Advanced control strategies for optimal operation of a combined solar and heat pump system

The UK domestic sector accounts for more than a quarter of total energy use. This energy use can be reduced through more efficient building operations. The energy efficiency can be improved through better control of heating in houses, which account for a large portion of total energy consumption. The energy consumption can be lowered by using renewable energy systems, which will also help the UK government to meet its targets towards reduction in carbon emissions and generation of clean energy. Building control has gained considerable interest from researchers and much improved ways of control strategies for heating and hot water systems have been investigated. This intensified research is because heating systems represent a significant share of our primary energy consumption to meet thermal comfort and indoor air quality criteria. Advances in computing control and research in advanced control theory have made it possible to implement advanced controllers in building control applications. Heating control system is a difficult problem because of the non-linearities in the system and the wide range of operating conditions under which the system must function. A model of a two zone building was developed in this research to assess the performance of different control strategies. Two conventional (On-Off and proportional integral controllers) and one advanced control strategies (model predictive controller) were applied to a solar heating system combined with a heat pump. The building was modelled by using a lumped approach and different methods were deployed to obtain a suitable model for an air source heat pump. The control objectives were to reduce electricity costs by optimizing the operation of the heat pump, integrating the available solar energy, shifting electricity consumption to the cheaper night-time tariff and providing better thermal comfort to the occupants. Different climatic conditions were simulated to test the mentioned controllers. Both on-off and PI controllers were able to maintain the tank and room temperatures to the desired set-point temperatures however they did not make use of night-time electricity. PI controller and Model Predictive Controller (MPC) based on thermal comfort are developed in this thesis. Predicted mean vote (PMV) was used for controlling purposes and it was modelled by using room air and radiant temperatures as the varying parameters while assuming other parameters as constants. The MPC dealt well with the disturbances and occupancy patterns. Heat energy was also stored into the fabric by using lower night-time electricity tariffs. This research also investigated the issue of model mismatch and its effect on the prediction results of MPC. MPC performed well when there was no mismatch in the MPC model and simulation model but it struggled when there was a mismatch. A genetic algorithm (GA) known as a non-dominated sorting genetic algorithm (NSGA II) was used to solve two different objective functions, and the mixed objective from the application domain led to slightly superior results. Overall results showed that the MPC performed best by providing better thermal comfort, consuming less electric energy and making better use of cheap night-time electricity by load shifting and storing heat energy in the heating tank. The energy cost was reduced after using the model predictive controller

Machine Learning for Smart and Energy-Efficient Buildings

Energy consumption in buildings, both residential and commercial, accounts for approximately 40% of all energy usage in the U.S., and similar numbers are being reported from countries around the world. This significant amount of energy is used to maintain a comfortable, secure, and productive environment for the occupants. So, it is crucial that the energy consumption in buildings must be optimized, all the while maintaining satisfactory levels of occupant comfort, health, and safety. Recently, Machine Learning has been proven to be an invaluable tool in deriving important insights from data and optimizing various systems. In this work, we review the ways in which machine learning has been leveraged to make buildings smart and energy-efficient. For the convenience of readers, we provide a brief introduction of several machine learning paradigms and the components and functioning of each smart building system we cover. Finally, we discuss challenges faced while implementing machine learning algorithms in smart buildings and provide future avenues for research at the intersection of smart buildings and machine learning

Occupant-oriented demand response with multi-zone thermal building control

In future energy systems with high shares of renewable energy sources, the electricity demand of buildings has to react to the fluctuating electricity generation in view of stability. As buildings consume one-third of global energy and almost half of this energy accounts for Heating, Ventilation, and Air Conditioning (HVAC) systems, HVAC are suitable for shifting their electricity consumption in time. To this end, intelligent control strategies are necessary as the conventional control of HVAC is not optimized for the actual demand of occupants and the current situation in the electricity grid. In this paper, we present the novel multi-zone controller Price Storage Control (PSC) that not only considers room-individual Occupants’ Thermal Satisfaction (OTS), but also the available energy storage, and energy prices. The main feature of PSC is that it does not need a building model or forecasts of future demands to derive the control actions for multiple rooms in a building. For comparison, we use an ideal, error-free Model Predictive Control (MPC), a heuristic control approach from the literature (PC), and a conventional hysteresis-based two-point control as upper and lower benchmarks. We evaluate the four controllers in a multi-zone environment for heating a building in winter and consider two different scenarios that differ in how much the permitted temperatures vary. In addition, we compare the impact of model parameters with high and low thermal capacitance. The results show that PSC strongly outperforms the conventional control approach and PC in both scenarios and for both parameters concerning the electricity costs and OTS. For high capacitance, it leads to 22 % costs reduction while the ideal MPC achieves cost reductions of more than 39 %. Considering that PSC does not need any building model or forecast, as opposed to MPC, the results support the suitability of our developed control strategy for controlling HVAC systems in future energy systems

Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities

In the last few years, the application of Model Predictive Control (MPC) for energy management in buildings has received significant attention from the research community. MPC is becoming more and more viable because of the increase in computational power of building automation systems and the availability of a significant amount of monitored building data. MPC has found successful implementation in building thermal regulation, fully exploiting the potential of building thermal mass. Moreover, MPC has been positively applied to active energy storage systems, as well as to the optimal management of on-site renewable energy sources. MPC also opens up several opportunities for enhancing energy efficiency in the operation of Heating Ventilation and Air Conditioning (HVAC) systems because of its ability to consider constraints, prediction of disturbances and multiple conflicting objectives, such as indoor thermal comfort and building energy demand. Despite the application of MPC algorithms in building control has been thoroughly investigated in various works, a unified framework that fully describes and formulates the implementation is still lacking. Firstly, this work introduces a common dictionary and taxonomy that gives a common ground to all the engineering disciplines involved in building design and control. Secondly the main scope of this paper is to define the MPC formulation framework and critically discuss the outcomes of different existing MPC algorithms for building and HVAC system management. The potential benefits of the application of MPC in improving energy efficiency in buildings were highlighted

Numerical Methods for Nonlinear Optimal Control Problems and Their Applications in Indoor Climate Control

Efficiency, comfort, and convenience are three major aspects in the design of control systems for residential Heating, Ventilation, and Air Conditioning (HVAC) units. In this dissertation, we study optimization-based algorithms for HVAC control that minimizes energy consumption while maintaining a desired temperature, or even human comfort in a room. Our algorithm uses a Computer Fluid Dynamics (CFD) model, mathematically formulated using Partial Differential Equations (PDEs), to describe the interactions between temperature, pressure, and air flow. Our model allows us to naturally formulate problems such as controlling the temperature of a small region of interest within a room, or to control the speed of the air flow at the vents, which are hard to describe using finite-dimensional Ordinary Partial Differential (ODE) models. Our results show that our HVAC control algorithms produce significant energy savings without a decrease in comfort. Also, we formulate a gradient-based estimation algorithm capable of reconstructing the states of doors in a building, as well as its temperature distribution, based on a floor plan and a set of thermostats. The estimation algorithm solves in real time a convection-diffusion CFD model for the air flow in the building as a function of its geometric configuration. We formulate the estimation algorithm as an optimization problem, and we solve it by computing the adjoint equations of our CFD model, which we then use to obtain the gradients of the cost function with respect to the flow’s temperature and door states. We evaluate the performance of our method using simulations of a real apartment in the St. Louis area. Our results show that the estimation method is both efficient and accurate, establishing its potential for the design of smarter control schemes in the operation of high-performance buildings. The optimization problems we generate for HVAC system\u27s control and estimation are large-scale optimal control problem. While some optimal control problems can be efficiently solved using algebraic or convex methods, most general forms of optimal control must be solved using memory-expensive numerical methods. In this dissertation we present theoretical formulations and corresponding numerical algorithms that can find optimal inputs for general dynamical systems by using direct methods. The results show these algorithms\u27 performance and potentials to be applied to solve large-scale nonlinear optimal control problem in real time

Review of control strategies for improving the energy flexibility provided by heat pump systems in buildings

