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

An investigation into the energy and control implications of adaptive comfort in a modern office building

PhD ThesisAn investigation into the potentials of adaptive comfort in an office building is carried out using fine grained primary data and computer modelling. A comprehensive literature review and background study into energy and comfort aspects of building management provides the backdrop against which a target building is subjected to energy and comfort audit, virtual simulation and impact assessment of adaptive comfort standard (BS EN 15251: 2007). Building fabric design is also brought into focus by examining 2006 and 2010 Approved Document part L potentials against Passive House design. This is to reflect the general direction of regulatory development which tends toward zero carbon design by the end of this decade. In finishing a study of modern controls in buildings is carried out to assess the strongest contenders that next generation heating, ventilation and air-conditioning technologies will come to rely on in future buildings. An actual target building constitutes the vehicle for the work described above. A virtual model of this building was calibrated against an extensive set of actual data using version control method. The results were improved to surpass ASHRAE Guide 14. A set of different scenarios were constructed to account for improved fabric design as well as historical weather files and future weather predictions. These scenarios enabled a comparative study to investigate the effect of BS EN 15251:2007 when compared to conventional space controls. The main finding is that modern commercial buildings built to the latest UK statutory regulations can achieve considerable carbon savings through adaptive comfort standard. However these savings are only modestly improved if fabric design is enhanced to passive house levels. Adaptive comfort can also be readily deployed using current web-enabled control applications. However an actual field study is necessary to provide invaluable insight into occupants’ acceptance of this standard since winter-time space temperature results derived from BS EN 15251:2007 constitute a notable departure from CIBSE environmental guidelines

Learning-based Predictive Control Approach for Real-time Management of Cyber-physical Systems

Cyber-physical systems (CPSs) are composed of heterogeneous, and networked hardware and software components tightly integrated with physical elements [72]. Large-scale CPSs are composed of complex components, subject to uncertainties [89], as though their design and development is a challenging task. Achieving reliability and real-time adaptation to changing environments are some of the challenges involved in large-scale CPSs development [51]. Addressing these challenges requires deep insights into control theory and machine learning. This research presents a learning-based control approach for CPSs management, considering their requirements, specifications, and constraints. Model-based control approaches, such as model predictive control (MPC), are proven to be efficient in the management of CPSs [26]. MPC is a control technique that uses a prediction model to estimate future dynamics of the system and generate an optimal control sequence over a prediction horizon. The main benefit of MPC in CPSs management comes from its ability to take the predictions of system’s environmental conditions and disturbances into account [26]. In this dissertation, centralized and distributed MPC strategies are designed for the management of CPSs. They are implemented for the thermal management of a CPS case study, smart building. The control goals are optimizing system efficiency (lower thermal power consumption in the building), and improving users’ convenience (maintaining desired indoor thermal conditions in the building). Model-based control strategies are advantageous in the management of CPSs due to their ability to provide system robustness and stability. The performance of a model-based controller strongly depends on the accuracy of the model as a representation of the system dynamics [26]. Accurate modeling of large-scale CPSs is difficult (due to the existence of unmodeled dynamics and uncertainties in the modeling process); therefore, modelbased control approach is not practical for these systems [6]. By incorporating machine learning with model-based control strategies, we can address CPS modeling challenges while preserving the advantages of model-based control methods. In this dissertation, a learning-based modeling strategy incorporated with a model-based control approach is proposed to manage energy usage and maintain thermal, visual, and olfactory performance in buildings. Neural networks (NNs) are used to learn the building’s performance criteria, occupant-related parameters, environmental conditions, and operation costs. Control inputs are generated through the model-based predictive controller and based on the learned parameters, to achieve the desired performance. In contrast to the existing building control systems presented in the literature, the proposed management system integrates current and future information of occupants (convenience, comfort, activities), building energy trends, and environment conditions (environmental temperature, humidity, and light) into the control design. This data is synthesized and evaluated in each instance of decision-making process for managing building subsystems. Thus, the controller can learn complex dynamics and adapt to the changing environment, to achieve optimal performance while satisfying problem constraints. Furthermore, while many prior studies in the filed are focused on optimizing a single aspect of buildings (such as thermal management), and little attention is given to the simultaneous management of all building objectives, our proposed management system is developed considering all buildings’ physical models, environmental conditions, comfort specifications, and occupants’ preferences, and can be applied to various building management applications. The proposed control strategy is implemented to manage indoor conditions and energy consumption in a building, simulated in EnergyPlus software. In addition, for comparison purposes, we designed and simulated a baseline controller for the building under the same conditions

