scholarly journals Building Energy Management Strategy Using an HVAC System and Energy Storage System

Energies ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 2690 ◽  
Author(s):  
Nam-Kyu Kim ◽  
Myung-Hyun Shim ◽  
Dongjun Won
Keyword(s):  
Energy Consumption ◽  
Energy Storage ◽  
Energy Management ◽  
Peak Load ◽  
Building Energy ◽  
Load Reduction ◽  
Reduction Algorithm ◽  
Hvac System ◽  
Indoor Temperature ◽  
Building Energy Management

Recently, a worldwide movement to reduce greenhouse gas emissions has emerged, and includes efforts such as the Paris Agreement in 2015. To reduce greenhouse gas emissions, it is important to reduce unnecessary energy consumption or use environmentally-friendly energy sources and consumer products. Many studies have been performed on building energy management systems and energy storage systems (ESSs), which are aimed at efficient energy management. Herein, a heating, ventilation, and air-conditioning (HVAC) system peak load reduction algorithm and an ESS peak load reduction algorithm are proposed. First, an HVAC system accounts for the largest portion of building energy consumption. An HVAC system operates by considering the time-of-use price. However, because the indoor temperature is constantly changing with time, load shifting can be expected only immediately prior to use. Therefore, the primary objective is to reduce the operating time by changing the indoor temperature constraint at the forecasted peak time. Next, numerous research initiatives on ESSs are ongoing. In this study, we aim to systematically design the peak load reduction algorithm of ESS. The structure is designed such that the algorithm can be applied by distinguishing between the peak and non-peak days. Finally, the optimization scheduling simulation is performed. The result shows that the electricity price is minimized by peak load reduction and electricity usage reduction. The proposed algorithm is verified through MATLAB simulations.

A smart material built upon the photo-thermochromic effect and its use for managing indoor temperature

2021 ◽  
Author(s):  
Song Shen ◽  
Tong Wu ◽  
Jiajia Xue ◽  
Haoxuan Li ◽  
Haoyan Cheng ◽  
...  
Keyword(s):  
Energy Management ◽  
Polymer Matrix ◽  
Building Energy ◽  
Smart Material ◽  
Indoor Temperature ◽  
Building Energy Management ◽  
Thermochromic Effect

We demonstrate a material by dispersing a thermochromic mixture of leuco dye, developer, and solvent as microspheres in a polymer matrix to improve the efficiency of building energy management. The...

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.

scholarly journals Data-Driven Predictive Control of Building Energy Consumption under the IoT Architecture

2020 ◽  
Vol 2020 ◽  
pp. 1-20
Author(s):  
Ji Ke ◽  
Yude Qin ◽  
Biao Wang ◽  
Shundong Yang ◽  
Hao Wu ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Internet Of Things ◽  
Energy Management ◽  
Predictive Control ◽  
Control Strategies ◽  
Principal Component ◽  
Building Energy ◽  
Physical Models ◽  
Data Driven ◽  
Building Energy Management

Model predictive control is theoretically suitable for optimal control of the building, which provides a framework for optimizing a given cost function (e.g., energy consumption) subject to constraints (e.g., thermal comfort violations and HVAC system limitations) over the prediction horizon. However, due to the buildings’ heterogeneous nature, control-oriented physical models’ development may be cost and time prohibitive. Data-driven predictive control, integration of the “Internet of Things”, provides an attempt to bypass the need for physical modeling. This work presents an innovative study on a data-driven predictive control (DPC) for building energy management under the four-tier building energy Internet of Things architecture. Here, we develop a cloud-based SCADA building energy management system framework for the standardization of communication protocols and data formats, which is favorable for advanced control strategies implementation. Two DPC strategies based on building predictive models using the regression tree (RT) and the least-squares boosting (LSBoost) algorithms are presented, which are highly interpretable and easy for different stakeholders (end-user, building energy manager, and/or operator) to operate. The predictive model’s complexity is reduced by efficient feature selection to decrease the variables’ dimensionality and further alleviate the DPC optimization problem’s complexity. The selection is dependent on the principal component analysis (PCA) and the importance of disturbance variables (IoD). The proposed strategies are demonstrated both in residential and office buildings. The results show that the DPC-LSBoost has outperformed the DPC-RT and other existing control strategies (MPC, TDNN) in performance, scalability, and robustness.

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.

scholarly journals Optimal Dispatch Strategy of Cogeneration with Thermal Energy Storage for Building Energy Management System

2017 ◽  
Vol 10 (2) ◽  
pp. 156-166
Author(s):  
Kebsiri Manusilp ◽  
David Banjerdpongchai
Keyword(s):  
Energy Storage ◽  
Energy Management ◽  
Thermal Energy ◽  
Building Energy ◽  
Test System ◽  
Optimal Operation ◽  
Energy Management System ◽  
Excessive Heat ◽  
Optimal Dispatch ◽  
Building Energy Management

This paper presents optimal dispatch strategy of cogeneration with thermal energy storage (TES) for building energy management system (BEMS). In previous research related to cogeneration as a supply system, it is observed that there is some excessive heat from cogeneration operation released to the atmosphere. In order to improve energy efficiency, we therefore incorporate TES to utilize the excessive heat from cogeneration into two objective functions, i.e., total operating cost (TOC) and total carbon dioxide emission (TCOE). In particular, we aim to minimize TOC which is referred to economic optimal operation and to minimize TCOE which is referred to environmental optimal operation. Both optimal operations are subjected to energy dispatch strategy which TES constraint is taken into account. We demonstrate the dispatch strategy with a load profile of a large shopping mall as a test system and compare the results to that of previous dispatch of cogeneration without TES. The proposed strategy of cogeneration with TES can reduce TOC of the test system up to 4.15% and 1.85% for economic and environmental optimal operations, respectively. Furthermore, TCOE can be reduced up to 5.25% and 6.25% for economic and environmental optimal operations, respectively.

Building Energy Management: Co-Generation Coupling With Thermal Energy Storage

2002 ◽  
Author(s):  
K. H. Khan ◽  
M. G. Rasul ◽  
M. M. K. Khan
Keyword(s):  
Energy Consumption ◽  
Energy Storage ◽  
Energy Management ◽  
Thermal Energy ◽  
Thermal Energy Storage ◽  
Energy Savings ◽  
Peak Demand ◽  
Building Energy Management ◽  
Demand Reduction ◽  
Chilled Water

This paper is concerned with the feasibility study and evaluation of an energy savings opportunity in buildings energy management using co-generation coupling with thermal energy storage. Both the technical and economical feasibility is presented first for the co-generation and then compared with the co-generation using thermal energy storage. On-site co-generation with double effect absorption chiller provides a potential of at least 13% peak demand reduction and about 16% savings in energy consumption. It provides an internal rate of return (IRR) greater than 21% but saving potential is limited by the low demand of co-generated chilled water within the community of the institution. Thermal energy storage coupling with co-generation offers a simple and economically more attractive approach for maximizing the utilization of co-generated chilled water and shows 23% reduction in peak demand and 21% savings in energy consumption. It provides higher IRR, greater than 25%.

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