10,412 research outputs found
Temporal Spatial Decomposition and Fusion Network for Time Series Forecasting
Feature engineering is required to obtain better results for time series
forecasting, and decomposition is a crucial one. One decomposition approach
often cannot be used for numerous forecasting tasks since the standard time
series decomposition lacks flexibility and robustness. Traditional feature
selection relies heavily on preexisting domain knowledge, has no generic
methodology, and requires a lot of labor. However, most time series prediction
models based on deep learning typically suffer from interpretability issue, so
the "black box" results lead to a lack of confidence. To deal with the above
issues forms the motivation of the thesis. In the paper we propose TSDFNet as a
neural network with self-decomposition mechanism and an attentive feature
fusion mechanism, It abandons feature engineering as a preprocessing convention
and creatively integrates it as an internal module with the deep model. The
self-decomposition mechanism empowers TSDFNet with extensible and adaptive
decomposition capabilities for any time series, users can choose their own
basis functions to decompose the sequence into temporal and generalized spatial
dimensions. Attentive feature fusion mechanism has the ability to capture the
importance of external variables and the causality with target variables. It
can automatically suppress the unimportant features while enhancing the
effective ones, so that users do not have to struggle with feature selection.
Moreover, TSDFNet is easy to look into the "black box" of the deep neural
network by feature visualization and analyze the prediction results. We
demonstrate performance improvements over existing widely accepted models on
more than a dozen datasets, and three experiments showcase the interpretability
of TSDFNet.Comment: 10 page
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
Power System Parameters Forecasting Using Hilbert-Huang Transform and Machine Learning
A novel hybrid data-driven approach is developed for forecasting power system
parameters with the goal of increasing the efficiency of short-term forecasting
studies for non-stationary time-series. The proposed approach is based on mode
decomposition and a feature analysis of initial retrospective data using the
Hilbert-Huang transform and machine learning algorithms. The random forests and
gradient boosting trees learning techniques were examined. The decision tree
techniques were used to rank the importance of variables employed in the
forecasting models. The Mean Decrease Gini index is employed as an impurity
function. The resulting hybrid forecasting models employ the radial basis
function neural network and support vector regression. Apart from introduction
and references the paper is organized as follows. The section 2 presents the
background and the review of several approaches for short-term forecasting of
power system parameters. In the third section a hybrid machine learning-based
algorithm using Hilbert-Huang transform is developed for short-term forecasting
of power system parameters. Fourth section describes the decision tree learning
algorithms used for the issue of variables importance. Finally in section six
the experimental results in the following electric power problems are
presented: active power flow forecasting, electricity price forecasting and for
the wind speed and direction forecasting
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