Approximations and Simulation of the Optimal Change Interval for Roundabout

Author(s):  
Shujian Bu ◽  
Tong Zhang ◽  
Li Du
Keyword(s):  
2019 ◽  
Vol 11 (24) ◽  
pp. 6976
Author(s):  
Suwon Song ◽  
Chun Gun Park

Change-point regression models are often used to develop building energy baselines that can be used to predict energy use and determine energy savings during a given performance period. However, the reliability of building energy baselines can depend on how well the change-point model fits the data measured during the baseline period. This research proposes the use of segmented linear regression models with one or two change points for automatically driving best-fit building energy baseline models, along with an algorithm using a data-driven grid search to find the optimal change point(s) within a given data boundary for the proposed models. The algorithm was programmed and tested with actual measured data (e.g., daily gas and electricity use) for case-study buildings. Graphical and statistical analysis was also performed to validate its reliability within acceptable deviations of an overall coefficient of variation of the root mean squared error (i.e., CV(RMSE)) of 1%, as compared to the results derived from the ASHRAE Inverse Model Toolkit (IMT) that was developed as a public domain program to manually derive the change-point model with user specified parameters. Consequently, it is expected that the algorithm can be applied for automatically deriving best-fit building energy baseline models with optimal change point(s) from measured data.


2018 ◽  
Vol 193 ◽  
pp. 151-178 ◽  
Author(s):  
Hossein Keshavarz ◽  
Clayton Scott ◽  
XuanLong Nguyen

1994 ◽  
Vol 36 (3) ◽  
pp. 371-378 ◽  
Author(s):  
John A. Eccleston ◽  
Deborah J. Street
Keyword(s):  

2019 ◽  
Vol 11 (6) ◽  
pp. 168781401985648
Author(s):  
Sung-Min Hong ◽  
Sinje Lee ◽  
Jong-Wu Hyeon ◽  
Jung-Han Lee ◽  
Seunghun Lee ◽  
...  

An underwater glider is an autonomous underwater vehicle that is propelled by changes in volume. Due to this propulsion method, it is possible to make observations with these devices continuously for 30–60 days. Of the gliders’ physical properties, the volume change has the greatest influence on the cruising speed. The speed can be increased by increasing the volume change, but this also increases the energy consumption. Therefore, the change in buoyancy is very important for the operation of underwater gliders. Hence, it is necessary to optimize the change in buoyancy. In this study, we describe a technique for optimizing the design of underwater gliders intended to operate in the East Sea of Korea using a combined buoyancy engine and thruster propulsion system. First, we carried out a simulation study to optimize the volume change of the buoyancy engine based on the average flow velocity distribution, water temperature, and vertical salinity distribution in the East Sea. Then, we used our simulations to predict the optimal change in volume of the underwater glider. Finally, we discuss the advantages of operating with thrusters in special environments under specific water temperature, salinity distribution, and ocean current conditions.


2006 ◽  
Vol 2006 ◽  
pp. 1-9
Author(s):  
Henry Schellhorn

We consider two Gaussian measures. In the “initial” measure the state variable is Gaussian, with zero drift and time-varying volatility. In the “target measure” the state variable follows an Ornstein-Uhlenbeck process, with a free set of parameters, namely, the time-varying speed of mean reversion. We look for the speed of mean reversion that minimizes the variance of the Radon-Nikodym derivative of the target measure with respect to the initial measure under a constraint on the time integral of the variance of the state variable in the target measure. We show that the optimal speed of mean reversion follows a Riccati equation. This equation can be solved analytically when the volatility curve takes specific shapes. We discuss an application of this result to simulation, which we presented in an earlier article.


2014 ◽  
Vol 30 (3) ◽  
pp. 035014 ◽  
Author(s):  
Roland Griesmaier ◽  
Michael S Vogelius

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