Efterfrågeflexibilitet inom uppvärmning och ventilation i pedagogiska kontorsbyggnader

Demand response on the building level aids stabilization of the consumption profile in the district heating and electricity grid. A stable consumption reduces peak demand and need for high cost peak power plants like heat-only boilers and gas turbines. The main benefit achieved is less CO2 emissions at the same time the producer and consumer benefit economically through cheaper production costs. The main objective with this study was to simulate a detailed model of an educational office building floor to determine the monetary saving potential of demand response combined with dynamic hourly district heating and electricity prices. In addition, the difference in potential between centralized and decentralized control approaches was to be examined. The impact on indoor environmental comfort and heating flexibility of the building were areas of interest. The heating flexibility is a measure of the buildings ability to adapt the heating according to the dynamic price signals sent by the energy producer. Both CAV and VAV ventilation designs were included in the study. In addition, the monetary savings potential of contract-power limitation within district heating and its impact on thermal comfort were studied. The impact of demand response was studied by control of space heating, adjustment of supply air temperature and airflow regulation. The research was conducted by dynamic energy simulations of an educational office building on the Aalto University campus area. The simulation software used was IDA Indoor Climate and Energy (version 4.7.1). Acceptable ranges of indoor environmental comfort parameters (temperature, PMV, CO2) were chosen and rule based control algorithms were developed and implemented into the simulation program. A centralized control approach of space heating did not show any significant potential in heat cost savings (1.5%) and the heating flexibility remained low (2.9%). The decentralized approach reached heat cost savings of 5% – 6% and heating flexibility of up to 15% when controlling both space heating and supply air temperature in CAV ventilation cases. All demand response control alternatives managed to maintain a good thermal comfort for over 90% of the occupied time. Occupancy did not affect neither cost savings, heating flexibility nor thermal comfort in any of the different simulation set-ups. The contract power of the building could be cut by 35% without affecting the thermal comfort at all. This brought an annual cost saving of 6.1 €/m2 – 26.9 €/m2 (27.1% – 35%) depending on district heat provider. A peak demand cut by 43% had only minor impact on the thermal comfort and provided even greater annual cost savings. The main conclusions from the study are that demand response within heating is only beneficial with a decentralized control and that peak demand limiting within district heating have a big cost saving potential.Efterfrågeflexibilitet inom fastigheter bistår till att stabilisera konsumtionsprofilen i fjärrvärme- och elektricitetsnätet. En stabil konsumtion förminskar efterfrågan av spetskraft och behovet av högkostnadskraftverk som oljebrännare och gasturbiner. Fördelen är ett minskat koldioxidutsläpp samtidigt som producent och konsument gagnas ekonomiskt av billigare produktionskostnader. Ett av de primära målen med denna studie var att simulera en detaljerad modellvåning i en pedagogisk kontorsbyggnad för att fastställa den monetära besparingspotentialen hos efterfrågeflexibilitet kombinerad med dynamisk timbaserad prissättning av fjärrvärme och elektricitet. Därtill jämfördes potentialen mellan en centraliserad och decentraliserad reglerstrategi. Efterfrågeflexibilitetens inverkan på inomhusklimatet samt byggnadens uppvärmningsflexibilitet utgjorde områden av intresse. Uppvärmningsflexibiliteten är ett mått på byggnadens förmåga att anpassa uppvärmningen enligt de dynamiska prissignalerna som energiproducenten anger. Både konstant samt behovsstyrd ventilation inkluderades i studien. Därtill undersöktes besparingspotentialen vid begränsning av fjärrvärmeanslutningens toppeffekt. Efterfrågeflexibilitetens inverkan studerades genom styrning av rumsuppvärmning, tilluftstemperatur samt reglering av luftflöden. Studien genomfördes med dynamiska energisimuleringar av en pedagogisk kontorsbyggnad belägen på Aalto Universitetets campus område. Simuleringsprogrammet som användes var IDA Indoor Climate and Energy (version 4.7.1). Acceptabla intervall för inomhusklimatets komfortparametrar (temperatur, PMV, CO2) definierades och regelbaserade kontrollalgoritmer utvecklades samt implementerades i simuleringsprogrammet. En centraliserad reglerstrategi inom uppvärmning gav inga signifikanta besparingar (1.5%) och uppvärmningsflexibiliteten förblev låg (2.9%). Den decentraliserade strategin gav kostnadsbesparingar uppemot 5% – 6% och en uppvärmningsflexibilitet på 15% vid styrning av både uppvärmning samt tilluftstemperatur. Alla regleralternativen rörande efterfrågeflexibilitet lyckades upprätthålla en god termisk komfort över 90% av den ockuperade tiden. Användningsgraden hade ingen inverkan på vare sig kostnadsbesparing, flexibilitet eller termisk komfort i något av fallen. Fjärrvärmeanslutningens toppeffekt kunde skäras ned med 35% utan att det inverkade på den termiska komforten. Detta gav en årlig kostnadsbesparing på 6.1 €/m2 – 26.9 €/m2 (27.1% – 35%) beroende på fjärrvärmeproducent. En nedskärning av toppeffekten med 43% hade endast smärre inverkan på den termiska komforten och tillförde ytterligare kostnadsbesparing. De primära slutsatserna från studien är att efterfrågeflexibilitet inom uppvärmning är endast fördelaktig med en decentraliserad reglerstrategi och att begränsning av toppeffekt inom fjärrvärme har en stor besparingspotential

