63,992 research outputs found
Predicting the energy output of wind farms based on weather data: important variables and their correlation
Pre-print available at: http://arxiv.org/abs/1109.1922Wind energy plays an increasing role in the supply of energy world wide. The energy output of a wind farm is highly dependent on the weather conditions present at its site. If the output can be predicted more accurately, energy suppliers can coordinate the collaborative production of different energy sources more efficiently to avoid costly overproduction. In this paper, we take a computer science perspective on energy prediction based on weather data and analyze the important parameters as well as their correlation on the energy output. To deal with the interaction of the different parameters, we use symbolic regression based on the genetic programming tool DataModeler. Our studies are carried out on publicly available weather and energy data for a wind farm in Australia. We report on the correlation of the different variables for the energy output. The model obtained for energy prediction gives a very reliable prediction of the energy output for newly supplied weather data. © 2012 Elsevier Ltd.Ekaterina Vladislavleva, Tobias Friedrich, Frank Neumann, Markus Wagne
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
Exploring Interpretable LSTM Neural Networks over Multi-Variable Data
For recurrent neural networks trained on time series with target and
exogenous variables, in addition to accurate prediction, it is also desired to
provide interpretable insights into the data. In this paper, we explore the
structure of LSTM recurrent neural networks to learn variable-wise hidden
states, with the aim to capture different dynamics in multi-variable time
series and distinguish the contribution of variables to the prediction. With
these variable-wise hidden states, a mixture attention mechanism is proposed to
model the generative process of the target. Then we develop associated training
methods to jointly learn network parameters, variable and temporal importance
w.r.t the prediction of the target variable. Extensive experiments on real
datasets demonstrate enhanced prediction performance by capturing the dynamics
of different variables. Meanwhile, we evaluate the interpretation results both
qualitatively and quantitatively. It exhibits the prospect as an end-to-end
framework for both forecasting and knowledge extraction over multi-variable
data.Comment: Accepted to International Conference on Machine Learning (ICML), 201
Statistical learning for wind power : a modeling and stability study towards forecasting
We focus on wind power modeling using machine learning techniques. We show on
real data provided by the wind energy company Ma{\"i}a Eolis, that parametric
models, even following closely the physical equation relating wind production
to wind speed are outperformed by intelligent learning algorithms. In
particular, the CART-Bagging algorithm gives very stable and promising results.
Besides, as a step towards forecast, we quantify the impact of using
deteriorated wind measures on the performances. We show also on this
application that the default methodology to select a subset of predictors
provided in the standard random forest package can be refined, especially when
there exists among the predictors one variable which has a major impact
Incorporating geostrophic wind information for improved space-time short-term wind speed forecasting
Accurate short-term wind speed forecasting is needed for the rapid
development and efficient operation of wind energy resources. This is, however,
a very challenging problem. Although on the large scale, the wind speed is
related to atmospheric pressure, temperature, and other meteorological
variables, no improvement in forecasting accuracy was found by incorporating
air pressure and temperature directly into an advanced space-time statistical
forecasting model, the trigonometric direction diurnal (TDD) model. This paper
proposes to incorporate the geostrophic wind as a new predictor in the TDD
model. The geostrophic wind captures the physical relationship between wind and
pressure through the observed approximate balance between the pressure gradient
force and the Coriolis acceleration due to the Earth's rotation. Based on our
numerical experiments with data from West Texas, our new method produces more
accurate forecasts than does the TDD model using air pressure and temperature
for 1- to 6-hour-ahead forecasts based on three different evaluation criteria.
Furthermore, forecasting errors can be further reduced by using moving average
hourly wind speeds to fit the diurnal pattern. For example, our new method
obtains between 13.9% and 22.4% overall mean absolute error reduction relative
to persistence in 2-hour-ahead forecasts, and between 5.3% and 8.2% reduction
relative to the best previous space-time methods in this setting.Comment: Published in at http://dx.doi.org/10.1214/14-AOAS756 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
“Dust in the wind...”, deep learning application to wind energy time series forecasting
To balance electricity production and demand, it is required to use different prediction techniques extensively. Renewable energy, due to its intermittency, increases the complexity and uncertainty of forecasting, and the resulting accuracy impacts all the different players acting around the electricity systems around the world like generators, distributors, retailers, or consumers. Wind forecasting can be done under two major approaches, using meteorological numerical prediction models or based on pure time series input. Deep learning is appearing as a new method that can be used for wind energy prediction. This work develops several deep learning architectures and shows their performance when applied to wind time series. The models have been tested with the most extensive wind dataset available, the National Renewable Laboratory Wind Toolkit, a dataset with 126,692 wind points in North America. The architectures designed are based on different approaches, Multi-Layer Perceptron Networks (MLP), Convolutional Networks (CNN), and Recurrent Networks (RNN). These deep learning architectures have been tested to obtain predictions in a 12-h ahead horizon, and the accuracy is measured with the coefficient of determination, the R² method. The application of the models to wind sites evenly distributed in the North America geography allows us to infer several conclusions on the relationships between methods, terrain, and forecasting complexity. The results show differences between the models and confirm the superior capabilities on the use of deep learning techniques for wind speed forecasting from wind time series data.Peer ReviewedPostprint (published version
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