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

Learning-Initialized Trajectory Planning in Unknown Environments

Autonomous flight in unknown environments requires precise planning for both the spatial and temporal profiles of trajectories, which generally involves nonconvex optimization, leading to high time costs and susceptibility to local optima. To address these limitations, we introduce the Learning-Initialized Trajectory Planner (LIT-Planner), a novel approach that guides optimization using a Neural Network (NN) Planner to provide initial values. We first leverage the spatial-temporal optimization with batch sampling to generate training cases, aiming to capture multimodality in trajectories. Based on these data, the NN-Planner maps visual and inertial observations to trajectory parameters for handling unknown environments. The network outputs are then optimized to enhance both reliability and explainability, ensuring robust performance. Furthermore, we propose a framework that supports robust online replanning with tolerance to planning latency. Comprehensive simulations validate the LIT-Planner's time efficiency without compromising trajectory quality compared to optimization-based methods. Real-world experiments further demonstrate its practical suitability for autonomous drone navigation

Experiment Design and Training Data Quality of Inverse Model for Short-term Building Energy Forecasting

For data-driven building energy forecasting modeling, the quality of training data strongly affects a model’s accuracy and cost-effectiveness. In order to obtain high-quality training data within a short time period, experiment design, active learning, or excitation is becoming increasingly important, especially for nonlinear systems such as building energy systems. Experiment design and system excitation have been widely studied and applied in fields such as robotics and automobile industry for their model development. But these methods have hardly been applied for building energy modeling. This paper presents an overall discussion on the topic of applying system excitation for developing building energy forecasting models. For gray-box and white-box models, a model’s physical representations and theories can be applied to guide their training data collections. However, for black-box (pure-data-driven) models, the training data’s quality is sensitive to the model structure, leading to a fact that there is no universal theory for data training.  The focus of black-box modeling has traditionally been on how to represent a data set well. The impact of how such a data set represents the real system and how the quality of a training data set affect the performances of black-box models have not been well studied. In this paper, the system excitation method, which is used in system identification area, is used to excite zone temperature set-points to generate training data. These training data from system excitation are then used to train a variety of black-box building energy forecasting models. The models’ performances (accuracy and extendibility) are compared among different model structures. For the same model structure, its performances are also compared between when it is trained using typical building operational data and when it is trained using exited training data. Results show that the black-box models trained by normal operation data achieve better performance than that trained by excited training data but have worse model extendibility; Training data obtained from excitation will help to improve performances of system identification models

Experimental Assessment on the Hysteretic Behavior of a Full-Scale Traditional Chinese Timber Structure Using a Synchronous Loading Technique

In traditional Chinese timber structures, few tie beams were used between columns, and the column base was placed directly on a stone base. In order to study the hysteretic behavior of such structures, a full-scale model was established. The model size was determined according to the requirements of an eighth grade material system specified in the architectural treatise Ying-zao-fa-shi written during the Song Dynasty. In light of the vertical lift and drop of the test model during horizontal reciprocating motions, the horizontal low-cycle reciprocating loading experiments were conducted using a synchronous loading technique. By analyzing the load-displacement hysteresis curves, envelope curves, deformation capacity, energy dissipation, and change in stiffness under different vertical loads, it is found that the timber frame exhibits obvious signs of self-restoring and favorable plastic deformation capacity. As the horizontal displacement increases, the equivalent viscous damping coefficient generally declines first and then increases. At the same time, the stiffness degrades rapidly first and then decreases slowly. Increasing vertical loading will improve the deformation, energy-dissipation capacity, and stiffness of the timber frame

Deep Learning for Feynman's Path Integral in Strong-Field Time-Dependent Dynamics

Feynman's path integral approach is to sum over all possible spatio-temporal paths to reproduce the quantum wave function and the corresponding time evolution, which has enormous potential to reveal quantum processes in classical view. However, the complete characterization of quantum wave function with infinite paths is a formidable challenge, which greatly limits the application potential, especially in the strong-field physics and attosecond science. Instead of brute-force tracking every path one by one, here we propose deep-learning-performed strong-field Feynman's formulation with pre-classification scheme which can predict directly the final results only with data of initial conditions, so as to attack unsurmountable tasks by existing strong-field methods and explore new physics. Our results build up a bridge between deep learning and strong-field physics through the Feynman's path integral, which would boost applications of deep learning to study the ultrafast time-dependent dynamics in strong-field physics and attosecond science, and shed a new light on the quantum-classical correspondence

