Building Heating, Ventilation, And Air Conditioning Systems Research Articles

LSTM-based indoor air temperature prediction framework for HVAC systems in smart buildings

Accurate indoor air temperature (IAT) predictions for heating, ventilation, and air conditioning (HVAC) systems are challenging, especially for multi-zone building and for different HVAC system types. Moreover, the nonlinearity of the buildings thermal dynamics makes the IAT prediction more difficult since it is affected by complex factors such as controlled and uncontrolled points, outside weather conditions and occupancy schedule. This paper presents a long short-term memory (LSTM) model to predict IAT for multi-zone building based on direct multi-step prediction with sequence-to-sequence approach. Two strategies, LSTM-MISO and LSTM-MIMO, are built for multi-input single-output and multi-input multi-output, respectively. The performance of these two strategies has been evaluated based on two case studies on real smart buildings using variable air volume (VAV) and constant air volume (CAV) systems. For both buildings, experimental results showed that the LSTM models outperform multilayer perceptron models by reducing the prediction error by 50%.

Optimal Price Based Demand Response of HVAC Systems in Commercial Buildings Considering Peak Load Reduction

Electric utility companies (EUCs) play an intermediary role of retailers between wholesale market and end-users, maximizing their profits. Retail pricing can be well deployed with the support of EUCs to promote demand response (DR) programs for heating, ventilating, and air-conditioning (HVAC) systems in commercial buildings. This paper proposes a pricing strategy to help EUCs and building operators achieve an optimal DR of price-elastic HVAC systems, considering peak load reduction. The proposed strategy is implemented by adopting a bi-level decision model. The nonlinear thermal response of an experimental building room is modeled using piecewise linear equations, which helps convert the bi-level model to the single-level model. The pricing strategy is implemented considering a time-of-use (TOU) pricing scheme, leading to low price volatility. Case studies are conducted for two types of load curves and the results demonstrate that the proposed strategy helps EUC promote the price-based DR of the commercial buildings for conventional load curves. However, EUC cannot reduce the peak load on duck curve caused by the large introduction of photovoltaic generators, even with price-sensitive HVAC systems in commercial building. This will be addressed in future studies by inducing DR participation of HVAC systems in residential buildings.

A Fuzzy Control Strategy Using the Load Forecast for Air Conditioning System

The energy consumption of air-conditioning systems is a major part of energy consumption in buildings. Optimal control strategies have been increasingly developed in building heating, ventilation, and air-conditioning (HVAC) systems. In this paper, a load forecast fuzzy (LFF) control strategy was proposed. The predictive load based on the SVM method was used as the input parameter of the fuzzy controller to perform feedforward fuzzy control on the HVAC system. This control method was considered as an effective way to reduce energy consumption while ensuring indoor comfort, which can solve the problem of hysteresis and inaccuracy in building HVAC systems by controlling the HVAC system in advance. The case study was conducted on a ground source heat pump system in Tianjin University to validate the proposed control strategy. In addition, the advantages of the LFF control strategy were verified by comparing with two feedback control strategies, which are the supply water temperature (SWT) control strategy and the room temperature fuzzy (RTF) control strategy. Results show that the proposed LFF control strategy is capable not only to ensure the minimum indoor temperature fluctuations but also decrease the total energy consumption.

Model-based predictive control for optimal MicroCSP operation integrated with building HVAC systems

This paper presents a model predictive control (MPC) framework to minimize the energy consumption and the energy cost of the building heating, ventilation, and air-conditioning (HVAC) system integrated with a micro-scale concentrated solar power (MicroCSP) system that cogenerates electricity and heat. The mathematical model of a MicroCSP system is derived and integrated into the building thermal model of an office building at Michigan Technological University. Then, the MPC framework is used to optimize thermal energy storage (TES) system usage, the energy conversion in the Organic Rankine Cycle (ORC), and the thermal energy flows to the HVAC system. The MPC results for energy and cost savings show the significance of understanding system dynamics and designing a real-time predictive controller to maximize the benefits of MicroCSP thermal and electrical energies production. Indeed, the designed MPC framework provided 37% energy saving and 70% cost saving compared to the conventional rule-based controller (RBC). Furthermore, the MicroCSP integration into the building HVAC is compared to the alternative of integrating photovoltaic (PV) panels and battery energy storage (BES) system to address the building HVAC needs. The results show the MicroCSP system outperforms PV solar panels for energy saving, while the PV panels outperform the MicroCSP system for cost saving when dynamic pricing is applied.

