scholarly journals Comparative Evaluation of Control-Oriented Zone Temperature Prediction Modeling Strategies in Buildings

2015 ◽  
Author(s):  
Venkatesh Chinde ◽  
Jeffrey C. Heylmun ◽  
Adam Kohl ◽  
Zhanhong Jiang ◽  
Soumik Sarkar ◽  
...  
Keyword(s):  
Comparative Evaluation ◽  
Performance Monitoring ◽  
Control Strategies ◽  
Critical Role ◽  
Energy Resource ◽  
Real Data ◽  
Data Driven ◽  
Monitoring And Control ◽  
Test Bed ◽  
Occupant Comfort

Predictive modeling of zone environment plays a critical role in developing and deploying advanced performance monitoring and control strategies for energy usage minimization in buildings while maintaining occupant comfort. The task remains extremely challenging, as buildings are fundamentally complex systems with large uncertainties stemming from weather, occupants, and building dynamics. Over the past few years, purely data-driven various control-oriented modeling techniques have been proposed to address different requirements, such as prediction accuracy, flexibility, computation and memory complexity. In this context, this paper presents a comparative evaluation among representative methods of different classes of models, such as first principles driven (e.g., lumped parameter autoregressive models using simple physical relationships), data-driven (e.g., artificial neural networks, Gaussian processes) and hybrid (e.g., semi-parametric). Apart from quantitative metrics described above, various qualitative aspects such as cost of commissioning, robustness and adaptability are discussed as well. Real data from Iowa Energy Center’s Energy Resource Station (ERS) test bed is used as the basis of evaluation presented here.

scholarly journals Towards Data-Driven Vehicle Estimation for Signalised Intersections in a Partially Connected Environment

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8477
Author(s):  
Roozbeh Mohammadi ◽  
Claudio Roncoli
Keyword(s):  
Traffic Control ◽  
Partial Information ◽  
Control Strategies ◽  
Synthetic Data ◽  
Real Data ◽  
Significant Loss ◽  
Data Driven ◽  
Estimation Methods ◽  
Connected Vehicles ◽  
Vehicle Data

Connected vehicles (CVs) have the potential to collect and share information that, if appropriately processed, can be employed for advanced traffic control strategies, rendering infrastructure-based sensing obsolete. However, before we reach a fully connected environment, where all vehicles are CVs, we have to deal with the challenge of incomplete data. In this paper, we develop data-driven methods for the estimation of vehicles approaching a signalised intersection, based on the availability of partial information stemming from an unknown penetration rate of CVs. In particular, we build machine learning models with the aim of capturing the nonlinear relations between the inputs (CV data) and the output (number of non-connected vehicles), which are characterised by highly complex interactions and may be affected by a large number of factors. We show that, in order to train these models, we may use data that can be easily collected with modern technologies. Moreover, we demonstrate that, if the available real data is not deemed sufficient, training can be performed using synthetic data, produced via microscopic simulations calibrated with real data, without a significant loss of performance. Numerical experiments, where the estimation methods are tested using real vehicle data simulating the presence of various penetration rates of CVs, show very good performance of the estimators, making them promising candidates for applications in the near future.

Achieving Improved Personalization and Energy Efficiency in Cohabited Work-Spaces Through Data-Driven Predictive Control

2020 ◽  
Author(s):  
Syed Ahsan Raza Naqvi ◽  
Zachary Nawrocki ◽  
Zaid Bin Tariq ◽  
Koushik Kar ◽  
Sandipan Mishra
Keyword(s):  
Predictive Control ◽  
Energy Savings ◽  
Control Strategies ◽  
Control Policy ◽  
Data Driven ◽  
Test Bed ◽  
Model Learning ◽  
Conference Room ◽  
Work Spaces ◽  
Temperature Preferences