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

Peer ReviewedPostprint (author's final draft

An energy-efficient smart comfort sensing system based on the IEEE 1451 standard for green buildings

In building automation, comfort is an important aspect, and the real-time measurement of comfort is notoriously complicated. In this paper, we have developed a wireless, smart comfort sensing system. The important parameters in designing the prevalent measurement of comfort systems, such as portability, power consumption, reliability, and system cost, were considered. To achieve the target design goals, the communication module, sensor node, and sink node were developed based on the IEEE1451 standard. Electrochemical and semiconductor sensors were considered for the development of the sensor array, and the results of both technologies were compared. The sensor and sink nodes were implemented using the ATMega88 microcontroller. Microsoft Visual Studio 2013 preview was used to create the graphical user interface in C#. The sensors were calibrated after the signal processing circuit to ensure that the standard accuracy of the sensor was achieved. This paper presents detailed design solutions to problems that existed in the literature.http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=7361hj201

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

Neural network-based predictive control system for energy optimization in sports facilities: a case study

Given the increased global energy demand and its associated environmental impacts, the management and optimization of sports facilities is becoming imperative as they are characterized by high energy demand and occupancy profiles. In this work, the theory of model predictive control ȋMPCȌ is combined with neural networks for temperature setpoint selection to achieve energy and performance optimization of sports facilities. It is demonstrated using the building information model ȋBIMȌ of a sports hall in the sports complex of Qatar University. MPC systems are powerful as they allow integrated dynamic optimization that accounts for the future system behavior in the decision-making process, while neural networks are advantageous for their ability to represent complex interdependencies with high accuracy. The proposed approach was able to achieve a total energy savings of around ͵͵Ψ. Considerations about the network performance, MPC settings tuning, and optimization sub-optimality or failure are essential during the design and implementation phases of the proposed system

Exploring Energy, Comfort, and Building Health Impacts of Deep Setback and Normal Occupancy Smart Thermostat Implementation

As smart thermostat adoption rates continue to increase, it becomes worthwhile to explore what unanticipated outcomes may result in their use. Specific attention was paid to smart thermostat impacts to deep setback and normal occupancy states in a variety of conditions while complying with the ventilation and temperature requirements of ASHRAE 90.2-2013. Custom weather models and occupancy schedules were generated to efficiently explore a combination of weather conditions, building constructions, and occupancy states. The custom modeling approach was combined with previous experimental data within the Openstudio graphics interface to the EnergyPlus building modeling engine. Results indicate smart thermostats add the most value to winter deep setback conditions while complying with ASHRAE 90.2. Major potential humidity issues were identified when complying with ASHRAE 90.2 during cooling season. It also appears smart thermostats add little value to occupants when complying with ASHRAE 90.2 during cooling season across multiple climates and building constructions. Further exploration into humidity issues identified are required, as well as refining the energy model and moving towards real-world validation