Optimal Retail Pricing for Demand Response of HVAC Systems in Commercial Buildings Considering Distribution Network Voltages

Retail pricing can be well deployed with support of distribution companies (DISCOs) to promote demand response (DR) programs using heating, ventilating, and air-conditioning (HVAC) systems as thermal energy storage resources. This paper proposes a retail pricing strategy to assist a DISCO and end-users in achieving optimal price-based DR for HVAC systems while considering operational aspects of distribution network. The pricing strategy is developed using a bi-level decision model. In the upper level, the DISCO optimizes the retail price to maximize its profit. In the lower level, the end-users schedule HVAC loads to minimize their electricity bills. The nonlinear thermal response model of an experimental building room is integrated with a data-driven model of an HVAC system and incorporated into the pricing strategy using piecewise linear approximation. The proposed strategy also employs time-of-use rates as retail price caps, leading to low-level price volatility. Case studies are presented for various ranges of bus voltage deviation and penetration levels of HVAC systems. The results demonstrate that the proposed strategy is effective for determining the optimal balancing point between the DISCO’s profit and end-users’ electricity bills while enabling the DISCO to reliably regulate the network voltages via the price-sensitive HVAC units.

Energy Optimization of HVAC Systems in Commercial Buildings Considering Indoor Air Quality Management

To intelligently schedule heating, ventilation, and air conditioning (HVAC) systems for reducing energy cost of commercial buildings, indoor temperature and indoor air quality (IAQ) should be jointly considered. Otherwise, the health and productivity of occupants may be affected. In this paper, we investigate the problem of minimizing the sum of energy cost associated with HVAC systems and thermal discomfort cost related to occupants in multi-zone commercial buildings considering IAQ management. Firstly, by taking uncertainties of electricity price, outdoor temperature, number of occupants, temperature preference of each occupant, and external thermal disturbance into consideration, we formulate a time-averaged expected total cost minimization problem without violating the constraints of indoor temperature and IAQ. Due to the existence of uncertain system parameters, temporally and spatially coupled constraints, the nonconvex objective function, and nonconvex constraints, it is particularly challenging to solve the formulated problem. To this end, we propose a real-time algorithm based on the framework of Lyapunov optimization techniques. The key idea of the proposed algorithm is to construct virtual queues related to indoor temperatures and stabilize such queues so that indoor temperatures fluctuate around the ideal time-average indoor temperature. By dynamically controlling the average fluctuation level of indoor temperatures, the total cost could be optimized. Extensive simulation results show the effectiveness of the proposed algorithm.

An internet of things upgrade for smart and scalable heating, ventilation and air-conditioning control in commercial buildings

Scalability of control algorithms used for savings energy in commercial building Heating, Ventilation and Air-Conditioning (HVAC) system and their implementation on low cost resource constrained hardware is a challenging problem. This paper presents the Internet of Things (IoT) prototype which implements a smart and scalable control approach called the Smart-Token Based Scheduling Algorithm (Smart-TBSA) to minimize energy in commercial building HVAC systems. The IoT prototype is formalized with an architecture that encapsulates the different components (hardware, software, networking, and their integration) along with their interactions. A detailed description of the different components, hardware design, deployment issues, and their integration with legacy systems as well as cloud-connectivity is presented. In addition, simple modifications required for transforming the optimization models to an active control technique is also presented. While scalability is provided by the decentralized control, recursive zone thermal model identification, prediction occupant’s thermal sensation, and embedding them within the optimization models enhances the smartness. Consequently, due to the implementation of Smart-TBSA using IoT devices, an otherwise centralized control architecture of the legacy building automation system is transformed to a more scalable and smart decentralized one. The proposed Smart-TBSA and IoT prototype are illustrated on a pilot building in Nanyang Technological University, Singapore having 85 zones. Our results shows that by combining IoT with decentralized control, energy savings up to 20% can be derived. Moreover, we show that legacy building automation system can be transformed into a more smart, adaptable, scalable, and decentralized control by deploying IoT devices without incurring significant costs.

