Apply Active Learning in Short-term Data-driven Building Energy Modeling

In the United States, the buildings sector accounted for about 41% of primary energy consumption. Building control and operation strategies have a great impact on building energy efficiency and the development of building-grid integration. For better building control, and for buildings to be better integrated with the grid operation, high fidelity building energy forecasting model that can be used for short-term and real-time operation is in great need. With the wide adoption of building automation system (BAS) and Internet of things (IoT), massive measurements from sensors and other sources are continuously collected which provide data on equipment and building operations. This provides a great opportunity for data-driven building energy modeling. However, data-driven approach is heavily dependent on data, while the collected operation data are often constrained to limited applicability (or termed as “bias” in this paper) because most of the building operation data are generated under limited operational modes, weather conditions, and very limited setpoints (often one or two fixed values, such as a constant zone temperature setpoint). For nonlinear systems, a data-driven model generated from biased data has poor scalabilities (when used for a different building) and extendibility (when used for different weather and operation conditions). The fact impedes the development of data-driven forecasting model as well as model-based control in buildings. The design of task that aims to describe or explain the variation of information under conditions that are hypothesized to reflect the variation is termed as active learning in machine learning. The purpose is to choose or generate informative training data, either to defy data bias or to reduce labeling cost (when doing experiments in building is too expensive). Research on applying active learning in building energy modeling is relatively unexplored. From the few existing researches, most of them only consider single operational setpoint, which is impractical for most real buildings where multiple setpoints in chillers, air handling units and air-conditioning terminals are used for building operation and control. Moreover, disturbances, especially weather and occupancy, in most cases are not considered. In this research, a nonlinear fractional factorial design combined with block design is applied as the active learning strategy to generate building operation (setpoints) schedule. The data generated on operation schedule will be used as training data for building energy modeling. The testbed is a virtual DOE reference large-size office building with hierarchical setpoints: zone temperature setpoint, supply air static pressure setpoint and chiller leaving water temperature setpoint. D-Optimal will be used as the nonlinear fractional factorial design algorithm, and its parameters are further compared and discussed. At the same time, block design will be applied to divide different weather and occupancy into four blocks. And D-Optimal design will be applied in each block, in which way the disturbance will be taken into consideration. Results show that compared with normal operation data and data generated by full factorial design, the proposed active learning method can increase model accuracy in validation and testing period, indicating its effectiveness to improve model generalization

Net-zero Building Cluster Simulations and On-line Energy Forecasting for Adaptive and Real-Time Control and Decisions

Buildings consume about 41.1% of primary energy and 74% of the electricity in the U.S. Moreover, it is estimated by the National Energy Technology Laboratory that more than 1/4 of the 713 GW of U.S. electricity demand in 2010 could be dispatchable if only buildings could respond to that dispatch through advanced building energy control and operation strategies and smart grid infrastructure. In this study, it is envisioned that neighboring buildings will have the tendency to form a cluster, an open cyber-physical system to exploit the economic opportunities provided by a smart grid, distributed power generation, and storage devices. Through optimized demand management, these building clusters will then reduce overall primary energy consumption and peak time electricity consumption, and be more resilient to power disruptions. Therefore, this project seeks to develop a Net-zero building cluster simulation testbed and high fidelity energy forecasting models for adaptive and real-time control and decision making strategy development that can be used in a Net-zero building cluster. The following research activities are summarized in this thesis: 1) Development of a building cluster emulator for building cluster control and operation strategy assessment. 2) Development of a novel building energy forecasting methodology using active system identification and data fusion techniques. In this methodology, a systematic approach for building energy system characteristic evaluation, system excitation and model adaptation is included. The developed methodology is compared with other literature-reported building energy forecasting methods; 3) Development of the high fidelity on-line building cluster energy forecasting models, which includes energy forecasting models for buildings, PV panels, batteries and ice tank thermal storage systems 4) Small scale real building validation study to verify the performance of the developed building energy forecasting methodology. The outcomes of this thesis can be used for building cluster energy forecasting model development and model based control and operation optimization. The thesis concludes with a summary of the key outcomes of this research, as well as a list of recommendations for future work.Ph.D., Civil Engineering -- Drexel University, 201

