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

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

Optimierungsrahmen für die Verbesserung der Energieflexibilität in Wohngebäuden

Energy flexibility is balancing the supply and demand of a building according to climate conditions, user preferences, and grid constraints. Energy flexibility in households is a practical approach to achieving sustainability in the building sector. However, the diversity in flexibility potential of energy systems and climatic variability complicate the selection of envelope parameters and building energy systems (BESs). This study aimed to design a framework to improve the energy flexibility of the building. For this purpose, a single-family house and diversified BESs were simulated in a TRNSYS-Python co-simulation platform. Initially, the bi-objective optimization identified flexible building envelopes in twenty-four locations. Then, the multi-criteria assessment of BESs was conducted using life-cycle energy flexibility indicators. Lastly, the energy flexibility potential of the BES was evaluated by employing steady-state optimization and model predictive control (MPC). The findings of this work set a benchmark for flexible household envelopes. The systematic approach for selecting BES could guide the energy system design, providing insight into energy flexibility. Further, this investigation has established that the dataset of building thermal load, boundary conditions, and control disturbances can be used to develop an MPC-based dynamic control. That controller could be employed on different BESs to achieve energy flexibility.Energieflexibilität ist der Ausgleich von Versorgung und Bedarf eines Gebäudes je nach Klima, Nutzerpräferenzen und Netzbeschränkungen. Energieflexibilität ist damit ein praktischer Ansatz für Nachhaltigkeit in Gebäuden. Die Vielfalt des Flexibilitätspotenzials von Energiesystemen und die klimatischen Unterschiede erschweren jedoch die Auswahl von Hüllparametern und Gebäudeenergiesystemen (BESs). Diese Studie zielte darauf ab, einen Rahmen zur Verbesserung der energetischen Flexibilität von Gebäuden zu entwickeln. Hierzu wurden ein Einfamilienhaus und verschiedene BES in einer TRNSYS-Python Co-Simulationsplattform simuliert. Zunächst wurden über eine bi-objektive Optimierung flexible Gebäudehüllen an vierundzwanzig Standorten ermittelt. Danach erfolgte eine multikriterielle Bewertung der BES anhand von Energieflexibilitätsindikatoren über den gesamten Lebenszyklus. Schließlich wurde das Energieflexibilitätspotenzial der BES durch den Einsatz statischer Optimierung und modellprädiktiver Regelung (MPC) bewertet. Die Ergebnisse dieser Arbeit setzen einen Maßstab für flexible Gebäudehüllen. Der systematische Ansatz zur Auswahl von BES könnte als Leitfaden für die Auslegung zukünftiger Systeme dienen. Darüber hinaus hat die Untersuchung ergeben, dass Daten zu thermischer Belastung des Gebäudes, Randbedingungen und Regelungsstörungen zur Entwicklung eines MPC verwendet werden können. Dieser Regler könnte bei verschiedenen BES eingesetzt werden, um Energieflexibilität zu erreichen

