Simulation-assisted control in building energy management systems

Technological advances in real-time data collection, data transfer and ever-increasing computational power are bringing simulation-assisted control and on-line fault detection and diagnosis (FDD) closer to reality than was imagined when building energy management systems (BEMSs) were introduced in the 1970s. This paper describes the development and testing of a prototype simulation-assisted controller, in which a detailed simulation program is embedded in real-time control decision making. Results from an experiment in a full-scale environmental test facility demonstrate the feasibility of predictive control using a physically-based thermal simulation program

The State of the Art in Model Predictive Control Application for Demand Response

Demand response programs have been used to optimize the participation of the demand side. Utilizing the demand response programs maximizes social welfare and reduces energy usage. Model Predictive Control is a suitable control strategy that manages the energy network, and it shows superiority over other predictive controllers. The goal of implementing this controller on the demand side is to minimize energy consumption, carbon footprint, and energy cost and maximize thermal comfort and social welfare.  This review paper aims to highlight this control strategy\u27s excellence in handling the demand response optimization problem. The optimization methods of the controller are compared. Summarization of techniques used in recent publications to solve the Model Predictive Control optimization problem is presented, including demand response programs, renewable energy resources, and thermal comfort. This paper sheds light on the current research challenges and future research directions for applying model-based control techniques to the demand response optimization problem

Advanced Occupancy Measurement Using Sensor Fusion

With roughly about half of the energy used in buildings attributed to Heating, Ventilation, and Air conditioning (HVAC) systems, there is clearly great potential for energy saving through improved building operations. Accurate knowledge of localised and real-time occupancy numbers can have compelling control applications for HVAC systems. However, existing technologies applied for building occupancy measurements are limited, such that a precise and reliable occupant count is difficult to obtain. For example, passive infrared (PIR) sensors commonly used for occupancy sensing in lighting control applications cannot differentiate between occupants grouped together, video sensing is often limited by privacy concerns, atmospheric gas sensors (such as CO2 sensors) may be affected by the presence of electromagnetic (EMI) interference, and may not show clear links between occupancy and sensor values. Past studies have indicated the need for a heterogeneous multi-sensory fusion approach for occupancy detection to address the short-comings of existing occupancy detection systems. The aim of this research is to develop an advanced instrumentation strategy to monitor occupancy levels in non-domestic buildings, whilst facilitating the lowering of energy use and also maintaining an acceptable indoor climate. Accordingly, a novel multi-sensor based approach for occupancy detection in open-plan office spaces is proposed. The approach combined information from various low-cost and non-intrusive indoor environmental sensors, with the aim to merge advantages of various sensors, whilst minimising their weaknesses. The proposed approach offered the potential for explicit information indicating occupancy levels to be captured. The proposed occupancy monitoring strategy has two main components; hardware system implementation and data processing. The hardware system implementation included a custom made sound sensor and refinement of CO2 sensors for EMI mitigation. Two test beds were designed and implemented for supporting the research studies, including proof-of-concept, and experimental studies. Data processing was carried out in several stages with the ultimate goal being to detect occupancy levels. Firstly, interested features were extracted from all sensory data collected, and then a symmetrical uncertainty analysis was applied to determine the predictive strength of individual sensor features. Thirdly, a candidate features subset was determined using a genetic based search. Finally, a back-propagation neural network model was adopted to fuse candidate multi-sensory features for estimation of occupancy levels. Several test cases were implemented to demonstrate and evaluate the effectiveness and feasibility of the proposed occupancy detection approach. Results have shown the potential of the proposed heterogeneous multi-sensor fusion based approach as an advanced strategy for the development of reliable occupancy detection systems in open-plan office buildings, which can be capable of facilitating improved control of building services. In summary, the proposed approach has the potential to: (1) Detect occupancy levels with an accuracy reaching 84.59% during occupied instances (2) capable of maintaining average occupancy detection accuracy of 61.01%, in the event of sensor failure or drop-off (such as CO2 sensors drop-off), (3) capable of utilising just sound and motion sensors for occupancy levels monitoring in a naturally ventilated space, (4) capable of facilitating potential daily energy savings reaching 53%, if implemented for occupancy-driven ventilation control