Peer ReviewedPostprint (author's final draft

Optimization of building performance via model-based predictive control

Il controllo predittivo basato su modello (MPC) è una tecnica di controllo avanzata che ha svolto un ruolo importante nella gestione di molti processi nel settore industriale. Oggi, nell’ottica di una gestione energetica efficiente degli edifici, l’utilizzo di questa strategia si sta dimostrando una soluzione promettente per ridurre al minimo i consumi e i costi energetici complessivi. Tuttavia, gli studi sulla sua fattibilità tecnica in edifici esistenti sono ancora in una fase iniziale. Pertanto, il risultato principale di questa tesi è la progettazione e lo sviluppo di un prototipo hardware e software per la verifica sul campo di un sistema di controllo predittivo, basato su modello, integrando un modello predittivo virtuale della porzione dell'edificio in esame, il controllore e l'interfaccia grafica per i dispositivi di monitoraggio e regolazione utilizzati. Inoltre, particolare attenzione è stata posta sulla fattibilità tecnica relativa all'implementazione di un tipico sistema MPC, che include un sottosistema di monitoraggio, un set di acquisizione dati e un metodo di identificazione del sistema per ottenere il modello per il controllore, mediante un approccio di modellazione grey-box. La fase di modellazione e l'approccio empirico sviluppato sono presentati nella prima parte di questa tesi di ricerca, mentre la parte centrale riguarda: lo sviluppo del prototipo di controllo predittivo, basato su modello, all'interno di uno strumento virtuale del software LabVIEW e la descrizione del test sperimentale, effettuato durante la stagione di riscaldamento, garantendo la normale operatività dell’edificio durante l'intero periodo di monitoraggio. Infine, è presentato lo studio sviluppato in ambiente di simulazione per indagare il potenziale della logica di controllo per la valutazione di scenari di riqualificazione. Il focus è sulla definizione dei principali componenti del simulatore MPC e sui risultati ottenuti testando uno degli scenari di intervento.Model Predictive Control (MPC) is an advanced control technique which has played an important role in the management of many processes in the industry sector. Nowadays, in the perspective of an efficient building energy management, the exploitation of this strategy is proving to be a promising solution for minimising overall energy consumptions and costs. However, investigations on the feasibility of the technique in real existing buildings are at an initial stage. Hence, the main outcome of this dissertation is the design and development of a prototype hardware and software set up for on-field testing of a model-based predictive control system, integrating a virtual predictive model of the portion of the building under investigation, the controller and the interface to the monitoring and regulation devices used. Moreover, this research is addressed to investigate the technical feasibility of the development and deployment of a typical MPC system, which includes a monitoring sub-system, a data acquisition set up and a system identification method to obtain the model for the controller by means of a grey-box modelling approach. The modelling phase and the empirical approach developed are presented in the first part of this research thesis, while the core part concerns: the development of the MPC prototype, within a virtual instrument of LabVIEW software and the description of the experimental test, which was carried out during heating season, ensuring normal building operation during the entire monitoring period. Finally, this dissertation presents the study developed in simulation environment to investigate the potential of the control logic for the evaluation of retrofitting scenarios. The focus is on the definition of the main MPC simulator components and on the results obtained by testing one of the intervention scenarios

Occupant-Oriented Demand Response with Room-Individual Building Control

In future energy systems with high shares of renewable energy sources, the electricity demand of buildings has to react to the fluctuating electricity generation in view of stability. As buildings consume one-third of global energy and almost half of this energy accounts for Heating, Ventilation, and Air Conditioning (HVAC) systems, HVAC are suitable for shifting their electricity consumption in time. To this end, intelligent control strategies are necessary as the conventional control of HVAC is not optimized for the actual demand of occupants and the current situation in the electricity grid. In this paper, we present the novel multi-zone controller Price Storage Control (PSC) that not only considers room-individual Occupants' Thermal Satisfaction (OTS), but also the available energy storage, and energy prices. The main feature of PSC is that it does not need a building model or forecasts of future demands to derive the control actions for multiple rooms in a building. For comparison, we use an ideal, error-free Model Predictive Control (MPC), a simplified variant without storage consideration (PC), and a conventional hysteresis-based two-point control. We evaluate the four controllers in a multi-zone environment for heating a building in winter and consider two different scenarios that differ in how much the permitted temperatures vary. In addition, we compare the impact of model parameters with high and low thermal capacitance. The results show that PSC strongly outperforms the conventional control approach in both scenarios and for both parameters. For high capacitance, it leads to 22 % costs reduction while the ideal MPC achieves cost reductions of more than 39 %. Considering that PSC does not need any building model or forecast, as opposed to MPC, the results support the suitability of our developed control strategy for controlling HVAC systems in future energy systems.Comment: Paper revisio