scholarly journals Energy Savings Potential and RD&D Opportunities for Commercial Building HVAC Systems

2017 ◽  
Author(s):  
William Goetzler ◽  
◽  
Richard Shandross ◽  
Jim Young ◽  
Oxana Petritchenko ◽  
...  
Keyword(s):  
Energy Savings ◽  
Hvac Systems ◽  
Commercial Building

scholarly journals A Computer Vision-Based Occupancy and Equipment Usage Detection Approach for Reducing Building Energy Demand

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 156
Author(s):  
Paige Wenbin Tien ◽  
Shuangyu Wei ◽  
John Calautit
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Energy Demand ◽  
Energy Savings ◽  
Internal Heat ◽  
Electrical Equipment ◽  
Building Energy ◽  
Hvac Systems ◽  
Equipment Usage

Because of extensive variations in occupancy patterns around office space environments and their use of electrical equipment, accurate occupants’ behaviour detection is valuable for reducing the building energy demand and carbon emissions. Using the collected occupancy information, building energy management system can automatically adjust the operation of heating, ventilation and air-conditioning (HVAC) systems to meet the actual demands in different conditioned spaces in real-time. Existing and commonly used ‘fixed’ schedules for HVAC systems are not sufficient and cannot adjust based on the dynamic changes in building environments. This study proposes a vision-based occupancy and equipment usage detection method based on deep learning for demand-driven control systems. A model based on region-based convolutional neural network (R-CNN) was developed, trained and deployed to a camera for real-time detection of occupancy activities and equipment usage. Experiments tests within a case study office room suggested an overall accuracy of 97.32% and 80.80%. In order to predict the energy savings that can be attained using the proposed approach, the case study building was simulated. The simulation results revealed that the heat gains could be over or under predicted when using static or fixed profiles. Based on the set conditions, the equipment and occupancy gains were 65.75% and 32.74% lower when using the deep learning approach. Overall, the study showed the capabilities of the proposed approach in detecting and recognising multiple occupants’ activities and equipment usage and providing an alternative to estimate the internal heat emissions.

Development and Validation of Online Models With Parameter Estimation for a Building Zone With VAV System

2004 ◽  
Author(s):  
Jin Wen ◽  
Theodore F. Smith
Keyword(s):  
Parameter Estimation ◽  
Air Flow ◽  
Hvac Systems ◽  
Test Facility ◽  
Prediction Errors ◽  
Commercial Building ◽  
Variable Air Volume ◽  
Online System ◽  
System Models ◽  
Validation Tests

The energy consumption by building heating, ventilating, and air conditioning (HVAC) systems has evoked more attention for energy efficient HVAC control and operation. Application of advanced control and operation strategies requires robust online system models. In this research, online models with parameter estimation for a building zone with variable air volume (VAV) system, which is one of the most common HVAC systems, are developed and validated using experimental data. Building zone temperature and VAV entering air flow are modeled based on physical rules and using only the measurements that are commonly available in a commercial building. Different series of validation tests were performed in a real-building test facility to examine the prediction accuracies for system outputs. Using the online system models with parameter estimation, the prediction errors for all the validation tests are less than 0.5°F for temperature outputs, and less than 50 ft3/min for air flow outputs. The online models can be further used for local and supervisory control, as well as fault detection applications.

Predictions of energy savings in HVAC systems by lumped models

2010 ◽  
Vol 42 (10) ◽  
pp. 1807-1814 ◽  
Author(s):  
A.P. Wemhoff ◽  
M.V. Frank
Keyword(s):  
Energy Savings ◽  
Hvac Systems ◽  
Lumped Models

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 Development and Implementation of Fault-Correction Algorithms in Fault Detection and Diagnostics Tools

Energies ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 2598
Author(s):  
Guanjing Lin ◽  
Marco Pritoni ◽  
Yimin Chen ◽  
Jessica Granderson
Keyword(s):  
Fault Detection ◽  
Energy Savings ◽  
Leading Edge ◽  
Hvac Systems ◽  
Automation System ◽  
Building Automation ◽  
Software Products ◽  
Automation Systems ◽  
Building Automation System ◽  
Staff Intervention

A fault detection and diagnostics (FDD) tool is a type of energy management and information system that continuously identifies the presence of faults and efficiency improvement opportunities through a one-way interface to the building automation system and the application of automated analytics. Building operators on the leading edge of technology adoption use FDD tools to enable median whole-building portfolio savings of 8%. Although FDD tools can inform operators of operational faults, currently an action is always required to correct the faults to generate energy savings. A subset of faults, however, such as biased sensors, can be addressed automatically, eliminating the need for staff intervention. Automating this fault “correction” can significantly increase the savings generated by FDD tools and reduce the reliance on human intervention. Doing so is expected to advance the usability and technical and economic performance of FDD technologies. This paper presents the development of nine innovative fault auto-correction algorithms for Heating, Ventilation, and Air Conditioning pi(HVAC) systems. When the auto-correction routine is triggered, it overwrites control setpoints or other variables to implement the intended changes. It also discusses the implementation of the auto-correction algorithms in commercial FDD software products, the integration of these strategies with building automation systems and their preliminary testing.

scholarly journals Dynamic simulation of indirect air conditioning systems with optimized computational time

2019 ◽  
Vol 111 ◽  
pp. 04042
Author(s):  
Nicolás Ablanque ◽  
Santiago Torras ◽  
Carles Oliet ◽  
Joaquim Rigola ◽  
Carlos-David Pérez-Segarra
Keyword(s):  
Air Conditioning ◽  
Energy Savings ◽  
Control Strategies ◽  
Model Potential ◽  
Hvac Systems ◽  
Object Oriented Programming ◽  
Computational Time ◽  
Air Conditioning System ◽  
Numerical Resolution ◽  
Air Conditioning Systems

The simulation of HVAC systems is a powerful tool to improve the energy efficiency in buildings. The modelling of such systems faces several obstacles due to both the physical phenomenology present and the numerical resolution difficulties. The present work is an attempt to develop a robust, fast, and accurate model for HVAC systems that can interact with the other relevant systems involved in buildings thermal management. The whole system model has been developed in the form of libraries under the Modelica language to exploit its advantageous characteristics: object-oriented programming, equationbased modelling, and handling of multi-physics. The global resolution is carried out dynamically so that not only steady-state predictions can be conducted but also control strategies can be studied over meaningful periods of time. This latter aspect is crucial for optimizing energy savings. The libraries include models for all the system individual components such as pumps, compressors or heat exchangers (operating with twophase flows and/or moist air) and also models assemblies to account for vapour compression units and liquid circuits. An illustrative example of an indirect air conditioning system is detailed in the present work in order to highlight the model potential.

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