5,800 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
Adaptive Local Iterative Filtering for Signal Decomposition and Instantaneous Frequency analysis
Time-frequency analysis for non-linear and non-stationary signals is
extraordinarily challenging. To capture features in these signals, it is
necessary for the analysis methods to be local, adaptive and stable. In recent
years, decomposition based analysis methods, such as the empirical mode
decomposition (EMD) technique pioneered by Huang et al., were developed by
different research groups. These methods decompose a signal into a finite
number of components on which the time-frequency analysis can be applied more
effectively.
In this paper we consider the iterative filters (IFs) approach as an
alternative to EMD. We provide sufficient conditions on the filters that ensure
the convergence of IFs applied to any signal. Then we propose a new
technique, the Adaptive Local Iterative Filtering (ALIF) method, which uses the
IFs strategy together with an adaptive and data driven filter length selection
to achieve the decomposition. Furthermore we design smooth filters with compact
support from solutions of Fokker-Planck equations (FP filters) that can be used
within both IFs and ALIF methods. These filters fulfill the derived sufficient
conditions for the convergence of the IFs algorithm. Numerical examples are
given to demonstrate the performance and stability of IFs and ALIF techniques
with FP filters. In addition, in order to have a complete and truly local
analysis toolbox for non-linear and non-stationary signals, we propose a new
definition for the instantaneous frequency which depends exclusively on local
properties of a signal
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