Final Causality in the Thought of Thomas Aquinas

Throughout his corpus, Thomas Aquinas develops an account of final causality that is both philosophically nuanced and interesting. The aim of my dissertation is to provide a systematic reconstruction of this account of final causality, one that clarifies its motivation and appeal. The body of my dissertation consists of four chapters. In Chapter 1, I examine the metaphysical underpinnings of Aquinas’s account of final causality by focusing on how Aquinas understands the causality of the final cause. I argue that Aquinas holds that an end is a cause because it is the determinate effect toward which an agent’s action is directed. I proceed by first presenting the general framework of causality within which Aquinas understands final causality. I then consider how Aquinas justifies the reality of each of the four kinds of cause, placing special emphasis on the final cause. In Chapter 2, I consider final causality from the perspective of goodness and explore the reasons why Aquinas thinks that the end of an action is always good. For even if one was convinced that the end of an action is indeed a cause, one might still resist attributing any normative or evaluative properties to the end, much less a positively-valenced normative property like goodness. In this chapter, I show how, given Aquinas’s metaphysics of powers and his characterization of goodness as that which all desire, it follows that every action is for the sake of some good. In Chapter 3, I consider Aquinas’s account of the relation between final causality and cognition. In many passages throughout his corpus—most famously in the fifth of his Five Ways—Aquinas advances the claim that cognition plays an essential role in final causality. In this chapter, I explore Aquinas’s account of the relation between final causality and cognition by reconstructing his Fifth Way and investigating the metaphysical foundations on which it rests. While the first three chapters of my dissertation focus on Aquinas’s account of final causality from the perspective of the ends of individual agents, in Chapter 4 I broaden my focus to consider the way in which the account of final causality developed in these earlier chapters shapes Aquinas’s philosophical cosmology. I argue that, on Aquinas’s view, when an individual agent acts for an end, it is plays a role in a larger system, e.g. a polis, an ecosystem, or the universe itself

Model-Based Enhanced Operation of Building Convective Heating Systems and Active Thermal Storage

This thesis presents an experimental and theoretical study of a reduced-order modelling methodology and dynamic response of convectively heated buildings and active thermal storage. A methodology was developed for the generation of control-oriented building models which can be used within model predictive control (MPC) or other model-based control strategies to satisfy occupant comfort and improve building-grid interaction. A methodology to identify and evaluate MPC strategies is presented to improve a building's energy flexibility. There is an emphasis on modelling building thermal mass and a dedicated thermal storage device. The two applications for reduced-order thermal modelling (buildings and dedicated active thermal energy storage devices) require different modelling approaches for control applications. Several case studies are introduced and are typical Quebec construction with convective-based heating systems: a detached low-mass house, a low-mass retail building, and a warehouse (with active thermal storage device). The residential building study outlined a methodology for multi-level control-oriented modelling with several zones and multiple floors. This multi-level approach allows the user to “zoom in and out” so that models at each control level remain manageable. In the second case study, implementation of MPC was presented for a conventional bank building to reduce the yearly utility bill and avoid the summer peak load penalty. A cost savings of 25% on the yearly electric utility bill and a peak power reduction of 38% were achieved. With the new optimized operation, the cost per square meter for the bank would decrease from $30.19/m2 to$22.57/m2, or a yearly savings of $7.62/m2. The last case study comprises a 1650 m2 warehouse equipped with a dedicated active high-temperature thermal energy storage device. A methodology was presented for the development and analysis of control-oriented models for enhanced operation of the electric thermal storage device. The goal was to maximize the building energy flexibility the building could provide to the grid by evaluating the Building Energy Flexibility Index (BEFI). A BEFI of 55% to 100% was achieved. The average demand during the critical times was reduced between 36 kW and 65 kW and the utility cost to the customer can be reduced by 12-30%

Model Predictive Control of Building Systems for Energy Flexibility

RÉSUMÉ Les besoins énergétiques des bâtiments contribuent de manière significative à la demande de pointe du réseau électrique. Cependant, les bâtiments, par leur capacité de stockage de l’énergie, peuvent fournir des services de flexibilité énergétique au réseau. La Gestion de la Demande de Puissance (GDP) du bâtiment est considérée comme une solution pratique pour réduire les demandes de pointe du réseau. Cette approche est moins coûteuse et plus écologique que d’utiliser la réserve de puissance ou que d'investir dans de nouvelles infrastructures. La GDP peut également jouer un rôle plus important au niveau de l’équilibrage de charge, lorsque le réseau intègre des sources d’énergie renouvelables, qui sont intermittentes et variables. Cette thèse étudie le potentiel de flexibilité énergétique des bâtiments vis-à-vis du réseau électrique par le biais de la simulation. Une méthodologie générale pour caractériser la flexibilité énergétique des bâtiments, ainsi qu’un ensemble d’indicateurs sont proposés. La méthodologie est testée sur un modèle détaillé de maison canadienne type, calibré avec des données mensuelles et horaires mesurées. La calibration permet de représenter fidèlement la consommation d'énergie selon les critères de la directive 14 de l’ASHRAE, ainsi que les variations dynamiques des conditions thermiques intérieures, ce qui est nécessaire pour l'étude des stratégies de commande. Les résultats des simulations, basés sur ce modèle calibré, montrent que la flexibilité énergétique fournie par la masse thermique du bâtiment est importante, même pour les bâtiments résidentiels à faible masse thermique. La quantité d'énergie flexible dépend cependant des conditions météorologiques, de l'heure du jour, de la durée de la GDP et de l'occupation du bâtiment. La flexibilité énergétique est également fortement liée à la stratégie de commande du système de chauffage et climatisation. Une méthode de contrôle avancée est étudiée : la Commande Prédictive basée sur un Modèle (CPM). Avant d’appliquer cette méthode à la flexibilité énergétique, un cadre général de CPM est proposé. Les erreurs de modélisation, l’estimation de l’état et l’identification des paramètres y sont discutées en détail. Ce cadre est ensuite appliqué à deux types de modèles de contrôleurs différents : un modèle détaillé et un modèle simplifié du bâtiment étudié. Les résultats montrent que la CPM peut améliorer la flexibilité énergétique par rapport à une stratégie de Commande Basée sur les Règles (CBR).----------ABSTRACT Energy needs from buildings contribute a large share to the peak demand of the electric grid. Meanwhile, buildings can also provide energy flexibility services to the grid with their related assets, e.g. energy storage. Demand Response (DR) of building systems has been considered a feasible solution to shift loads, or to reduce the peak demands. This approach is less costly and more environmentally-friendly than operating reserve power, or investing in extra power plants. DR can play a more important role for load balancing when the grid integrates with renewable energy sources, which are intermittent and variable. This thesis investigates the energy flexibility potential in buildings for the grid through simulation studies. A general methodology to characterize the building energy flexibility is proposed along with a set of indicators. The methodology is applied to a detailed building model of a typical Canadian home, which is calibrated with monthly and hourly measured data. The calibration evaluates not only the energy use required by the ASHRAE guideline 14, but also the dynamic indoor conditions, which is important to study control strategies. Simulation results, based on the calibrated model, show that the energy flexibility provided by the building thermal mass is significant, even for typical Canadian residential buildings with a low thermal mass. The amount of flexible energy however depends on the weather condition, time of day, duration of the DR event and occupancy scenario of the building. The control strategy of the space conditioning system has also a high impact on the energy flexibility. An advanced control method called Model Predictive Control (MPC) is investigated. Prior to applying the MPC method on energy flexibility study, a general supervisory MPC framework is presented. Common issues associated with modelling errors, state estimation, and parameter identification are discussed in detail. The framework is then applied to two different types of controller models: a detailed model and a simplified model of the studied building respectively. The MPC method is shown to be able to increase the building flexibility as compared to the Rule-Based Control (RBC) strategy. MPC with the detailed model delivers the highest flexible energy, twice or three times of the RBC method depending on the time of the DR event

