1,896 research outputs found
Optimized Solar Photovoltaic Generation in a Real Local Distribution Network
Remarkable penetration of renewable energy in electric networks, despite its
valuable opportunities, such as power loss reduction and loadability
improvements, has raised concerns for system operators. Such huge penetration
can lead to a violation of the grid requirements, such as voltage and current
limits and reverse power flow. Optimal placement and sizing of Distributed
Generation (DG) are one of the best ways to strengthen the efficiency of the
power systems. This paper builds a simulation model for the local distribution
network based on obtained load profiles, GIS information, solar insolation,
feeder and voltage settings, and define the optimization problem of solar PVDG
installation to determine the optimal siting and sizing for different
penetration levels with different objective functions. The objective functions
include voltage profile improvement and energy loss minimization and the
considered constraints include the physical distribution network constraints
(AC power flow), the PV capacity constraint, and the voltage and reverse power
flow constraints.Comment: To be published (Accepted) in: Proceedings of the IEEE PES Innovative
Smart Grid Technologies Conference (ISGT), Washington D.C., USA, 201
Hierarchically Structured Reinforcement Learning for Topically Coherent Visual Story Generation
We propose a hierarchically structured reinforcement learning approach to
address the challenges of planning for generating coherent multi-sentence
stories for the visual storytelling task. Within our framework, the task of
generating a story given a sequence of images is divided across a two-level
hierarchical decoder. The high-level decoder constructs a plan by generating a
semantic concept (i.e., topic) for each image in sequence. The low-level
decoder generates a sentence for each image using a semantic compositional
network, which effectively grounds the sentence generation conditioned on the
topic. The two decoders are jointly trained end-to-end using reinforcement
learning. We evaluate our model on the visual storytelling (VIST) dataset.
Empirical results from both automatic and human evaluations demonstrate that
the proposed hierarchically structured reinforced training achieves
significantly better performance compared to a strong flat deep reinforcement
learning baseline.Comment: Accepted to AAAI 201
A novel Fireworks Algorithm with wind inertia dynamics and its application to traffic forecasting
Fireworks Algorithm (FWA) is a recently contributed heuristic optimization method that has shown a promising performance in applications stemming from different domains. Improvements to the original algorithm have been designed and tested in the related literature. Nonetheless, in most of such previous works FWA has been tested with standard test functions, hence its performance when applied to real application cases has been scarcely assessed. In this manuscript a mechanism for accelerating the convergence of this meta-heuristic is proposed based on observed wind inertia dynamics (WID) among fireworks in practice. The resulting enhanced algorithm will be described algorithmically and evaluated in terms of convergence speed by means of test functions. As an additional novel contribution of this work FWA and FWA-WID are used in a practical application where such heuristics are used as wrappers for optimizing the parameters of a road traffic short-term predictive model. The exhaustive performance analysis of the FWA and FWA-ID in this practical setup has revealed that the relatively high computational complexity of this solver with respect to other heuristics makes it critical to speed up their convergence (specially in cases with a costly fitness evaluation as the one tackled in this work), observation that buttresses the utility of the proposed modifications to the naive FWA solver
A Lite Fireworks Algorithm with Fractal Dimension Constraint for Feature Selection
As the use of robotics becomes more widespread, the huge amount of vision
data leads to a dramatic increase in data dimensionality. Although deep
learning methods can effectively process these high-dimensional vision data.
Due to the limitation of computational resources, some special scenarios still
rely on traditional machine learning methods. However, these high-dimensional
visual data lead to great challenges for traditional machine learning methods.
Therefore, we propose a Lite Fireworks Algorithm with Fractal Dimension
constraint for feature selection (LFWA+FD) and use it to solve the feature
selection problem driven by robot vision. The "LFWA+FD" focuses on searching
the ideal feature subset by simplifying the fireworks algorithm and
constraining the dimensionality of selected features by fractal dimensionality,
which in turn reduces the approximate features and reduces the noise in the
original data to improve the accuracy of the model. The comparative
experimental results of two publicly available datasets from UCI show that the
proposed method can effectively select a subset of features useful for model
inference and remove a large amount of noise noise present in the original data
to improve the performance.Comment: International Conference on Pharmaceutical Sciences 202
- …