Online HVAC Temperature and Air Quality Control for Cost-efficient Commercial Buildings Based on Lyapunov Optimization Technique

Commercial buildings consume up to 35.5% of total electricity consumed in the United States. As a subsystem in the smart building management system, Heating, Ventilation, and Air Conditioning (HVAC) systems are responsible for 45% of electricity consumption in commercial buildings. Therefore, energy management of HVAC systems is of interest. The HVAC system brings thermal and air quality comfort to the occupants of the building, designing a controller that maximizes this comfort is the first objective. Inevitably, ideal comfort tracking means more energy consumption and energy cost. Hence, the more advanced objective is balancing the comfort-cost tradeoff. Since HVAC systems have nonlinear, complex and MIMO characteristics, modeling the system and formulating an optimization problem for them is challenging. Moreover, there are physical and comfort constraints to be satisfied, and randomness of parameters such as thermal disturbances, number of occupants in the building that affects the air quality, thermal and air quality setpoints we want to track, electricity price and outside temperature to be considered. Adding real time analysis to this problem furthers the challenge. In this thesis, utilizing Lyapunov optimization technique, we first transform the constraints to stability equations, and formulate a stochastic optimization problem, then we minimize the time average of the expected cost of the system while the cost is a weighted sum of the discomfort and energy cost. Results show that using the proposed algorithm and real data, the algorithm is feasible, and an optimal solution for the problem is achieved

Comparative Analysis of Thermal Unit Control Methods for Sustainable Housing Applications

This study aims to develop different control strategies for application to nonlinear model of a thermal unit and compare their performances as an advanced thermal control methods for HVAC applications of sustainable buildings. The mathematical description of thermal unit was obtained exploiting a data-driven and physically meaningful nonlinear continuous-time model, which represents a test-bed used in passive air conditioning for sustainable housing applications. The presented controller strategies use both inside temperature and air flow control in the thermal unit. The proposed control schemes were assessed with extensive simulations and Monte-Carlo analysis in the presence of modelling and measurement errors. The contribution of this work consists of providing an application example of the design and testing through simulations, of a data-driven thermal unit control. Furthermore, this study provides an insight into different control strategies in air conditioning systems and helps the practitioners and HVAC learners to design proper controller solutions

Development of robust building energy demand-side control strategy under uncertainty

The potential of carbon emission regulations applied to an individual building will encourage building owners to purchase utility-provided green power or to employ onsite renewable energy generation. As both cases are based on intermittent renewable energy sources, demand side control is a fundamental precondition for maximizing the effectiveness of using renewable energy sources. Such control leads to a reduction in peak demand and/or in energy demand variability, therefore, such reduction in the demand profile eventually enhances the efficiency of an erratic supply of renewable energy. The combined operation of active thermal energy storage and passive building thermal mass has shown substantial improvement in demand-side control performance when compared to current state-of-the-art demand-side control measures. Specifically, "model-based" optimal control for this operation has the potential to significantly increase performance and bring economic advantages. However, due to the uncertainty in certain operating conditions in the field its control effectiveness could be diminished and/or seriously damaged, which results in poor performance. This dissertation pursues improvements of current demand-side controls under uncertainty by proposing a robust supervisory demand-side control strategy that is designed to be immune from uncertainty and perform consistently under uncertain conditions. Uniqueness and superiority of the proposed robust demand-side controls are found as below: a. It is developed based on fundamental studies about uncertainty and a systematic approach to uncertainty analysis. b. It reduces variability of performance under varied conditions, and thus avoids the worst case scenario. c. It is reactive in cases of critical "discrepancies" observed caused by the unpredictable uncertainty that typically scenario uncertainty imposes, and thus it increases control efficiency. This is obtainable by means of i) multi-source composition of weather forecasts including both historical archive and online sources and ii) adaptive Multiple model-based controls (MMC) to mitigate detrimental impacts of varying scenario uncertainties. The proposed robust demand-side control strategy verifies its outstanding demand-side control performance in varied and non-indigenous conditions compared to the existing control strategies including deterministic optimal controls. This result reemphasizes importance of the demand-side control for a building in the global carbon economy. It also demonstrates a capability of risk management of the proposed robust demand-side controls in highly uncertain situations, which eventually attains the maximum benefit in both theoretical and practical perspectives.Ph.D.Committee Chair: Augenbroe, Gofried; Committee Member: Brown, Jason; Committee Member: Jeter, Sheldon; Committee Member: Paredis,Christiaan; Committee Member: Sastry, Chellur

