Predictions of energy savings in HVAC systems by lumped models

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.

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Energy savings in HVAC systems using discrete model-based predictive control

The 2012 International Joint Conference on Neural Networks (IJCNN) ◽

10.1109/ijcnn.2012.6252538 ◽

2012 ◽

Cited By ~ 7

Author(s):

Pedro M. Ferreira ◽

Sergio M. Silva ◽

Antonio E. Ruano

Keyword(s):

Predictive Control ◽

Discrete Model ◽

Energy Savings ◽

Hvac Systems ◽

Model Based

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

Energies ◽

10.3390/en13102598 ◽

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.

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Dynamic simulation of indirect air conditioning systems with optimized computational time

E3S Web of Conferences ◽

10.1051/e3sconf/201911104042 ◽

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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Optimal Setpoints for HVAC Systems via Iterative Cooperative Neighbor Communication

Journal of Dynamic Systems Measurement and Control ◽

10.1115/1.4027887 ◽

2014 ◽

Vol 137 (1) ◽

Cited By ~ 5

Author(s):

Matthew Elliott ◽

Bryan P. Rasmussen

Keyword(s):

Energy Savings ◽

Optimization Technique ◽

Optimal Solution ◽

Hvac Systems ◽

Global Optimum ◽

Cost Information ◽

Steady State Condition ◽

Air Conditioning Systems ◽

The Cost ◽

Dynamic Subsystem

Heating, ventilation, and air conditioning systems in large buildings frequently feature a network topology wherein the outputs of each dynamic subsystem act as disturbances to other subsystems. The distributed optimization technique presented in this paper leverages this topology without requiring a centralized controller or widespread knowledge of the interaction dynamics between subsystems. Each subsystem's controller calculates an optimal steady state condition. The output corresponding to this condition is then communicated to downstream neighbors only. Similarly, each subsystem communicates to its upstream neighbors the predicted costs imposed by the neighbors' own calculated outputs. By judicious construction of the cost functions, all of the cost information is propagated through the network, allowing a Pareto optimal solution to be reached. The novelty of this approach is that communication between all plants is not necessary to achieve a global optimum. Since each optimizer does not require knowledge of its neighbors' dynamics, changes in one controller do not require changes to all controllers in the network. Proofs of convergence to Pareto optimality under certain conditions are presented, and convergence under the approach is demonstrated with a simulation example. The approach is also applied to a laboratory-based water chiller system; several experiments demonstrate the features of the approach and potential for energy savings.

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

10.2172/1419622 ◽

2017 ◽

Cited By ~ 8

Author(s):

William Goetzler ◽

◽

Richard Shandross ◽

Jim Young ◽

Oxana Petritchenko ◽

...

Keyword(s):

Energy Savings ◽

Hvac Systems ◽

Commercial Building

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Artificial Intelligence-Assisted Heating Ventilation and Air Conditioning Control and the Unmet Demand for Sensors: Part 1. Problem Formulation and the Hypothesis

Sensors ◽

10.3390/s19051131 ◽

2019 ◽

Vol 19 (5) ◽

pp. 1131 ◽

Cited By ~ 7

Author(s):

Chin-Chi Cheng ◽

Dasheng Lee

Keyword(s):

Artificial Intelligence ◽

Air Conditioning ◽

Energy Savings ◽

Weather Forecasting ◽

Average Energy ◽

Weather Forecast ◽

Hvac Systems ◽

Prediction Errors ◽

Hvac Control ◽

Maximum Saving

In this study, information pertaining to the development of artificial intelligence (AI) technology for improving the performance of heating, ventilation, and air conditioning (HVAC) systems was collected. Among the 18 AI tools developed for HVAC control during the past 20 years, only three functions, including weather forecasting, optimization, and predictive controls, have become mainstream. Based on the presented data, the energy savings of HVAC systems that have AI functionality is less than those equipped with traditional energy management system (EMS) controlling techniques. This is because the existing sensors cannot meet the required demand for AI functionality. The errors of most of the existing sensors are less than 5%. However, most of the prediction errors of AI tools are larger than 7%, except for the weather forecast. The normalized Harris index (NHI) is able to evaluate the energy saving percentages and the maximum saving rations of different kinds of HVAC controls. Based on the NHI, the estimated average energy savings percentage and the maximum saving rations of AI-assisted HVAC control are 14.4% and 44.04%, respectively. Data regarding the hypothesis of AI forecasting or prediction tools having less accuracy forms Part 1 of this series of research.

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