Application of neural networks for evaluating energy performance certificates of residential buildings

被引:86
作者
Khayatian, Fazel [1 ]
Sarto, Luca [1 ]
Dall'O', Giuliano [1 ]
机构
[1] Politecn Milan, Dept Architecture Built Environm & Construct Engn, Milan, Italy
关键词
Energy performance certificate (EPC); Artificial neural networks (ANN); Energy certificate validation; Residential buildings; COOLING-LOAD; CONSUMPTION; PREDICTION;
D O I
10.1016/j.enbuild.2016.04.067
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
The Energy Performance Building Directive 91 of 2002, mandates Member States of the European Union to enforce energy certification of buildings through local legislation. Among the Italian regions, Lombardy has issued predicted energy performance certificates for buildings since 2007 which accumulate to over one million entries. The current study is an attempt to validate a dataset of energy certificates by benefitting from the magnitude of registered buildings. Considering that manual evaluation of every entry is exhaustive and time consuming, artificial neural network is used as a fast and robust alternative for predicting heat demand indicators. Various combinations of input features are compared for selecting a reliable model. The number of inputs and hidden neurons are also optimized in order to achieve better accuracy. Results show that using 12 variables from an energy certificate is sufficient for estimating the related heat demand indicator. Regarding the stochastic initialization of neural networks, a set of 100 models are trained for obtaining a frequency distribution and confidence interval. Final results indicate that about 95% of entries fall within 3 confidence intervals. (C) 2016 Elsevier B.V. All rights reserved.
引用
收藏
页码:45 / 54
页数:10
相关论文
共 25 条
[1]   A review on applications of ANN and SVM for building electrical energy consumption forecasting [J].
Ahmad, A. S. ;
Hassan, M. Y. ;
Abdullah, M. P. ;
Rahman, H. A. ;
Hussin, F. ;
Abdullah, H. ;
Saidur, R. .
RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2014, 33 :102-109
[2]  
Alpaydin E., 2010, INTRO MACHINE LEARNI
[3]  
[Anonymous], 2008, ENERGY PERFORMANCE B
[4]   Modeling of the appliance, lighting, and space-cooling energy consumptions in the residential sector using neural networks [J].
Aydinalp, M ;
Ugursal, VI ;
Fung, AS .
APPLIED ENERGY, 2002, 71 (02) :87-110
[5]   Comparison of neural network, conditional demand analysis, and engineering approaches for modeling end-use energy consumption in the residential sector [J].
Aydinalp-Koksal, Merih ;
Ugursal, V. Ismet .
APPLIED ENERGY, 2008, 85 (04) :271-296
[6]   Integration of artificial neural networks and genetic algorithm to predict electrical energy consumption [J].
Azadeh, A. ;
Ghaderi, S. F. ;
Tarverdian, S. ;
Saberi, M. .
APPLIED MATHEMATICS AND COMPUTATION, 2007, 186 (02) :1731-1741
[7]   An original tool for checking energy performance and certification of buildings by means of Artificial Neural Networks [J].
Buratti, C. ;
Barbanera, M. ;
Palladino, D. .
APPLIED ENERGY, 2014, 120 :125-132
[8]   On the use of an energy certification database to create indicators for energy planning purposes: Application in northern Italy [J].
Dall'O, Giuliano ;
Sarto, Luca ;
Sanna, Nicola ;
Tonetti, Valeria ;
Ventura, Martina .
ENERGY POLICY, 2015, 85 :207-217
[9]   Nearly Zero-Energy Buildings of the Lombardy Region (Italy), a Case Study of High-Energy Performance Buildings [J].
Dall'O', Giuliano ;
Belli, Valentina ;
Brolis, Mauro ;
Mozzi, Ivan ;
Fasano, Mauro .
ENERGIES, 2013, 6 (07) :3506-3527
[10]   Prediction of building energy consumption by using artificial neural networks [J].
Ekici, Betul Bektas ;
Aksoy, U. Teoman .
ADVANCES IN ENGINEERING SOFTWARE, 2009, 40 (05) :356-362