scholarly journals Application of Artificial Neural Networks in the Urban Building Energy Modelling of Polish Residential Building Stock

Energies ◽  
2021 ◽  
Vol 14 (24) ◽  
pp. 8285
Author(s):  
Marcin Zygmunt ◽  
Dariusz Gawin
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Planning Process ◽  
Residential Building ◽  
Building Energy ◽  
Building Stock ◽  
Energy Efficient Buildings ◽  
Energy Modelling ◽  
Residential Building Stock ◽  
Artificial Neural

The development of energy-efficient buildings and sustainable energy supply systems is an obligatory undertaking towards a more sustainable future. To protect the natural environment, the modernization of urban infrastructure is indisputably important, possible to achieve considering numerous buildings as a group, i.e., Building Energy Cluster (BEC). The urban planning process evaluates multiple complex criteria to select the most profitable scenario in terms of energy consumption, environmental protection, or financial profitability. Thus, Urban Building Energy Modelling (UBEM) is presently a popular approach applied for studies towards the development of sustainable cities. Today’s UBEM tools use various calculation methods and approaches, as well as include different assumptions and limitations. While there are several popular and valuable software for UBEM, there is still no such tool for analyses of the Polish residential stock. In this work an overview on the home-developed tool called TEAC, focusing on its’ mathematical model and use of Artificial Neural Networks (ANN). An exemplary application of the TEAC software is also presented.

scholarly journals Building Energy Modeling Using Artificial Neural Networks

2016 ◽  
Vol 7 (2) ◽  
pp. 24-34 ◽  
Author(s):  
Maya Arida ◽  
Nabil Nassif ◽  
Rand Talib ◽  
Taher Abu-Lebdeh
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Building Energy ◽  
Energy Modeling ◽  
Building Energy Modeling ◽  
Artificial Neural

scholarly journals Using Artificial Neural Networks for Indoor Climate Control in the Field of Preventive Conservation

2019 ◽  
Vol 111 ◽  
pp. 04054
Author(s):  
Simon Harasty ◽  
Andreas Daniel Böttcher ◽  
Steven Lambeck
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Control Strategy ◽  
Ann Model ◽  
Indoor Climate ◽  
Climate Control ◽  
Building Stock ◽  
Preventive Conservation ◽  
Predictive Controller ◽  
Artificial Neural

In the field of preventive conservation, a main goal is the conservation of cultural heritage by establishing an appropriate indoor climate. Especially in applications with limited possibilities for the usage of HVAC systems, an optimization of the control strategy is needed. Because the changes in temperature and humidity are slow, the usage of predictive controller can be beneficial. Due to the availability of already gathered data, data driven models like artificial neural networks (ANN) are suitable as model. In this paper four different approaches for optimizing the control strategy regarding the requirements of preventive conservation are presented. The first approach is the modelling of the indoor climate of a building using an ANN. As further improvement and second application the adaption of a weather forecast to a local forecast is shown. Since the building stock has the biggest influence on the linkage between outdoor and indoor climate next to the air change rates, an ANN model for a building’s wall represents the third application. Finally, the potential for reducing the need for computational power by using an ANN instead of a non-linear optimization for the predictive controller is presented.

scholarly journals Predicting the impact of traffic–induced vibrations on buildings using artificial neural networks

2018 ◽  
Vol 219 ◽  
pp. 04004 ◽  
Author(s):  
Anna Jakubczyk-Gałczyńska
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Residential Buildings ◽  
High Reliability ◽  
Residential Building ◽  
Field Studies ◽  
Good Tool ◽  
Original Algorithm ◽  
Artificial Neural ◽  
The Impact

Traffic–induced vibrations may constitute a considerable load to a building, cause cracking of plaster, cracks in load–bearing elements or even a global structural collapse of the whole structure [1-4]. Vibrations measurements of real structures are costly and laborious, not justified in all cases. The aim of the paper is to create an original algorithm, to predict the negative dynamic impact on the examined residential building with a high probability. The model to forecast the impact of vibrations on buildings is based on artificial neural networks [5]. The author’s own field studies carried out according to the Polish standard [6] and literature examples [7-10] have been used to create the algorithms. The results of the conducted analysis show that an artificial neural network can be considered a good tool to predict the impact of traffic–induced vibrations on residential buildings, with a sufficiently high reliability.

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