Simulation-Based Multi-Objective Optimization of institutional building renovation considering energy consumption, Life-Cycle Cost and Life-Cycle Assessment

Buildings are responsible for a significant amount of energy consumption resulting in a considerable negative environmental impact. Therefore, it is essential to decrease their energy consumption by improving the design of new buildings or renovating existing buildings. Heat losses or gains through building envelopes affect the energy use and the indoor condition. Heating, Ventilation, and Air Conditioning (HVAC) and lighting systems are responsible for 33% and 25% of the total energy consumption in office buildings, respectively. However, renovating building envelopes and energy consuming systems to lessen energy losses is usually expensive and has a long payback period. Despite the significant contribution of research on optimizing energy consumption, there is limited research focusing on the renovation of existing buildings to minimize their Life Cycle Cost (LCC) and environmental impact using Life Cycle Assessment (LCA). This paper aims to find the optimal scenario for the renovation of institutional buildings considering energy consumption and LCA while providing an efficient method to deal with the limited renovation budget. Different scenarios can be compared in a building renovation strategy to improve energy efficiency. Each scenario considers several methods including the improvement of the building envelopes, HVAC and lighting systems. However, some of these scenarios could be inconsistent and should be eliminated. Another consideration in this research is the appropriate coupling of renovation scenarios. For example, the HVAC system must be redesigned when renovating the building envelope to account for the reduced energy demand and to avoid undesirable side effects. A genetic algorithm (GA), coupled with an energy simulation tool, is used for simultaneously minimizing the energy consumption, LCC, and environmental impact of a building. A case study is developed to demonstrate the feasibility of the proposed method.

Optimal Price Based Demand Response of HVAC Systems in Multizone Office Buildings Considering Thermal Preferences of Individual Occupants Buildings

Thermal energy capacity of buildings can be coupled to power networks via heating, ventilating, and air-conditioning (HVAC) units. Optimizing the operation of HVAC systems in multizone buildings is a challenging task, as occupants have different thermal preferences dependent on time-varying indoor and outdoor environments. To estimate the social cost of demand response (DR), building aggregators need to assess occupants’ thermal discomfort (TD), which is related to their productivity outcomes. This paper proposes a price-based DR strategy for multizone office buildings to co-optimize the energy cost of HVAC units and the TD levels of occupants. To overcome simplified TD representations, a mobile interface is developed that grants occupants the ability to indicate their personal thermal preferences. These preferences are modeled using artificial neural networks, which are explicitly and directly integrated into the optimal DR scheduling. In addition, we evaluate the thermal response of a multizone office building to the operation of a variable speed heat pump (VSHP). Using the models of occupants’ TD and building thermal response, the optimization problem for the proposed DR strategy is formulated and solved with mixed-integer linear programming. The case study results verify that the proposed strategy successfully optimizes VSHP operations and occupants’ TD levels, mitigating the risk of occupant interruption to the optimal DR schedule.

Coordination and Control of Building HVAC Systems to Provide Frequency Regulation to the Electric Grid

Buildings consume 73% of electricity produced in the United States and, currently, they are largely passive participants in the electric grid. However, the flexibility in building loads can be exploited to provide ancillary services to enhance the grid reliability. In this paper, we investigate two control strategies that allow Heating, Ventilation and Air-Conditioning (HVAC) systems in commercial and residential buildings to provide frequency regulation services to the grid while maintaining occupants comfort. The first optimal control strategy is based on model predictive control acting on a variable air volume HVAC system (continuously variable HVAC load), which is available in large commercial buildings. The second strategy is rule-based control acting on an aggregate of on/off HVAC systems, which are available in residential buildings in addition to many small to medium size commercial buildings. Hardware constraints that include limiting the switching between the different states for on/off HVAC units to maintain their lifetimes are considered. Simulations illustrate that the proposed control strategies provide frequency regulation to the grid, without affecting the indoor climate significantly.

