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

Evaluation of an offshore wind farm computational fluid dynamics model against operational site data

Modelling wind turbine wake effects at a range of wind speeds and directions with actuator disk (AD) models can provide insight but also be challenging. With any model it is important to quantify the level of error, but this can also present a challenge when comparing a steady-state model to measurement data with scatter. This paper models wind flow in a wind farm at a range of wind speeds and directions using an AD implementation. The results from these models are compared to data collected from the actual farm being modelled. An extensive comparison is conducted, constituted from 35 cases where two turbulence models, the standard k-ε and k-ω SST are evaluated. The steps taken in building the models as well as processes for comparing the AD computational fluid dynamics (CFD) results to real-world data using the regression models of ensemble bagging and Gaussian process are outlined. Turbine performance data and boundary conditions are determined using the site data. Modifications to an existing opensource AD code are shown so that the predetermined turbine performance can be implemented into the CFD model. Steady state solutions are obtained with the OpenFOAM CFD solver. Results are compared in terms of velocity deficit at the measurement locations. Using the standard k-ε model, a mean absolute error for all cases together of roughly 8% can be achieved, but this error changes for different directions and methods of evaluating it

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