shows that the relative deviation of cooling and heating energy consumption is within ±10% using the MCD method. The relative deviation of total energy consumption is within 10% using the MSHD method.

[1]  Paulo Carreira,et al.  Comparative assessment of low-complexity models to predict electricity consumption in an institutional building: Linear regression vs. fuzzy modeling vs. neural networks , 2017 .

[2]  Li Li.  Comparison and Analysis of Energy Consumption Calculation Methods , 1999 .

[3]  P.J.C.J. De Wilde,et al.  Computational Support for the Selection of Energy Saving Building Components , 2004 .

[4]  Giuliano Dall'O',et al.  Application of neural networks for evaluating energy performance certificates of residential buildings , 2016 .

[5]  R. D. Fischer,et al.  Degree-days method for simplified energy analysis , 1982 .

[6]  Rudi Stouffs,et al.  Design explorations of performance driven geometry in architectural design using parametric modeling and genetic algorithms , 2011, Adv. Eng. Informatics.

[7]  Rajesh Kumar,et al.  Energy analysis of a building using artificial neural network: A review , 2013 .

[8]  M. Shin,et al.  Prediction of cooling energy use in buildings using an enthalpy-based cooling degree days method in a hot and humid climate , 2016 .

[9]  Thomas Bednar,et al.  Quantification of safety factors for simplified heating and cooling demand calculation methods for Vienna , 2011 .

[10]  Ibrahim El-Amin,et al.  Artificial neural networks as applied to long-term demand forecasting , 1999, Artif. Intell. Eng..

[11]  Alfonso P. Ramallo-González,et al.  Using self-adaptive optimisation methods to perform sequential optimisation for low-energy building design , 2014 .

[12]  Wei Tian,et al.  Relationship between built form and energy performance of office buildings in a severe cold Chinese region , 2017 .

[13]  Monzón Marta,et al.  Simplified model to determine the energy demand of existing buildings. Case study of social housing in Zaragoza, Spain , 2017 .

[14]  Mohammad S. Al-Homoud,et al.  Computer-aided building energy analysis techniques , 2001 .

[15]  Tuğçe Kazanasmaz,et al.  Comparative study of a building energy performance software (KEP-IYTE-ESS) and ANN-based building heat load estimation , 2014 .

[16]  Joseph Virgone,et al.  Development and validation of regression models to predict monthly heating demand for residential buildings , 2008 .

[17]  Ziwei Li,et al.  A graph- and feature-based building space recognition algorithm for performance simulation in the early design stage , 2018 .

[18]  SU Fen-xian.  Improvement of Method in BIN Building Energy Consumption Calculation , 2006 .

[19]  Tony N.T. Lam,et al.  Artificial neural networks for energy analysis of office buildings with daylighting , 2010 .

[20]  R. Sullivan,et al.  COMMERCIAL BUILDING ENERGY PERFORMANCE ANALYSIS USING MULTIPLE REGRESSION PROCEDURES , 1983 .

[21]  Radiša Jovanović,et al.  Ensemble of various neural networks for prediction of heating energy consumption , 2015 .

[22]  Yousef Mohammadi,et al.  Multi-objective optimization of building envelope design for life cycle environmental performance , 2016 .

[23]  Betul Bektas Ekici,et al.  Prediction of building energy consumption by using artificial neural networks , 2009, Adv. Eng. Softw..

[24]  Mario Sassone,et al.  The early design stage of a building envelope: Multi-objective search through heating, cooling and lighting energy performance analysis , 2015 .

[25]  Yacine Rezgui,et al.  Trees vs Neurons: Comparison between random forest and ANN for high-resolution prediction of building energy consumption , 2017 .

[26]  Zhiqiang John Zhai,et al.  Roles of building simulation tools in sustainable building design , 2014 .

[27]  Karen Kensek.  Building Information Modeling , 2014 .

[28]  Yan Cheng-wen,et al.  Application of ANN for the prediction of building energy consumption at different climate zones with HDD and CDD , 2010, 2010 2nd International Conference on Future Computer and Communication.

[29]  Chirag Deb,et al.  Forecasting diurnal cooling energy load for institutional buildings using Artificial Neural Networks , 2016 .

[30]  Ngoc-Tri Ngo.  Early predicting cooling loads for energy-efficient design in office buildings by machine learning , 2019, Energy and Buildings.

[31]  S. Ranji Ranjithan,et al.  Multivariate regression as an energy assessment tool in early building design , 2012 .

[32]  Jean-Louis Scartezzini,et al.  Passive design optimization of newly-built residential buildings in Shanghai for improving indoor thermal comfort while reducing building energy demand , 2017, Energy and Buildings.

[33]  Tiberiu Catalina,et al.  Multiple regression model for fast prediction of the heating energy demand , 2013 .

[34]  Matthew J. Eckelman,et al.  Predictive modeling for US commercial building energy use: A comparison of existing statistical and machine learning algorithms using CBECS microdata , 2018 .

[35]  Kyle Konis,et al.  Passive performance and building form: An optimization framework for early-stage design support , 2016 .

[36]  Seong-Hwan Yoon,et al.  Stochastic comparison between simplified energy calculation and dynamic simulation , 2013 .

[37]  Mohammad Mottahedi,et al.  On the development of multi-linear regression analysis to assess energy consumption in the early stages of building design , 2014 .

[38]  Ana Paula Melo,et al.  A novel surrogate model to support building energy labelling system: A new approach to assess cooling energy demand in commercial buildings , 2016 .

[39]  Carlos Rubio-Bellido,et al.  Multivariable regression analysis to assess energy consumption and CO2 emissions in the early stages of offices design in Chile , 2016 .

[40]  Ana Paula Melo,et al.  Development and analysis of a metamodel to represent the thermal behavior of naturally ventilated and artificially air-conditioned residential buildings , 2016 .