A critical review of fault modeling of HVAC systems in buildings

Buildings consumed about 40% of primary energy and 70% of the electricity in the U.S. It is well known that most buildings lose a portion of their desired and designed energy efficiency in the years after they are commissioned or recommissioned. Majority of the Heating, Ventilation, and Air-Conditioning (HVAC) systems have multiple faults residing in the systems causing either energy, thermal comfort, or indoor air quality penalties. There are hundreds of fault detection and diagnostics (FDD) algorithms available, but there is lacking a common framework to assess and validate those FDD algorithms. Fault modeling is one of the key components of such a framework. In general, fault modeling has two purposes: testing and assessment of FDD algorithms, and fault impacts analysis in terms of building energy consumption and occupants’ thermal comfort. It is expected that fault ranking from the fault impact analysis can facilitate building facility managers to make decisions. This paper provides a detailed review of current state-of-the-art for the fault modeling of HVAC systems in buildings, including fault model, fault occurrence probability, and fault simulation platform. Fault simulations considering fault occurrence probability can generate realistic faulty data across a variety of faulty operating conditions, and facilitate testing and assessment of different FDD algorithms. They can also help the fault impact study. Three research gaps are identified through this critical literature review: (1) The number of available fault models of HVAC systems is still limited. A fault model library could be developed to cover all common HVAC faults for both traditional and non-traditional HVAC systems. (2) It is imperative to include the fault occurrence probability in fault simulations for a realistic fault impacts analysis such as fault ranking. (3) Fault simulation platforms need further improvements to better facilitate the fault impact analysis.

Identifying decaying contaminant source location in building HVAC system using the adjoint probability method

Building heating, ventilation and air-conditioning (HVAC) system can be potential contaminant emission source. Released contaminants from the mechanical system are transported through the HVAC system and thus impact indoor air quality (IAQ). Effective control and improvement measures require accurate identification and prompt removal of contaminant sources from the HVAC system so as to eliminate the unfavourable influence on the IAQ. This paper studies the application of the adjoint probability method for identifying a dynamic (decaying) contaminant source in building HVAC system. A limited number of contaminant sensors are used to detect contaminant concentration variations at certain locations of the HVAC ductwork. Using the sensor inputs, the research is able to trace back and find the source location. A multi-zone airflow model, CONTAM, is employed to obtain a steady state airflow field for the studied building with detailed duct network, upon which the adjoint probability based inverse tracking method is applied. The study reveals that the adjoint probability method can effectively identify the decaying contaminant source location in building HVAC system with few properly located contaminant concentration sensors.

Hierarchical control of building HVAC system for ancillary services provision

This paper examines the provision of demand-side ancillary services using building thermodynamics and heating ventilation and air-conditioning (HVAC) systems. In particular, we consider the secondary frequency control service where the load has to declare a power capacity and a nominal power at the beginning of the regulation period and then in real-time track the received regulation signal around its declared nominal power. A hierarchical control scheme is proposed in this paper. The local building controllers at the lowest level track the temperature setpoints received from the thermal flexibility controller which maximizes the flexibility of building’s thermal consumption. At the highest level, the electrical flexibility controller controls the HVAC system while maximizing the flexibility provided to the grid. The two flexibility control layers are based on robust optimization methods. A control oriented model of a typical HVAC system is developed, and simulations are carried out to demonstrate the efficacy of the proposed approach. Results show that extra flexibility may be attained by exploiting the coefficient of performance (COP) of the chiller. Furthermore, occupant comfort increases as a consequence of providing ancillary services.

