scholarly journals Evaluating the Economic and Environmental Impacts of Smart Management Systems for Cooling and Heating Systems in Building: Case study of Office Building in Tehran

Author(s):  
Gholamreza Heravi ◽  
Milad Rostami ◽  
Maryam Shekari

Considering the increasing rate of energy consumption and its environmental detrimental effects, as well as considering the use of non-renewable energy sources such as fossil fuels, energy management issues have become more important. Given the 40% share of the building industry's total energy consumption, as well as the 80% share of energy consumed during the operation period, attention to the areas of energy management and optimization during the operation period of the buildings can have a major impact on buildings’ energy performance. In this research, through identifying building energy management tools and studying previous studies and assessing the effects of building energy management systems, the economic and environmental impacts of using building energy management systems on the annual energy consumption in an office building in Tehran as a case study has been investigated. The results indicate a 32 percent reduction in energy consumption and a significant reduction in the release of the environmental pollutants in smart mode compared to the base mode. Moreover, considering the social costs associated with the emitted pollutants as well as the return period, it has been attempted to identify the factors contributing to the economic justification of using smart heating and cooling systems. According to the results, the use of smart energy management systems can be considered as an effective step in optimizing and managing energy consumption in the construction sector.

2021 ◽  
Vol 11 (17) ◽  
pp. 7886
Author(s):  
Deyslen Mariano-Hernández ◽  
Luis Hernández-Callejo ◽  
Martín Solís ◽  
Angel Zorita-Lamadrid ◽  
Oscar Duque-Perez ◽  
...  

Smart buildings seek to have a balance between energy consumption and occupant comfort. To make this possible, smart buildings need to be able to foresee sudden changes in the building’s energy consumption. With the help of forecasting models, building energy management systems, which are a fundamental part of smart buildings, know when sudden changes in the energy consumption pattern could occur. Currently, different forecasting methods use models that allow building energy management systems to forecast energy consumption. Due to this, it is increasingly necessary to have appropriate forecasting models to be able to maintain a balance between energy consumption and occupant comfort. The objective of this paper is to present an energy consumption forecasting strategy that allows hourly day-ahead predictions. The presented forecasting strategy is tested using real data from two buildings located in Valladolid, Spain. Different machine learning and deep learning models were used to analyze which could perform better with the proposed strategy. After establishing the performance of the models, a model was assembled using the mean of the prediction values of the top five models to obtain a model with better performance.


Author(s):  
Cristina Nichiforov ◽  
Grigore Stamatescu ◽  
Iulia Stamatescu ◽  
Ioana Fagarasan ◽  
Sergiu Stelian Iliescu

2017 ◽  
Vol 50 (1) ◽  
pp. 12027-12032 ◽  
Author(s):  
Amanda Abreu ◽  
Romain Bourdais ◽  
Hérvé Guéguen

2021 ◽  
pp. 1-18
Author(s):  
Murat Kuzlu ◽  
Manisa Pipattanasomporn ◽  
Onur Elma

2016 ◽  
Vol 10 (1) ◽  
pp. 25-39 ◽  
Author(s):  
Milos Manic ◽  
Dumidu Wijayasekara ◽  
Kasun Amarasinghe ◽  
Juan J. Rodriguez-Andina

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 40402-40415 ◽  
Author(s):  
Zhishu Shen ◽  
Tiehua Zhang ◽  
Jiong Jin ◽  
Kenji Yokota ◽  
Atsushi Tagami ◽  
...  

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