Tempol Protects Against Acetaminophen Induced Acute Hepatotoxicity by Inhibiting Oxidative Stress and Apoptosis

Acetaminophen (APAP)-induced acute hepatotoxicity is the leading cause of drug-induced acute liver failure. The aim of this study was to evaluate the effects of 4-hydroxy-2,2,6,6-tetramethylpiperidine-N-oxyl (tempol) on the protection of APAP-induced hepatotoxicity in mice. Mice were pretreated with a single dose of tempol (20 mg/kg per day) orally for 7 days. On the seventh day, mice were injected with a single dose of APAP (300 mg/kg) to induce acute hepatotoxicity. Our results showed that tempol treatment markedly improved liver functions with alleviations of histopathological damage induced by APAP. Tempol treatment upregulated levels of antioxidant proteins, including superoxide dismutase, catalase, and glutathione. Also, phosphorylation of phosphoinositide 3-kinase (PI3K) and protein kinase B (Akt) and protein expression of nuclear factor erythroid 2-related factor (Nrf 2) and heme oxygense-1 (HO-1) were all increased by tempol, which indicated tempol protected against APAP-induced hepatotoxicity via the PI3K/Akt/Nrf2 pathway. Moreover, tempol treatment decreased pro-apoptotic protein expressions (cleaved caspase-3 and Bax) and increased anti-apoptotic Bcl-2 in liver, as well as reducing apoptotic cells of TUNEL staining, which suggested apoptotic effects of tempol treatment. Overall, we found that tempol normalizes liver function in APAP-induced acute hepatotoxicity mice via activating PI3K/Akt/Nrf2 pathway, thus enhancing antioxidant response and inhibiting hepatic apoptosis

Efficient residual network using hyperspectral images for corn variety identification

Corn seeds are an essential element in agricultural production, and accurate identification of their varieties and quality is crucial for planting management, variety improvement, and agricultural product quality control. However, more than traditional manual classification methods are needed to meet the needs of intelligent agriculture. With the rapid development of deep learning methods in the computer field, we propose an efficient residual network named ERNet to identify hyperspectral corn seeds. First, we use linear discriminant analysis to perform dimensionality reduction processing on hyperspectral corn seed images so that the images can be smoothly input into the network. Second, we use effective residual blocks to extract fine-grained features from images. Lastly, we detect and categorize the hyperspectral corn seed images using the classifier softmax. ERNet performs exceptionally well compared to other deep learning techniques and conventional methods. With 98.36% accuracy rate, the result is a valuable reference for classification studies, including hyperspectral corn seed pictures

Short-term Building Energy Model Recommendation System: A Meta-learning Approach

High-fidelity and computationally efficient energy forecasting models for building systems are needed to ensure optimal automatic operation, reduce energy consumption, and improve the building’s resilience capability to power disturbances. Various models have been developed to forecast building energy consumption. However, given buildings have different characteristics and operating conditions, model performance varies. Existing research has mainly taken a trial-and-error approach by developing multiple models and identifying the best performer for a specific building, or presumed one universal model form which is applied on different building cases. To the best of our knowledge, there does not exist a generalized system framework which can recommend appropriate models to forecast the building energy profiles based on building characteristics. To bridge this research gap, we propose a meta-learning based framework, termed Building Energy Model Recommendation System (BEMR). Based on the building’s physical features as well as statistical and time series meta-features extracted from the operational data and energy consumption data, BEMR is able to identify the most appropriate load forecasting model for each unique building. Three sets of experiments on 48 test buildings and one real building were conducted. The first experiment was to test the accuracy of BEMR when the training data and testing data cover the same condition. BEMR correctly identified the best model on 90% of the buildings. The second experiment was to test the robustness of the BEMR when the testing data is only partially covered by the training data. BEMR correctly identified the best model on 83% of the buildings. The third experiment uses a real building case to validate the proposed framework and the result shows promising applicability and extensibility. The experimental results show that BEMR is capable of adapting to a wide variety of building types ranging from a restaurant to a large office, and gives excellent performance in terms of both modeling accuracy and computational efficiency