Application of Artificial Neural Networks onBuilding Energy Estimation

2017-12-11
Abstract -The decision makers in the building design stage need to give more importance for energy efficient solutions to improve the performance the building in its operational phase. In practice, they are expected to generate alternative designs in the early design stage and use complicated building simulation tools to estimate building energy use of these alternatives more accurately; however, this is a time-consuming procedure and it is difficult to evaluate different alternatives in the limited time of a design period. For this reason, emerging artificial intelligence (AI) algorithms are becoming popular amongst practitioners in the industry to study the energy performance evaluation. Their inherently fast nature makes AI algorithms suitable for this topic depending on the database generated. In this study, an Artificial Neural Network (ANN) is developed to estimate energy performance of the buildings efficiently based on a previously generated dataset including building properties and energy loads. The ANN is constructed based on different network topologies, activation functions and learning rates combinations to provide more accurate results on heating and cooling loads. The results of the study are presented and compared with the results in the previous studies. It shows the right parameter combination for ANN generates quite competitive results with the ones from the previous studies.
International Conference on Engineering Technologies, (7 - 09 Aralık 2017)

Suggestions

Thermal performance optimization of building aspect ratio and south window size in five cities having different climatic characteristics of Turkey
Inanici, MN; Demirbilek, FN (2000-01-01)
The aim of the study is to establish optimum building aspect ratios and south window sizes of residential buildings from thermal performance point of view. The effects of 6 different building aspect ratios and eight different south window sizes for each building aspect ratio are analyzed for apartments located at intermediate floors of buildings, by the aid of the computer based thermal analysis program SUNCODE-PC in five cities of Turkey: Erzurum, Ankara, Diyarbakir, Izmir, and Antalya. The results are eva...
Artificial intelligence applications in earthquake resistant architectural design: Determination of irregular structural systems with deep learning and ImageAI method
Bingol, Kaan; Akan, Asli Er; ÖRMECİOĞLU, HİLAL TUĞBA; ER, ARZU (Journal of the Faculty of Engineering and Architecture of Gazi University, 2020-01-01)
Although the architectural design process is carried out with the collaboration of experts who are experienced in many different areas from the main preferences to the detailing stage, the major decisions such as plan organization, mass design etc. are taken by the architect. Computer Aided Design (CAD) programs are generally effective after the major decisions of the design are taken. For this reason, it is common for the main decisions, taken during the design process, to be changed during the analysis of...
Energy based seismic assessment and design
Alıcı, Fırat Soner; Sucuoğlu, Haluk; Erberik, Murat Altuğ; Department of Civil Engineering (2019)
The need to improve the reliability of current earthquake resistant design procedures has promoted energy-based concepts that employ seismic input energy and energy dissipation capacity of structures as the main design tools. Energy based approaches provide effective tools at both design and assessment stages for a comprehensive interpretation of the seismic behavior of structural systems during an earthquake excitation. Energy based assessment and design procedure includes two crucial aspects. The first on...
Image-based construction of building energy models using computer vision
Gürsel Dino, İpek; Iseri, Orcun Koral; Akin, Sahin; Kalfaoglu, Esat; Erdogan, Bilge; Kalkan, Sinan; Alatan, Abdullah Aydın (Elsevier BV, 2020-08-01)
Improving existing buildings' energy performance requires energy models that accurately represent the building. Computer vision methods, particularly image-based 3D reconstruction, can effectively support the creation of 3D building models. In this paper, we present an image-based 3D reconstruction pipeline that supports the semi-automated modeling of existing buildings. We developed two methods for the robust estimation of the building planes from a 3D point cloud that (i) independently estimate each plane...
Energy benchmarking for residential buildings
Tereci, Aysegul; Elias Özkan, Soofia Tahira; Eicker, Ursula (2013-05-01)
The impact of the urban form on the energy demand of buildings is difficult to quantify as, usually, the mutual shading of buildings in urban settings is not included in dynamic building simulations. As a result, there is not much information available on how the total primary energy demand is affected by the specific urban form.
Citation Formats
M. Altun, A. B. Ersöz, A. Akçamete Güngör, and O. Pekcan, “Application of Artificial Neural Networks onBuilding Energy Estimation,” presented at the International Conference on Engineering Technologies, (7 - 09 Aralık 2017), Konya, Türkiye, 2017, Accessed: 00, 2021. [Online]. Available: http://icente.selcuk.edu.tr/uploads/files2/ICENTE_proceeding_V3.pdf.