Distributed Energy Optimization for HVAC Systems in University Campus Buildings

Educational buildings consume about 2% of the total energy in a country, which leads to energy cost concerns for building operators. To reduce the building energy cost, an effective way is to intelligently schedule heating, ventilation, and air conditioning (HVAC) systems, which account for above 40% of the total energy consumption in educational buildings. In this paper, we investigate an energy optimization problem for HVAC systems in university campus buildings. To be specific, we first formulate a total cost optimization problem that minimizes the sum of energy cost related to HVAC systems and thermal discomfort cost associated with occupants considering zone occupancy pattern and thermal preference of occupants in each zone. Due to the existence of uncertain parameters as well as spatially and temporally coupled constraints, it is very challenging to solve the formulated problem. To this end, we propose a distributed model predictive control algorithm based on the alternating direction method of multipliers, which has high scalability with an increasing number of zones and can protect user privacy. Extensive simulations based on the real-world traces show that the proposed distributed algorithm could effectively reduce the total cost and offer a flexible tradeoff between energy cost and thermal discomfort.

Energy Performance and CO2 Emissions of HVAC Systems in Commercial Buildings

Energy performance of buildings has attracted much attention among building physicists and engineers worldwide. The effects of building heating; ventilation; and air conditioning (HVAC) systems’ design upgrade on the building energy performance are the focus of the current study. The adopted HVAC system consisted of chilled ceiling and chilled beam systems served by a centrifugal water chiller. An energy simulation study was undertaken in accordance with the national Australian built environment rating system-rules for collecting and using data. A three-dimensional simulation study was carried out utilizing the virtual environment-integrated environmental solutions software. Results from the current study have shown the importance of utilizing energy-efficient HVAC systems and HVAC strategies for achieving a high building energy star rating. Recommended strategies in order to achieve the nominated star rating; as predicted by the simulation analysis; were presented. Moreover; the effects of solar radiation inside the building atrium were significant; which cannot be overcome by simply installing a low shading coefficient glazing type at the atrium skylight. In addition to providing chilled ceiling technology; a high efficiency chiller and low energy lighting; it is recommended that the building be well tuned during the commissioning period. The current approach could be extended to accommodate higher energy ratings of commercial buildings at different locations worldwide.

A Passivity-Based Power-Shaping Control of Building HVAC Systems

Regulating indoor air environment is one of the core functions of building energy management system. Heating, ventilation, and air-conditioning (HVAC) control systems play an important role in adjusting the room temperature to provide occupants a desired level of comfort. Occupant comfort has a direct effect on the energy consumption and providing an optimal balance between comfort and energy consumption is a challenging problem. This paper presents a framework for control of building HVAC systems using a methodology based on power-shaping paradigm that exploits the passivity property of a system. The system dynamics are expressed in the Brayton–Moser (BM) form which exhibits a gradient structure with the mixed-potential function, which has the units of power. The power-shaping technique is used to synthesize the controller by assigning a desired power function to the closed-loop dynamics so as to make the equilibrium point asymptotically stable. The proposed methodology is demonstrated on HVAC subsystems: RC network building zone model and a heat exchanger system.

A Comparative Study of Energy Consumption for Residential HVAC Systems Using EnergyPlus

Energy conservation and sustainability have become an attractive field for research due to the growth in population and continuing search for better living standards. Heating, Ventilation, and Air Conditioning (HVAC) systems account for almost half of consumed energy in buildings and around 10 to 20% of total energy consumption in developed countries. In general, the trend of installing central HVAC systems increases in residential and commercial buildings. In this research, a study of energy consumption of HVAC systems in residential buildings has been conducted with the aim to compare those systems from an energy consumption point of view. The final goal of this research is to reduce energy requirements of residential buildings sector to save energy and reduce carbon emission. A medium size residential building in the city of Tripoli, Libya, was selected as a case study. EnergyPlus building simulation software along with OpenStudio software were used to model the house and HVAC systems. The results show that the virtual component “ideal air loads” used in EnergyPlus is very easy to use, however, its calculated energy consumption is overestimated compared to other models. Therefore, using that component can be misleading and may result in high monthly and annually energy consumption results. The results also show that in a residential building, unitary systems consume the least annual energy consumption compared to other models. It was concluded that variations in energy consumption of the considered HVAC systems decrease as the coefficient of performance (COP) increases and visa verse.

