33,807 research outputs found
Wind energy forecasting with neural networks: a literature review
Renewable energy is intermittent by nature and to integrate this energy into the Grid while assuring safety and stability the accurate forecasting of there newable energy generation is critical. Wind Energy prediction is based on the ability to forecast wind. There are many methods for wind forecasting based on the statistical properties of the wind time series and in the integration of meteorological information, these methods are being used commercially around the world. But one family of new methods for wind power fore castingis surging based on Machine Learning Deep Learning techniques. This paper analyses the characteristics of the Wind Speed time series data and performs a literature review of recently published works of wind power forecasting using Machine Learning approaches (neural and deep learning networks), which have been published in the last few years.Peer ReviewedPostprint (published version
Modeling and forecasting electricity spot prices: A functional data perspective
Classical time series models have serious difficulties in modeling and
forecasting the enormous fluctuations of electricity spot prices. Markov regime
switch models belong to the most often used models in the electricity
literature. These models try to capture the fluctuations of electricity spot
prices by using different regimes, each with its own mean and covariance
structure. Usually one regime is dedicated to moderate prices and another is
dedicated to high prices. However, these models show poor performance and there
is no theoretical justification for this kind of classification. The merit
order model, the most important micro-economic pricing model for electricity
spot prices, however, suggests a continuum of mean levels with a functional
dependence on electricity demand. We propose a new statistical perspective on
modeling and forecasting electricity spot prices that accounts for the merit
order model. In a first step, the functional relation between electricity spot
prices and electricity demand is modeled by daily price-demand functions. In a
second step, we parameterize the series of daily price-demand functions using a
functional factor model. The power of this new perspective is demonstrated by a
forecast study that compares our functional factor model with two established
classical time series models as well as two alternative functional data models.Comment: Published in at http://dx.doi.org/10.1214/13-AOAS652 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Short and long-term wind turbine power output prediction
In the wind energy industry, it is of great importance to develop models that
accurately forecast the power output of a wind turbine, as such predictions are
used for wind farm location assessment or power pricing and bidding,
monitoring, and preventive maintenance. As a first step, and following the
guidelines of the existing literature, we use the supervisory control and data
acquisition (SCADA) data to model the wind turbine power curve (WTPC). We
explore various parametric and non-parametric approaches for the modeling of
the WTPC, such as parametric logistic functions, and non-parametric piecewise
linear, polynomial, or cubic spline interpolation functions. We demonstrate
that all aforementioned classes of models are rich enough (with respect to
their relative complexity) to accurately model the WTPC, as their mean squared
error (MSE) is close to the MSE lower bound calculated from the historical
data. We further enhance the accuracy of our proposed model, by incorporating
additional environmental factors that affect the power output, such as the
ambient temperature, and the wind direction. However, all aforementioned
models, when it comes to forecasting, seem to have an intrinsic limitation, due
to their inability to capture the inherent auto-correlation of the data. To
avoid this conundrum, we show that adding a properly scaled ARMA modeling layer
increases short-term prediction performance, while keeping the long-term
prediction capability of the model
“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
Quantifying the Influences on Probabilistic Wind Power Forecasts
In recent years, probabilistic forecasts techniques were proposed in research
as well as in applications to integrate volatile renewable energy resources
into the electrical grid. These techniques allow decision makers to take the
uncertainty of the prediction into account and, therefore, to devise optimal
decisions, e.g., related to costs and risks in the electrical grid. However, it
was yet not studied how the input, such as numerical weather predictions,
affects the model output of forecasting models in detail. Therefore, we examine
the potential influences with techniques from the field of sensitivity analysis
on three different black-box models to obtain insights into differences and
similarities of these probabilistic models. The analysis shows a considerable
number of potential influences in those models depending on, e.g., the
predicted probability and the type of model. These effects motivate the need to
take various influences into account when models are tested, analyzed, or
compared. Nevertheless, results of the sensitivity analysis will allow us to
select a model with advantages in the practical application.Comment: 5 pages; 1 table; 3 figures; This work has been submitted to the IEEE
for possible publication. Copyright may be transferred without notice, after
which this version may no longer be accessibl
The Challenge of Machine Learning in Space Weather Nowcasting and Forecasting
The numerous recent breakthroughs in machine learning (ML) make imperative to
carefully ponder how the scientific community can benefit from a technology
that, although not necessarily new, is today living its golden age. This Grand
Challenge review paper is focused on the present and future role of machine
learning in space weather. The purpose is twofold. On one hand, we will discuss
previous works that use ML for space weather forecasting, focusing in
particular on the few areas that have seen most activity: the forecasting of
geomagnetic indices, of relativistic electrons at geosynchronous orbits, of
solar flares occurrence, of coronal mass ejection propagation time, and of
solar wind speed. On the other hand, this paper serves as a gentle introduction
to the field of machine learning tailored to the space weather community and as
a pointer to a number of open challenges that we believe the community should
undertake in the next decade. The recurring themes throughout the review are
the need to shift our forecasting paradigm to a probabilistic approach focused
on the reliable assessment of uncertainties, and the combination of
physics-based and machine learning approaches, known as gray-box.Comment: under revie
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