scholarly journals Institutional smart buildings energy audit

2019 ◽  
Vol 9 (2) ◽  
pp. 783
Author(s):  
Ali M Baniyounes ◽  
Yazeed Yasin Ghadi ◽  
Ayman Abu Baker
Keyword(s):  
Control Systems ◽  
Building Energy ◽  
Building Automation ◽  
Energy Audit ◽  
Smart Buildings ◽  
Energy Management Systems ◽  
Building Energy Management ◽  
Automation Systems ◽  
Fuzzy Neural ◽  
Save Energy

<span>Smart buildings and Fuzzy based control systems used in Buildings Management System (BMS), Building Energy Management Systems (BEMS) and Building Automation Systems (BAS) are a point of interests among researcher and stake holders of buildings’ developing sector due to its ability to save energy and reduce greenhouse gas emissions. Therefore this paper will review, investigates define and evaluates the use of fuzzy logic controllers in smart buildings under subtropical Australia’s subtropical regions. In addition the paper also will define the latest development, design and proposed controlling strategies used in institutional buildings. Furthermore this paper will highlight and discuss the conceptual basis of these technologies including Fuzzy, Neural and Hybrid add-on technologies, its capabilities and its limitation.</span>

scholarly journals A Data-Driven Forecasting Strategy to Predict Continuous Hourly Energy Demand in Smart Buildings

2021 ◽  
Vol 11 (17) ◽  
pp. 7886
Author(s):  
Deyslen Mariano-Hernández ◽  
Luis Hernández-Callejo ◽  
Martín Solís ◽  
Angel Zorita-Lamadrid ◽  
Oscar Duque-Perez ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Energy Management ◽  
Building Energy ◽  
Management Systems ◽  
Consumption Pattern ◽  
Smart Buildings ◽  
Energy Management Systems ◽  
Forecasting Models ◽  
Building Energy Management ◽  
Occupant Comfort

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.

scholarly journals Architecture For Automation System Metrics Collection, Visualization and Data Engineering – HAMK Sheet Metal Center Building Automation Case Study

2019 ◽  
Vol 9 (1) ◽  
pp. 561-570
Author(s):  
Khoa Dang ◽  
Igor Trotskii
Keyword(s):  
System Integration ◽  
Data Analytics ◽  
Building Energy ◽  
Facility Management ◽  
Automation System ◽  
Building Energy Efficiency ◽  
Building Automation ◽  
Automation Systems ◽  
Integration Data

AbstractEver growing building energy consumption requires advanced automation and monitoring solutions in order to improve building energy efficiency. Furthermore, aggregation of building automation data, similarly to industrial scenarios allows for condition monitoring and fault diagnostics of the Heating, Ventilations and Air Conditioning (HVAC) system. For existing buildings, the commissioned SCADA solutions provide historical trends, alarms management and setpoint curve adjustments, which are essential features for facility management personnel. The development in Internet of Things (IoT) and Industry 4.0, as well as software microservices enables higher system integration, data analytics and rich visualization to be integrated into the existing infrastructure. This paper presents the implementation of a technology stack, which can be used as a framework for improving existing and new building automation systems by increasing interconnection and integrating data analytics solutions. The implementation solution is realized and evaluated for a nearly zero energy building, as a case study.

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