Prediction of building energy needs in early stage of design by using ANFIS

被引:153
作者
Ekici, Betul Bektas [1 ]
Aksoy, U. Teoman [1 ]
机构
[1] Firat Univ, Dept Construct Educ, TR-23119 Elazig, Turkey
关键词
ANFIS; Heating energy; Cooling energy; Insulation; Orientation; NEURO-FUZZY INFERENCE; THERMAL COMFORT; NATURAL VENTILATION; SYSTEM; HEAT; MODEL; SIMULATION; INSULATION; EFFICIENCY; DEMAND;
D O I
10.1016/j.eswa.2010.10.021
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Studies performed on the prediction of building energy consumption are increasingly important for selecting the best control strategies against the excessive energy consumptions. This paper presents Adaptive Network Based Inference System (ANFIS) model to forecast building energy consumption in a cold region. The objective of this paper is to examine the feasibility and applicability of ANFIS in building energy load forecasting area. Different combinations of building samples formed by using three different form factors (FF 1/2, FF 1/1 and FF 2/1), nine azimuth angles varied 0 degrees-80 degrees, three transparency ratios of 15%, 20%, 25% and five insulation thicknesses of 0, 2.5, 5, 10 and 15 cm. Finally, it is observed that ANFIS can be a strong tool with the 96.5 and 83.8% for heating and cooling energy prediction in pre-design stage of energy efficient buildings for choosing the best combinations. (C) 2010 Elsevier Ltd. All rights reserved.
引用
收藏
页码:5352 / 5358
页数:7
相关论文
共 31 条
[1]   Impacts of some building passive design parameters on heating demand for a cold region [J].
Aksoy, U. Teoman ;
Inalli, Mustafa .
BUILDING AND ENVIRONMENT, 2006, 41 (12) :1742-1754
[2]  
AKSOY UT, 2002, THESIS FIRAT U ELAZI
[3]   A neuro-fuzzy model for prediction of the indoor temperature in typical Australian residential buildings [J].
Alasha'ary, Haitham ;
Moghtaderi, Behdad ;
Page, Adrian ;
Sugo, Heber .
ENERGY AND BUILDINGS, 2009, 41 (07) :703-710
[4]   Adaptive neuro-fuzzy inference systems (ANFIS) application to investigate potential use of natural ventilation in new building designs in Turkey [J].
Ayata, Tahir ;
Cam, Ertugrul ;
Yildiz, Osman .
ENERGY CONVERSION AND MANAGEMENT, 2007, 48 (05) :1472-1479
[5]   An adaptive neuro-fuzzy inference system (ANFIS) model for wire-EDM [J].
Caydas, Ulas ;
Hascalik, Ahmet ;
Ekici, Sami .
EXPERT SYSTEMS WITH APPLICATIONS, 2009, 36 (03) :6135-6139
[6]   Performance rating of glass windows and glass windows with films in aspect of thermal comfort and heat transmission [J].
Chaiyapinunt, S ;
Phueakphongsuriya, B ;
Mongkomsaksit, K ;
Khomporn, N .
ENERGY AND BUILDINGS, 2005, 37 (07) :725-738
[7]   Reinforcement learning for energy conservation and comfort in buildings [J].
Dalamagkidis, K. ;
Kolokotsa, D. ;
Kalaitzakis, K. ;
Stavrakakis, G. S. .
BUILDING AND ENVIRONMENT, 2007, 42 (07) :2686-2698
[8]   Adaptive fuzzy model identification to predict the heat transfer coefficient in pool boiling of distilled water [J].
Das, Mihir K. ;
Kishor, Nand .
EXPERT SYSTEMS WITH APPLICATIONS, 2009, 36 (02) :1142-1154
[9]   Simulation of energy saving in Iranian buildings using integrative modelling for insulation [J].
Farhanieh, B ;
Sattari, S .
RENEWABLE ENERGY, 2006, 31 (04) :417-425
[10]   A comparative analysis of urban and rural residential thermal comfort under natural ventilation environment [J].
Han, Jie ;
Yang, Wei ;
Zhou, Jin ;
Zhang, Guoqiang ;
Zhang, Quan ;
Moschandreas, Demetrios J. .
ENERGY AND BUILDINGS, 2009, 41 (02) :139-145