Abstract This paper studies the problem of indoor zone temperature control in shared work-spaces equipped with heterogeneous heating and cooling sources with the goal of increased energy savings and environment personalization. We consider two scenarios to assess the performance of our control strategies. The first scenario requires time-bound pre-cooling/pre-heating of a shared space in preparation for a scheduled activity (Scenario A). The second scenario considers a cohabited work-space where occupants have different temperature preferences (Scenario B). Utilizing an on-campus smart conference room (SCR) as a test-bed, we use data-driven model learning to establish a relationship between the room’s heating, ventilation and cooling (HVAC) operations and the zone temperatures. Next, we use a model predictive control (MPC)-based approach to achieve a desired average temperature while minimizing power consumption (for Scenario A) and minimize the thermal discomfort experienced by individuals based on their temperature preferences (for Scenario B). The experimental results show that for Scenario A, the proposed control policy can save a significant amount of energy and achieve the desired mean temperature in the space fairly accurately. We further note that for Scenario B, the control scheme can achieve a significant spatial differentiation in temperature towards satisfying the occupants’ thermal preferences.

scholarly journals Machine learning applied to simulations of collisions between rotating, differentiated planets

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Miles L. Timpe ◽  
Maria Han Veiga ◽  
Mischa Knabenhans ◽  
Joachim Stadel ◽  
Stefano Marelli
Keyword(s):  
Machine Learning ◽  
Planet Formation ◽  
Critical Role ◽  
Ensemble Methods ◽  
Terrestrial Planet ◽  
Data Driven ◽  
Analytic Methods ◽  
Accurate Treatment ◽  
Post Impact ◽  
The Cost

AbstractIn the late stages of terrestrial planet formation, pairwise collisions between planetary-sized bodies act as the fundamental agent of planet growth. These collisions can lead to either growth or disruption of the bodies involved and are largely responsible for shaping the final characteristics of the planets. Despite their critical role in planet formation, an accurate treatment of collisions has yet to be realized. While semi-analytic methods have been proposed, they remain limited to a narrow set of post-impact properties and have only achieved relatively low accuracies. However, the rise of machine learning and access to increased computing power have enabled novel data-driven approaches. In this work, we show that data-driven emulation techniques are capable of classifying and predicting the outcome of collisions with high accuracy and are generalizable to any quantifiable post-impact quantity. In particular, we focus on the dataset requirements, training pipeline, and classification and regression performance for four distinct data-driven techniques from machine learning (ensemble methods and neural networks) and uncertainty quantification (Gaussian processes and polynomial chaos expansion). We compare these methods to existing analytic and semi-analytic methods. Such data-driven emulators are poised to replace the methods currently used in N-body simulations, while avoiding the cost of direct simulation. This work is based on a new set of 14,856 SPH simulations of pairwise collisions between rotating, differentiated bodies at all possible mutual orientations.

scholarly journals Model-Based and Data-Driven HVAC Control Strategies for Residential Demand Response

2021 ◽  
pp. 1-1
Author(s):  
Xiao Kou ◽  
Yan Du ◽  
Fangxing Li ◽  
Hector Pulgar-Painemal ◽  
Helia Zandi ◽  
...  
Keyword(s):  
Demand Response ◽  
Control Strategies ◽  
Data Driven ◽  
Model Based ◽  
Hvac Control ◽  
Residential Demand

Novel Method for Detecting Weak AE Signals Based on the Similarity of Time-Frequency Spectra

Geophysics ◽  
2021 ◽  
pp. 1-62
Author(s):  
Wencheng Yang ◽  
Xiao Li ◽  
Yibo Wang ◽  
Yue Zheng ◽  
Peng Guo
Keyword(s):  
Mathematical Morphology ◽  
Signal To Noise Ratio ◽  
Critical Role ◽  
Real Data ◽  
Monitoring Method ◽  
Frequency Spectra ◽  
Arrival Times ◽  
Time Frequency ◽  
Fracturing Process ◽  
Ae Signals

As a key monitoring method, the acoustic emission (AE) technique has played a critical role in characterizing the fracturing process of laboratory rock mechanics experiments. However, this method is limited by low signal-to-noise ratio (SNR) because of a large amount of noise in the measurement and environment and inaccurate AE location. Furthermore, it is difficult to distinguish two or more hits because their arrival times are very close when AE signals are mixed with the strong background noise. Thus, we propose a new method for detecting weak AE signals using the mathematical morphology character correlation of the time-frequency spectrum. The character in all hits of an AE event can be extracted from time-frequency spectra based on the theory of mathematical morphology. Through synthetic and real data experiments, we determined that this method accurately identifies weak AE signals. Compared with conventional methods, the proposed approach can detect AE signals with a lower SNR.