Reducing the performance gap using calibrated simulation models

Buildings have a significant impact on the environment. Construction of buildings and their operation accounts for 36% of global final energy use and 40% of energy‐related carbon dioxide (CO2) emissions. Also, as per the 2019 International Energy Agency (IEA) and United Nations Environment Programme (UNEP) report, the building sector has a strong potential to provide long-term energy and greenhouse gas emission savings without high financial costs. Building performance simulation tools, ranging from steady-state calculations to dynamic simulation methods, can calculate the anticipated energy consumption of a building with adequate levels of accuracy. However, there is considerable evidence to suggest that buildings underperform post-completion when compared against the expected performance prediction during the design-stage. The difference between the actual operation and the design intent is termed the ‘performance gap’. While the energy performance gap in buildings is a well-known phenomenon, its in-use interpretation is quite vague. It is important to understand the basis of assumptions and protocols used in design-stage performance calculations to assess the causes of the performance gap. In the context of the performance gap, energy performance is generally the most emphasised. The gap, however, is not limited to energy – it also applies to indoor environmental quality (IEQ) parameters, such as temperature and air quality. Moreover, the pursuit of energy efficiency may have the unintended consequence of compromising IEQ, thereby requiring a comprehensive approach to performance assessment. It is therefore important to consider energy and IEQ performance issues together. This thesis contributes to an improved understanding, quantification and resolution of performance gap related issues by using a novel simulation-based approach that enables systematic identification and classification of the root causes of the performance gap. A new measurement and verification (M&V) framework that is underpinned by building performance simulation and calibration is proposed. A key aspect of this new methodological framework is the identification and separation of the three types of performance gaps because of: 1. Use of inappropriate design-stage calculation methods (such as those used for regulatory compliance), 2. Technical issues with the building, its systems and their operations, and 3. Operational changes that the building has gone through to meet its functional requirements. For the first type of performance gap, CIBSE TM54 (CIBSE, 2013a) already provides guidance to reduce the perceived gap and enable improved estimates of building performance during the design-stage. This thesis focuses on the understanding of operational-stage issues and their detailed causes, related to the second and third types of the performance gap. This thesis is the first study that systematically defines, identifies and separates, • the technical issues that cause the performance gap between design intent and actual operation, and • the deviations of operating conditions from the design that are driven by the building’s function and occupancy. This is achieved by integrating the conventional post-occupancy performance assessment approach with building performance modelling and evidence-based model calibration. Another addition to the conventional approach, explored in this study, is the incorporation of IEQ. The issue of IEQ is addressed in two ways: first, by using zonal temperatures for calibration cross-validation, and second, by assessing the energy-related unintended consequences of IEQ underperformance which may happen during building operations. The calibrated simulation models are operationally accurate virtual representation of the actual building and can help to isolate the performance issues and validate the findings. The new framework proposed in this thesis is better suited than conventional M&V protocols such as ASHRAE (American Society of Heating Refrigerating and Air Conditioning Engineers) Guideline 14 and IPMVP (International Performance Measurement and Verification Protocol). These conventional M&V protocols also propose a calibration-based approach, but they generally focus on broad statistical requirements and are not tied to a framework for a procedural verification of all the most important issues that can cause the performance gap. It is likely that using these conventional protocols will identify some key issues during investigations while leaving other potential issues hidden. The guidance on calibration and validation provided in conventional M&V protocols is commonly used for all model calibration exercises. However, the conventional protocols were developed for calculating energy savings in retrofit applications, and the calibration criteria defined in them are mainly for checking the accuracy of building-level energy use totals. The calibration criteria do not check for the uncertainty or the accuracy of dependent parameters, such as zone temperatures and other environmental outputs, which could cross-validate the model. Mathematically, meeting just the statistical criteria for building-level energy use totals in a highly parameterized model and an under-determined search space can lead to unrealistic solutions also being validated. To better support the calibration accuracy with the new proposed M&V framework, advanced model validation criteria have also been developed. New multi-level calibration criteria are proposed, which factors in data quantity, quality and granularity. In this new advanced validation criteria, the current industry standard of monthly energy use checks is the lowest level of calibration, with higher levels requiring detailed checks, using granular and disaggregated energy use. However, all levels of calibration require minimum dependent parameter checks, such as IEQ checks for typical zone temperatures. Dependent parameter checks are desirable in model calibration; however, current statistical criteria used for calibration are not suitable for these checks. Revised and new metrics and thresholds are proposed and explored in this thesis for use in advanced calibrated model validation checks. Beyond the use of IEQ parameters (e.g. zone temperatures) in model calibration, another area of focus of this thesis is the unintended IEQ underperformance captured during the monitoring. The scope of this assessment is limited to underperformance in IEQ parameters linked to achieving high energy efficiency objectives, thermal comfort and indoor air quality (IAQ). Amongst the various IEQ parameters, thermal comfort and IAQ have complex and dynamic interactions with buildings energy end-uses. Comprising of multiple factors, which are both subjective and empirical, thermal comfort and IAQ performances have a high interrelation with the energy performance objectives. Therefore, along with conventionally tracked parameters of temperature and CO2, additional IAQ parameters (not used during the calibration process), such as NO2, PM2.5 and PM10, are analysed to enhance the understanding of unintended energy-related IEQ underperformance. The new methodology proposed in this study is applied to five case study buildings across four building sectors – offices, schools, hospitals and apartment blocks. These buildings represent a large cross-section of the UK building stock and, therefore, can provide useful insights into the issues in the construction sector that drive the performance gap. While detailed performance assessment and advanced validation is done for all five case study buildings using the proposed framework, in one case study building the multi-level calibration checking criteria is also fully explored. Using this methodology on the various case study buildings, cross-sectoral lessons, related to root causes of the energy performance gap and applicable in the wider industry context, are uncovered. Linking to the three types of performance gaps mentioned earlier, analysis of the results shows that, in most of the case studies, some of the energy performance gap is the perceived gap (related to point 1: use of inappropriate design-stage calculation methods) or is because of operational changes (related to point 3: changes that the building has gone through to meet its functional requirements). However, the most critical cause of the gap is due to technical issues (related to point 2: issues with the building, its systems and their operations) identified across the case studies. These issues were either design errors, improper construction and installation, poor commissioning or shortcomings in building systems and the use of new low-carbon technologies. It was observed that long-term involvement (with responsibility for the operational performance) of the design and construction teams are effective in lowering performance gaps. Issues related to IEQ were also observed across the case studies, such as overheating risks and poor IAQ. These added to the existing knowledge of energy-related IEQ issues and highlighted the need to address IEQ simultaneously with energy through better design, advanced operational controls and by incorporating regular IEQ measurements as part of operations and maintenance protocols. The novel approach presented here builds a case to move building performance calculations towards an operational context, where design projections are done using advanced simulation and with a view of tracking the projections through to operation using measurable performance outcomes. Overall, the study shows the importance of the early involvement of all stakeholders and their accountability to minimise performance issues. Integrating the findings from the case studies, a case could be built for having IEQ performance objectives in energy performance contracts. This can mitigate the trade-offs of IEQ against energy performance that leads to unintended health consequences for occupants. Further, this work promotes a way of integrating dynamic thermal simulation in regular post-occupancy checking and management of buildings

