Learning Dynamical Demand Response Model in Real-Time Pricing Program

Price responsiveness is a major feature of end use customers (EUCs) that participate in demand response (DR) programs, and has been conventionally modeled with static demand functions, which take the electricity price as the input and the aggregate energy consumption as the output. This, however, neglects the inherent temporal correlation of the EUC behaviors, and may result in large errors when predicting the actual responses of EUCs in real-time pricing (RTP) programs. In this paper, we propose a dynamical DR model so as to capture the temporal behavior of the EUCs. The states in the proposed dynamical DR model can be explicitly chosen, in which case the model can be represented by a linear function or a multi-layer feedforward neural network, or implicitly chosen, in which case the model can be represented by a recurrent neural network or a long short-term memory unit network. In both cases, the dynamical DR model can be learned from historical price and energy consumption data. Numerical simulation illustrated how the states are chosen and also showed the proposed dynamical DR model significantly outperforms the static ones.Comment: Accepted to IEEE ISGT NA 201

Simulation-Free Runway Balancing Optimization Under Uncertainty Using Neural Network

International audienceThis paper proposes a new optimization scheme using neural network for runway balancing to minimize departure and arrival aircraft delay. While other researchers have proposed solutions to the runway balancing problem using a simulation-based technique to calculate aircraft delay, the proposed method replaces the simulation by a neural network model-based estimation using the actual operational data, thus providing the following two advantages. First, accurate estimation of aircraft delay can improve the solution of the runway balancing problem. Second, the simulation process is not required in the optimization. Although it is difficult to develop an accurate simulation model especially under uncertain environment, the neural network model can estimate the average delay without explicitly modeling uncertainty. In this paper, as a first step, the effectiveness of the proposed method is validated through simulations. First, simulations considering uncertainty are used to generate the data, which are then used to train the neural network. The neural network predicts the delay under the current traffic and only this predicted delay is used for the runway balancing optimization with simulated annealing. The simulation result shows that the result by neural network outperforms the one by the simulation-based method under uncertainty. This means that the neural network can accurately estimate the delay under uncertainty environment, and is applicable in the optimization process

SUPERDARN CROSS POLAR CAP POTENTIAL AND PARAMETERS OF THE NEAR-EARTH SPACE

The Super Dual Auroral Radar Network (SuperDARN) of HF coherent radars routinely report the so-called cross polar cap potential (CPCP), a voltage applied by the solar wind and interplanetary magnetic field (IMF) onto the high-latitude ionosphere. The CPCP ultimately drives the global-scale plasma circulation and thus reflects the influence of the Sun on the near-Earth electrodynamic environment. In this Thesis, SuperDARN measurements of the CPCP collected over the year 2000 are investigated with a goal to statistically assess its relationship with various parameters of the solar wind and IMF and to compare found tendencies with expectations of several key theories/models predicting the CPCP. It is shown that SuperDARN measurements show smaller CPCPs when compared with theories/empirical models and show a smaller dependence on various parameters. Some reported tendencies, such as IMF Bz dependence, were found to be consistent with measurements by other instruments, as reported in the literature. In an attempt to clarify the reasons for discrepancies, SuperDARN CPCPs were compared with velocity measurements, acting as a proxy for the ionospheric electric field, from the Resolute Bay ionosonde, which was in operation within the central polar cap and was monitoring the flows contributing significantly to the CPCP. The expected linear relationship between SuperDARN CPCP and ionosonde velocities was confirmed. As a side issue, the Resolute Bay ionosonde velocities were compared with the velocities measured by the SuperDARN radars at Rankin Inlet and Inuvik over the area monitored by the ionosonde. Reasonable agreement was found between the instruments, which implies that ionosondes and SuperDARN are compatible

X-ray ejecta kinematics of the Galactic core-collapse supernova remnant G292.0+1.8

We report on the results from the analysis of our 114 ks Chandra HETGS observation of the Galactic core-collapse supernova remnant G292.0+1.8. To probe the 3D structure of the clumpy X-ray emitting ejecta material in this remnant, we measured Doppler shifts in emission lines from metal-rich ejecta knots projected at different radial distances from the expansion center. We estimate radial velocities of ejecta knots in the range of -2300 <~ v_r <~ 1400 km s^-1. The distribution of ejecta knots in velocity vs. projected-radius space suggests an expanding ejecta shell with a projected angular thickness of ~90" (corresponding to ~3 pc at d = 6 kpc). Based on this geometrical distribution of the ejecta knots, we estimate the location of the reverse shock approximately at the distance of ~4 pc from the center of the supernova remnant, putting it in close proximity to the outer boundary of the radio pulsar wind nebula. Based on our observed remnant dynamics and the standard explosion energy of 10^51 erg, we estimate the total ejecta mass to be <~ 8 M_sun, and we propose an upper limit of <~ 35 M_sun on the progenitor's mass.Comment: 5 figures, accepted by Ap

Aged B cells alter immune regulation of allografts in mice

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134428/1/eji3757-sup-0001-PRC.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/134428/2/eji3757_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/134428/3/eji3757.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/134428/4/eji3757-sup-0002-figure1-3.pd