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

An analysis-forecast system for uncertainty modeling of wind speed: A case study of large-scale wind farms

© 2017 Elsevier Ltd The uncertainty analysis and modeling of wind speed, which has an essential influence on wind power systems, is consistently considered a challenging task. However, most investigations thus far were focused mainly on point forecasts, which in reality cannot facilitate quantitative characterization of the endogenous uncertainty involved. An analysis-forecast system that includes an analysis module and a forecast module and can provide appropriate scenarios for the dispatching and scheduling of a power system is devised in this study; this system superior to those presented in previous studies. In order to qualitatively and quantitatively investigate the uncertainty of wind speed, recurrence analysis techniques are effectively developed for application in the analysis module. Furthermore, in order to quantify the uncertainty accurately, a novel architecture aimed at uncertainty mining is devised for the forecast module, where a non-parametric model optimized by an improved multi-objective water cycle algorithm is considered a predictor for producing intervals for each mode component after feature selection. The results of extensive in-depth experiments show that the devised system is not only superior to the considered benchmark models, but also has good potential practical applications in wind power systems

Big Data Analysis application in the renewable energy market: wind power

Entre as enerxías renovables, a enerxía eólica e unha das tecnoloxías mundiais de rápido crecemento. Non obstante, esta incerteza debería minimizarse para programar e xestionar mellor os activos de xeración tradicionais para compensar a falta de electricidade nas redes electricas. A aparición de técnicas baseadas en datos ou aprendizaxe automática deu a capacidade de proporcionar predicións espaciais e temporais de alta resolución da velocidade e potencia do vento. Neste traballo desenvólvense tres modelos diferentes de ANN, abordando tres grandes problemas na predición de series de datos con esta técnica: garantía de calidade de datos e imputación de datos non válidos, asignación de hiperparámetros e selección de funcións. Os modelos desenvolvidos baséanse en técnicas de agrupación, optimización e procesamento de sinais para proporcionar predicións de velocidade e potencia do vento a curto e medio prazo (de minutos a horas)

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

A review on Day-Ahead Solar Energy Prediction

Accurate day-ahead prediction of solar energy plays a vital role in the planning of supply and demand in a power grid system. The previous study shows predictions based on weather forecasts composed of numerical text data. They can reflect temporal factors therefore the data versus the result might not always give the most accurate and precise results. That is why incorporating different methods and techniques which enhance accuracy is an important topic. An in-depth review of current deep learning-based forecasting models for renewable energy is provided in this paper

Forecasting wind speeds at tall tower heights within Missouri

Forecasting of wind speeds is necessary for the planning and operations of the wind power generating plants. This research investigates the short term forecasting of wind speeds at tall tower heights for stations within Missouri: Columbia, Neosho and Blanchard. The first objective was to characterize the chaotic nature of this parameter using mono and multi fractal analysis using the Rescale Range Analysis (R/S Analysis) and the Multifractal Detrended Fluctuation Analysis respectively (MF-DFA). It was determined that the system was fractal and there were no trends indicative of increasing fractality and complexity with increasing height. The second objective was the qualitative and quantitative chaotic characterization of the wind speeds using phase-space portraits and the Largest Lyapunov Exponent (LLE) respectively. The methods confirm the results of the fractal analyses. A simple non-linear prediction algorithm, Empirical Dynamical Modeling (EDM) was then used to forecast the wind speeds using a moving window. It was determined that the EDM was comparable to persistence. It beats this benchmark model in the very short term range of one time step or 10 minutes. The third objective was to cluster the data using Self-Organizing Maps (SOMs), having identified the optimum number of clusters as 4 using the Elbow and Silhouette Methods, among others. Three continuous intervals belonging to a particular cluster, which represented approximately 50 percent and over of the input vectors or rows from the data frame were identified. These intervals were then used as inputs into a Long Short-Term Memory Network (LSTM) with variables, pressure and wind speeds, as well as a lagged series LSTM with embedding dimension, d, and time delay (tau). These were compared to the Moving window Auto Regressive Integrated Moving Average (ARIMA) and to persistence. It was determined that the lagged series LSTM improved on the LSTM with wind speed and pressure series inputs, and all models beat persistence. The lagged LSTM beats the Moving ARIMA for at least 2 of the forecasting times of 60 and 120 minutes for all intervals.Includes bibliographical references

Wind generation forecasting methods and proliferation of artificial neural network:A review of five years research trend

To sustain a clean environment by reducing fossil fuels-based energies and increasing the integration of renewable-based energy sources, i.e., wind and solar power, have become the national policy for many countries. The increasing demand for renewable energy sources, such as wind, has created interest in the economic and technical issues related to the integration into the power grids. Having an intermittent nature and wind generation forecasting is a crucial aspect of ensuring the optimum grid control and design in power plants. Accurate forecasting provides essential information to empower grid operators and system designers in generating an optimal wind power plant, and to balance the power supply and demand. In this paper, we present an extensive review of wind forecasting methods and the artificial neural network (ANN) prolific in this regard. The instrument used to measure wind assimilation is analyzed and discussed, accurately, in studies that were published from May 1st, 2014 to May 1st, 2018. The results of the review demonstrate the increased application of ANN into wind power generation forecasting. Considering the component limitation of other systems, the trend of deploying the ANN and its hybrid systems are more attractive than other individual methods. The review further revealed that high forecasting accuracy could be achieved through proper handling and calibration of the wind-forecasting instrument and method