Autonomic management of a building's multi-HVAC system start-up

Most studies about the control, automation, optimization and supervision of building HVAC systems concentrate on the steady-state regime, i.e., when the equipment is already working at its setpoints. The originality of the current work consists of proposing the optimization of building multi-HVAC systems from start-up until they reach the setpoint, making the transition to steady state-based strategies smooth. The proposed approach works on the transient regime of multi-HVAC systems optimizing contradictory objectives, such as the desired comfort and energy costs, based on the "Autonomic Cycle of Data Analysis Tasks" concept. In this case, the autonomic cycle is composed of two data analysis tasks: one for determining if the system is going towards the defined operational setpoint, and if that is not the case, another task for reconfiguring the operational mode of the multi-HVAC system to redirect it. The first task uses machine learning techniques to build detection and prediction models, and the second task defines a reconfiguration model using multiobjective evolutionary algorithms. This proposal is proven in a real case study that characterizes a particular multi-HVAC system and its operational setpoints. The performance obtained from the experiments in diverse situations is impressive since there is a high level of conformity for the multi-HVAC system to reach the setpoint and deliver the operation to the steady-state smoothly, avoiding overshooting and other non-desirable transitional effects.European CommissionJunta de Comunidades de Castilla-La ManchaMinisterio de Ciencia e Innovació

Occupancy driven supervisory control of indoor environment systems to minimise energy consumption of airport terminal building

A very economical way of reducing the operational energy consumed by large commercial buildings such as an airport terminal is the automatic control of its active energy systems. Such control can adjust the indoor environment systems setpoints to satisfy comfort during occupancy or when unoccupied, initiate energy conservation setpoints and if necessary, shut down part of the building systems. Adjusting energy control setpoints manually in large commercial buildings can be a nightmare for facility managers. Incidentally for such buildings, occupancy based control strategies are not achieved through the use of conventional controllers alone. This research, therefore, investigated the potential of using a high-level control system in airport terminal building. The study presents the evolution of a novel fuzzy rule-based supervisory controller, which intelligently establishes comfort setpoints based on flow of passenger through the airport as well as variable external environmental conditions. The inputs to the supervisory controller include: the time schedule of the arriving and departing passenger planes; the expected number of passengers; zone daylight illuminance levels; and external temperature. The outputs from the supervisory controller are the low-level controllers internal setpoint profile for thermal comfort, visual comfort and indoor air quality. Specifically, this thesis makes contribution to knowledge in the following ways: It utilised artificial intelligence to develop a novel fuzzy rule-based, energy-saving supervisory controller that is able to establish acceptable indoor environmental quality for airport terminals based on occupancy schedules and ambient conditions. It presents a unique methodology of designing a supervisory controller using expert knowledge of an airport s indoor environment systems through MATLAB/Simulink platform with the controller s performance evaluated in both MATLAB and EnergyPlus simulation engine. Using energy conservation strategies (setbacks and switch-offs), the pro-posed supervisory control system was shown to be capable of reducing the energy consumed in the Manchester Airport terminal building by up to 40-50% in winter and by 21-27% in summer. It demonstrates that if a 45 minutes passenger processing time is aimed for instead of the 60 minutes standard time suggested by ICAO, energy consumption is significantly reduced (with less carbon emission) in winter particularly. The potential of the fuzzy rule-based supervisory controller to optimise comfort with minimal energy based on variation in occupancy and external conditions was demonstrated through this research. The systematic approach adopted, including the use of artificial intelligence to design supervisory controllers, can be extended to other large buildings which have variable but predictable occupancy patterns