Mycobacterium tuberculosis Infection in a Domesticated Korean Wild Boar (Sus scrofa coreanus)

2017 ◽  
Vol 80 (6) ◽  
pp. 1009-1014 ◽  
Author(s):  
Min-Goo Seo ◽  
In-Ohk Ouh ◽  
Munki Kim ◽  
Jienny Lee ◽  
Young-Hoan Kim ◽  
...  
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Wild Boar ◽  
Sus Scrofa ◽  
Control Strategies ◽  
Rpob Gene ◽  
Tuberculosis Infection ◽  
Primate Species ◽  
Monitoring And Control ◽  
Mycobacterium Tuberculosis Infection ◽  
First Case

ABSTRACTTuberculosis, a chronic progressive disease, has been reported in bovine, swine, and primate species. Here, we report the first case of Mycobacterium tuberculosis infection in a Korean wild boar (Sus scrofa coreanus). The owners this domesticated boar brought it to the Gyeongbuk Veterinary Service Laboratory in Korea after it was found dead and severely emaciated. Demarcated yellowish white nodules were found around the larynx and retropharyngeal lymph node during necropsy. The lungs had diffuse fibrinous pleuritis, severe congestion, and scattered nodules. More nodules were found in the spleen. Tuberculosis is characterized by massive macrophage infiltration and central caseous necrosis; both characteristics were found in the lungs. Histopathologic examination revealed that the alveolar lumen had marked fibrosis and exudates. Examination of the fluid revealed extensive macrophage permeation. To confirm a Mycobacterium infection, PCR was performed using two primer sets specific to the rpoB gene of Mycobacterium; Mycobacterium was detected in the lungs and spleen. To identify the species of Mycobacterium, immunohistochemical evaluation was performed using antibodies against Mycobacterium tuberculosis and Mycobacterium bovis. The results revealed immunoreactivity against M. tuberculosis but not against M. bovis. The consumption of undercooked or raw meat from game animals may expose humans and other animals to sylvatic infection. Consequently, Koreans who ingest wild boar may be at risk of a tuberculosis infection. To reduce the risk of foodborne infection and maintain public health, continuous monitoring and control strategies are required.

scholarly journals Shaping Streamflow Using a Real-Time Stormwater Control Network

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2259 ◽  
Author(s):  
Abhiram Mullapudi ◽  
Matthew Bartos ◽  
Brandon Wong ◽  
Branko Kerkez
Keyword(s):  
Real Time ◽  
Control Strategies ◽  
Low Cost ◽  
Control Network ◽  
Monitoring And Control ◽  
Set Point ◽  
Smart Water ◽  
Decay Times ◽  
Retention Basins ◽  
Scale Control

“Smart” water systems are transforming the field of stormwater management by enabling real-time monitoring and control of previously static infrastructure. While the localized benefits of active control are well-established, the potential for system-scale control of watersheds is poorly understood. This study shows how a real-world smart stormwater system can be leveraged to shape streamflow within an urban watershed. Specifically, we coordinate releases from two internet-controlled stormwater basins to achieve desired control objectives downstream—such as maintaining the flow at a set-point, and generating interleaved waves. In the first part of the study, we describe the construction of the control network using a low-cost, open-source hardware stack and a cloud-based controller scheduling application. Next, we characterize the system’s control capabilities by determining the travel times, decay times, and magnitudes of various waves released from the upstream retention basins. With this characterization in hand, we use the system to generate two desired responses at a critical downstream junction. First, we generate a set-point hydrograph, in which flow is maintained at an approximately constant rate. Next, we generate a series of overlapping and interleaved waves using timed releases from both retention basins. We discuss how these control strategies can be used to stabilize flows, thereby mitigating streambed erosion and reducing contaminant loads into downstream waterbodies.

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