Effectiveness of using WiFi technologies to detect and predict building occupancy

This paper presents findings of a case-study demonstrating the effectiveness of using WiFi networks to detect occupancy as opposed to CO2 sensors, commonly used for demand-controlled heating, ventilation and air conditioning (HVAC) systems. The study took place in one building at the University of Manitoba Fort Garry campus in Canada. In a classroom, the number of WiFi connections was collected on an hourly basis over one-week, simultaneously with CO2 concentration levels at 10-min intervals. The number of occupants in this classroom was also counted on an hourly basis over the same study period. Data analysis showed that WiFi counts predicted actual occupancy levels more accurately than CO2 concentration levels, thus validating the use of this technology to track occupancy. This study was the first to use both CO2 concentration and WiFi counts simultaneously as indicators for occupancy. Results demonstrated the possibility of using WiFi counts in large buildings for controlling HVAC systems at a higher accuracy and lower cost than other sensor technologies.Implications and influences: Given the large contribution of HVAC systems to overall buildings' energy consumption, this study presents a new method for efficiently operating HVAC systems. Results highlighted the accuracy of using WiFi connections as predictors for occupancy patterns to be used for controlling HVAC systems instead of CO2 sensors. These findings provide a foundation for further research on using WiFi networks to manage and operate HVAC systems in new buildings. Efficient operation of these systems based on real-time occupancy as opposed to static schedules provides facility managers with an opportunity for significant energy savings at a relatively low cost.

Transactive Control of Commercial Buildings for Demand Response

Transactive control is a type of distributed control strategy that uses market mechanisms to engage self-interested responsive loads to achieve power balance in the electrical power grid. In this paper, we propose a transactive control approach of commercial building heating, ventilation, and air-conditioning (HVAC) systems for demand response. We first describe the system models, and identify their model parameters using data collected from systems engineering building (SEB) located on our Pacific Northwest National Laboratory campus. We next present a transactive control market structure for commercial building HVAC systems, and describe its agent bidding and market clearing strategies. Several case studies are performed in a simulation environment using building controls virtual test bed (BCVTB) and calibrated SEB EnergyPlus model. We show that the proposed transactive control approach is very effective at peak shaving, load shifting, and strategic conservation for commercial building HVAC systems.

Hybrid model predictive control of a residential HVAC system with on-site thermal energy generation and storage

This paper describes the development, implementation and experimental investigation of a Hybrid Model Predictive Control (HMPC) strategy to control solar-assisted heating, ventilation and air-conditioning (HVAC) systems with on-site thermal energy generation and storage. A comprehensive approach to the thermal energy management of a residential building is presented to optimise the scheduling of the available thermal energy resources to meet a comfort objective. The system has a hybrid nature with both continuous variables and discrete, logic-driven operating modes. The proposed control strategy is organized in two hierarchical levels. At the high-level, an HMPC controller with a 24-h prediction horizon and a 1-h control step is used to select the operating mode of the HVAC system. At the low-level, each operating mode is optimised using a 1-h rolling prediction horizon with a 5-min control step. The proposed control strategy has been practically implemented on the Building Management and Control System (BMCS) of a Net Zero-Energy Solar Decathlon house. This house features a sophisticated HVAC system comprising of an air-based photovoltaic thermal (PVT) collector and a phase change material (PCM) thermal storage integrated with the air-handling unit (AHU) of a ducted reverse-cycle heat pump system. The simulation and experimental results demonstrated the high performance achievable using an HMPC approach to optimising complex multimode HVAC systems in residential buildings, illustrating efficient selection of the appropriate operating modes to optimally manage thermal energy of the house.