A novel concept to measure envelope thermal transmittance and air infiltration using a combined simulation and experimental approach

This paper presents a novel method to determine building envelope thermal transmittance (known as U-values) and air infiltration rate by a combination of Energy modeling (DesignBuilder and EnergyPlus), regression models and genetic algorithm at quasi-steady state conditions. DesignBuilder is used to develop the thermal model of an office building, including physical building models, materials specification, occupancy schedules, detailed HVAC system and components for energy simulation purposes. Specifically, the simulation was carried out in EnergyPlus at diverse U-values and air infiltration rates to produce a large datasets. Subsequently, the results were used to generate a linear regression model to evaluate the associations of thermal demands with U-values and air infiltration rate. Genetic algorithm was then applied to obtain a set of U-values and air infiltration rate with the minimum difference between field measurement and model prediction. The calibrated U-values and air infiltration rate were employed as inputs in EnergyPlus to model one workday heat consumption. When compared with thermal demand from measured data, the accuracy of the calibrated model improved significantly

An integrated approach to indoor contaminant modelling

Air pollutants are those chemicals that are not generally present in the atmosphere because of natural causes but are disseminated into the air by human activity. In most parts of Europe, outdoor pollutants are principally the products of combustion from space heating, power generation, chemical industry waste, or from motor vehicle traffic (McGinlay 1997). Indoor air environments contain a myriad of inorganic and organic gases and vapors typically in trace (parts-per-billion) quantities. The chemical composition of air varies widely between particular locations as well as between measurements taken at different times for the same location. The nature of these variations is such that it is difficult to definitively characterize a typical indoor air environment with respect to specific contaminants present and concentration levels. A large number of air pollutants have known or suspected harmful effects that can be manifested on plant or animal life and/or the environment. Pollutants may not only prove a problem in the immediate vicinity of their emission, but they can travel long distances and react with other species present in the atmosphere to produce secondary pollutants (Weschler 2004)