29,611 research outputs found
STAR: A Concise Deep Learning Framework for Citywide Human Mobility Prediction
Human mobility forecasting in a city is of utmost importance to
transportation and public safety, but with the process of urbanization and the
generation of big data, intensive computing and determination of mobility
pattern have become challenging. This study focuses on how to improve the
accuracy and efficiency of predicting citywide human mobility via a simpler
solution. A spatio-temporal mobility event prediction framework based on a
single fully-convolutional residual network (STAR) is proposed. STAR is a
highly simple, general and effective method for learning a single tensor
representing the mobility event. Residual learning is utilized for training the
deep network to derive the detailed result for scenarios of citywide
prediction. Extensive benchmark evaluation results on real-world data
demonstrate that STAR outperforms state-of-the-art approaches in single- and
multi-step prediction while utilizing fewer parameters and achieving higher
efficiency.Comment: Accepted by MDM 201
Limits on the Benefits of Energy Storage for Renewable Integration
The high variability of renewable energy resources presents significant
challenges to the operation of the electric power grid. Conventional generators
can be used to mitigate this variability but are costly to operate and produce
carbon emissions. Energy storage provides a more environmentally friendly
alternative, but is costly to deploy in large amounts. This paper studies the
limits on the benefits of energy storage to renewable energy: How effective is
storage at mitigating the adverse effects of renewable energy variability? How
much storage is needed? What are the optimal control policies for operating
storage? To provide answers to these questions, we first formulate the power
flow in a single-bus power system with storage as an infinite horizon
stochastic program. We find the optimal policies for arbitrary net renewable
generation process when the cost function is the average conventional
generation (environmental cost) and when it is the average loss of load
probability (reliability cost). We obtain more refined results by considering
the multi-timescale operation of the power system. We view the power flow in
each timescale as the superposition of a predicted (deterministic) component
and an prediction error (residual) component and formulate the residual power
flow problem as an infinite horizon dynamic program. Assuming that the net
generation prediction error is an IID process, we quantify the asymptotic
benefits of storage. With the additional assumption of Laplace distributed
prediction error, we obtain closed form expressions for the stationary
distribution of storage and conventional generation. Finally, we propose a
two-threshold policy that trades off conventional generation saving with loss
of load probability. We illustrate our results and corroborate the IID and
Laplace assumptions numerically using datasets from CAISO and NREL.Comment: 45 pages, 17 figure
Radial Band Structure of Electrons in Liquid Metals
The electronic band structure of a liquid metal was investigated by measuring
precisely the evolution of angle-resolved photoelectron spectra during the
melting of a Pb monolayer on a Si(111) surface. We found that the liquid
monolayer exhibits a free-electron-like band and it undergoes a coherent radial
scattering, imposed by the radial correlation of constituent atoms, to form a
characteristic secondary hole band. This unique double radial bands and their
gradual evolution during melting can be quantitatively reproduced, including
detailed spectral intensity profiles, with our radial scattering model based on
a theoretical prediction of 1962. Our result establishes the radial band
structure as a key concept for describing the nature of electrons in strongly
disordered states of matter.Comment: 4 pages, 4 figures, accepted to Physical Review Letter
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