Predictive pre-cooling control for low lift radiant cooling using building thermal mass

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Architecture, 2010.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (p. 143-159).Low lift cooling systems (LLCS) hold the potential for significant energy savings relative to conventional cooling systems. An LLCS is a cooling system which leverages existing HVAC technologies to provide low energy cooling by operating a chiller at low pressure ratios more of the time. An LLCS combines variable capacity chillers, hydronic distribution, radiant cooling, thermal energy storage and predictive control to achieve lower condensing temperatures, higher evaporating temperatures, and reductions in instantaneous cooling loads by spreading the daily cooling load over time. The LLCS studied in this research is composed of a variable speed chiller and a concrete-core radiant floor, which acts as thermal energy storage. The operation of the chiller is optimized to minimize daily energy consumption while meeting thermal comfort requirements. This is achieved through predictive pre-cooling of the thermally massive concrete floor. The predictive pre-cooling control optimization uses measured data from a test chamber, forecasts of controlled climate conditions and internal loads, empirical models of chiller performance, and data-driven models of the temperature response of the zone being controlled. These data and models are used to determine a near-optimal operational strategy for the chiller over a 24-hour horizon. At each hour, this optimization is updated with measured data from the previous hour and new forecasts for the next 24 hours. The novel contributions of this research include the following: experimental validation of the sensible cooling energy savings of the LLCS relative to a high efficiency split system air conditioner - savings measured in a full size test chamber were 25 percent for a typical summer week in Atlanta subject to standard efficiency internal loads; development of a methodology for incorporating real building thermal mass, chiller performance models, and room temperature response models into a predictive pre-cooling control optimization for LLCS; and detailed experimental data on the performance of a rolling-piston compressor chiller to support this and future research.by Nicholas Thomas Gayeski.Ph.D

Optimal Building Thermal Load Scheduling for Simultaneous Participation in Energy and Frequency Regulation Markets

This paper presents an optimal scheduling solution for building thermal loads that simultaneously participate in the wholesale energy and frequency regulation markets. The solution combines (1) a lower-level regulation capacity reset strategy that identifies the available regulation capacity for each hour, and (2) an upper-level zone temperature scheduling algorithm to find the optimal load trajectory with a minimum net electricity cost. In the supervisory scheduling strategy, piece-wise linear approximations of representative air-conditioning equipment behaviors, derived from an offline analysis of the capacity reset mechanism, are used to predict the cooling power and regulation capacity; and a mixed-integer convex program is formulated and solved to determine the optimal control actions. In order to evaluate the performance of the developed control solution, two baseline strategies are considered, one with a conventional night setup/back control and the other utilizing an optimization procedure for minimizing the energy cost only. Five-day simulation tests were carried out for the various control strategies. Compared to the baseline night setup/back strategy, the energy-priority controller led to a 26% lower regulation credit and consequentially caused a net cost increase of 2%; the proposed bi-market control solution was able to increase the regulation credit by 118% and reduce the net electricity cost by 14%.Open Access fees paid for in whole or in part by the University of Oklahoma Libraries.Ye