scholarly journals An Ontology-Based Framework for Building Energy Management with IoT

Electronics ◽  
2019 ◽  
Vol 8 (5) ◽  
pp. 485 ◽  
Author(s):  
Clement Lork ◽  
Vishal Choudhary ◽  
Naveed Ul Hassan ◽  
Wayes Tushar ◽  
Chau Yuen ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Energy Management ◽  
Management System ◽  
Energy Savings ◽  
Environmental Parameters ◽  
Building Energy ◽  
Building Energy Efficiency ◽  
Commercial Building ◽  
Building Energy Management ◽  
Building Management

In this paper, we develop an ontology-based framework for energy management in buildings. We divide the functional architecture of a building energy management system into three interconnected modules that include building management system (BMS), benchmarking (BMK), and evaluation & control (ENC) modules. The BMS module is responsible for measuring several useful environmental parameters, as well as real-time energy consumption of the building. The BMK module provides the necessary information required to understand the context and cause of building energy efficiency or inefficiency, and also the information which can further differentiate normal and abnormal energy consumption in different scenarios. The ENC module evaluates all the information coming from BMS and BMK modules, the information is contextualized, and finally the cause of energy inefficiency/abnormality and mitigating control actions are determined. Methodology to design appropriate ontology and inference rules for various modules is also discussed. With the help of actual data obtained from three different rooms in a commercial building in Singapore, a case study is developed to demonstrate the application and advantages of the proposed framework. By mitigating the appropriate cause of abnormal inefficiency, we can achieve 5.7%, 11.8% and 8.7% energy savings in Room 1, Room 2, and Room 3 respectively, while creating minimum inconvenience for the users.

Effect Evaluation of Introduced Building Energy Management System in University Campus

2013 ◽  
Vol 368-370 ◽  
pp. 1222-1227 ◽  
Author(s):  
Yuan Su ◽  
Jun Wei Yan
Keyword(s):  
Energy Consumption ◽  
Energy Management ◽  
Management System ◽  
Southern China ◽  
Energy System ◽  
Building Energy ◽  
Energy Management System ◽  
University Campus ◽  
Building Energy Management ◽  
Consumption Intensity

Nowadays, universities are taking responsibility for their environmental impact and are working to ensure environmental sustainability. In this research, we aim to analyze energy system of a model university campus in southern China and grasp the energy consumption of the whole campus from the viewpoint of reducing GHG emission. We investigated and analyzed the present situation of energy system by using measured data and inquiry survey. In order to grasp the data exactly, we introduced building energy management system (BEMS) to some typical buildings with electricity consumption controlling. Then examination of energy consumption intensity according the different typical buildings has been analyzed on the basis of the research at campus. The campus's energy consumption prediction was carried out during the 24-h field measurements period. Furthermore, energy consumption intensity of the whole campus were predicted.

Aggregation of Users in a Residential/Commercial Building Managed by a Building Energy Management System (BEMS)

2019 ◽  
Vol 55 (1) ◽  
pp. 26-34 ◽  
Author(s):  
Luigi Martirano ◽  
Giuseppe Parise ◽  
Giacomo Greco ◽  
Matteo Manganelli ◽  
Ferdinando Massarella ◽  
...  
Keyword(s):  
Energy Management ◽  
Management System ◽  
Building Energy ◽  
Energy Management System ◽  
Commercial Building ◽  
Building Energy Management

scholarly journals Reinforcement Learning-Based School Energy Management System

Energies ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 6354
Author(s):  
Yassine Chemingui ◽  
Adel Gastli ◽  
Omar Ellabban
Keyword(s):  
Reinforcement Learning ◽  
Energy Consumption ◽  
Thermal Comfort ◽  
Energy Management ◽  
Management System ◽  
Learning Algorithm ◽  
Building Energy ◽  
High Energy ◽  
Energy Management System ◽  
Building Energy Management

Energy efficiency is a key to reduced carbon footprint, savings on energy bills, and sustainability for future generations. For instance, in hot climate countries such as Qatar, buildings are high energy consumers due to air conditioning that resulted from high temperatures and humidity. Optimizing the building energy management system will reduce unnecessary energy consumptions, improve indoor environmental conditions, maximize building occupant’s comfort, and limit building greenhouse gas emissions. However, lowering energy consumption cannot be done despite the occupants’ comfort. Solutions must take into account these tradeoffs. Conventional Building Energy Management methods suffer from a high dimensional and complex control environment. In recent years, the Deep Reinforcement Learning algorithm, applying neural networks for function approximation, shows promising results in handling such complex problems. In this work, a Deep Reinforcement Learning agent is proposed for controlling and optimizing a school building’s energy consumption. It is designed to search for optimal policies to minimize energy consumption, maintain thermal comfort, and reduce indoor contaminant levels in a challenging 21-zone environment. First, the agent is trained with the baseline in a supervised learning framework. After cloning the baseline strategy, the agent learns with proximal policy optimization in an actor-critic framework. The performance is evaluated on a school model simulated environment considering thermal comfort, CO2 levels, and energy consumption. The proposed methodology can achieve a 21% reduction in energy consumption, a 44% better thermal comfort, and healthier CO2 concentrations over a one-year simulation, with reduced training time thanks to the integration of the behavior cloning learning technique.

scholarly journals Housing Development Building Management System (HDBMS) For Optimized Electricity Bills

2017 ◽  
Vol 2 (2) ◽  
pp. 64 ◽  
Author(s):  
Weixian Li ◽  
Thillainathan Logenthiran ◽  
Van-Tung Phan ◽  
Wai Lok Woo
Keyword(s):  
Energy Management ◽  
Management System ◽  
Building Energy ◽  
Housing Development ◽  
Smart Buildings ◽  
Power Distribution System ◽  
Building Energy Management ◽  
Building Management ◽  
Building Management System ◽  
Multi Agent

Smart Buildings is a modern building that allows residents to have sustainable comfort with high efficiency of electricity usage. These objectives could be achieved by applying appropriate, capable optimization algorithms and techniques. This paper presents a Housing Development Building Management System (HDBMS) strategy inspired by Building Energy Management System (BEMS) concept that will integrate with smart buildings using Supply Side Management (SSM) and Demand Side Management (DSM) System. HDBMS is a Multi-Agent System (MAS) based decentralized decision making system proposed by various authors. MAS based HDBMS was created using JAVA on a IEEE FIPA compliant multi-agent platform named JADE. It allows agents to communicate, interact and negotiate with energy supply and demand of the smart buildings to provide the optimal energy usage and minimal electricity costs.  This results in reducing the load of the power distribution system in smart buildings which simulation studies has shown the potential of proposed HDBMS strategy to provide the optimal solution for smart building energy management.

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