21,397 research outputs found
Wind Power Forecasting Methods Based on Deep Learning: A Survey
Accurate wind power forecasting in wind farm can effectively reduce the enormous impact on grid operation safety when high permeability intermittent power supply is connected to the power grid. Aiming to provide reference strategies for relevant researchers as well as practical applications, this paper attempts to provide the literature investigation and methods analysis of deep learning, enforcement learning and transfer learning in wind speed and wind power forecasting modeling. Usually, wind speed and wind power forecasting around a wind farm requires the calculation of the next moment of the definite state, which is usually achieved based on the state of the atmosphere that encompasses nearby atmospheric pressure, temperature, roughness, and obstacles. As an effective method of high-dimensional feature extraction, deep neural network can theoretically deal with arbitrary nonlinear transformation through proper structural design, such as adding noise to outputs, evolutionary learning used to optimize hidden layer weights, optimize the objective function so as to save information that can improve the output accuracy while filter out the irrelevant or less affected information for forecasting. The establishment of high-precision wind speed and wind power forecasting models is always a challenge due to the randomness, instantaneity and seasonal characteristics
Data-Driven Forecasting of High-Dimensional Chaotic Systems with Long Short-Term Memory Networks
We introduce a data-driven forecasting method for high-dimensional chaotic
systems using long short-term memory (LSTM) recurrent neural networks. The
proposed LSTM neural networks perform inference of high-dimensional dynamical
systems in their reduced order space and are shown to be an effective set of
nonlinear approximators of their attractor. We demonstrate the forecasting
performance of the LSTM and compare it with Gaussian processes (GPs) in time
series obtained from the Lorenz 96 system, the Kuramoto-Sivashinsky equation
and a prototype climate model. The LSTM networks outperform the GPs in
short-term forecasting accuracy in all applications considered. A hybrid
architecture, extending the LSTM with a mean stochastic model (MSM-LSTM), is
proposed to ensure convergence to the invariant measure. This novel hybrid
method is fully data-driven and extends the forecasting capabilities of LSTM
networks.Comment: 31 page
Forecasting bus passenger flows by using a clustering-based support vector regression approach
As a significant component of the intelligent transportation system, forecasting bus passenger
flows plays a key role in resource allocation, network planning, and frequency setting. However, it remains
challenging to recognize high fluctuations, nonlinearity, and periodicity of bus passenger flows due to
varied destinations and departure times. For this reason, a novel forecasting model named as affinity
propagation-based support vector regression (AP-SVR) is proposed based on clustering and nonlinear
simulation. For the addressed approach, a clustering algorithm is first used to generate clustering-based
intervals. A support vector regression (SVR) is then exploited to forecast the passenger flow for each
cluster, with the use of particle swarm optimization (PSO) for obtaining the optimized parameters. Finally,
the prediction results of the SVR are rearranged by chronological order rearrangement. The proposed model
is tested using real bus passenger data from a bus line over four months. Experimental results demonstrate
that the proposed model performs better than other peer models in terms of absolute percentage error and
mean absolute percentage error. It is recommended that the deterministic clustering technique with stable
cluster results (AP) can improve the forecasting performance significantly.info:eu-repo/semantics/publishedVersio
Kernel methods for detecting coherent structures in dynamical data
We illustrate relationships between classical kernel-based dimensionality
reduction techniques and eigendecompositions of empirical estimates of
reproducing kernel Hilbert space (RKHS) operators associated with dynamical
systems. In particular, we show that kernel canonical correlation analysis
(CCA) can be interpreted in terms of kernel transfer operators and that it can
be obtained by optimizing the variational approach for Markov processes (VAMP)
score. As a result, we show that coherent sets of particle trajectories can be
computed by kernel CCA. We demonstrate the efficiency of this approach with
several examples, namely the well-known Bickley jet, ocean drifter data, and a
molecular dynamics problem with a time-dependent potential. Finally, we propose
a straightforward generalization of dynamic mode decomposition (DMD) called
coherent mode decomposition (CMD). Our results provide a generic machine
learning approach to the computation of coherent sets with an objective score
that can be used for cross-validation and the comparison of different methods
Decomposition of Nonlinear Dynamical Systems Using Koopman Gramians
In this paper we propose a new Koopman operator approach to the decomposition
of nonlinear dynamical systems using Koopman Gramians. We introduce the notion
of an input-Koopman operator, and show how input-Koopman operators can be used
to cast a nonlinear system into the classical state-space form, and identify
conditions under which input and state observable functions are well separated.
We then extend an existing method of dynamic mode decomposition for learning
Koopman operators from data known as deep dynamic mode decomposition to systems
with controls or disturbances. We illustrate the accuracy of the method in
learning an input-state separable Koopman operator for an example system, even
when the underlying system exhibits mixed state-input terms. We next introduce
a nonlinear decomposition algorithm, based on Koopman Gramians, that maximizes
internal subsystem observability and disturbance rejection from unwanted noise
from other subsystems. We derive a relaxation based on Koopman Gramians and
multi-way partitioning for the resulting NP-hard decomposition problem. We
lastly illustrate the proposed algorithm with the swing dynamics for an IEEE
39-bus system.Comment: 8 pages, submitted to IEEE 2018 AC
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