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

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)

Short-term forecasting and uncertainty analysis of wind turbine power based on long short-term memory network and Gaussian mixture model

Wind power plays a leading role in the development of renewable energy. However, the random nature of wind turbine power and its associated uncertainty create challenges in dispatching this power effectively in the power system, which can result in unnecessary curtailment of the wind turbine power. Improving the accuracy of wind turbine power forecasting is an effective measure for resolving such problems. This study uses a deep learning network to forecast the wind turbine power based on a long short-term memory network (LSTM) algorithm and uses the Gaussian mixture model (GMM) to analyze the error distribution characteristics of short-term wind turbine power forecasting. The LSTM algorithm is used to forecast the power and uncertainties for three wind turbines within a wind farm. According to numerical weather prediction (NWP) data and historical power data for three turbines, the forecasting accuracy of the turbine with the largest number of training samples is the best of the three. For one of the turbines, the LSTM, radial basis function (RBF), wavelet, deep belief network (DBN), back propagation neural networks (BPNN), and Elman neural network (ELMAN) have been used to forecast the wind turbine power. This study compares the results and demonstrates that LSTM can greatly improve the forecasting accuracy. Moreover, this study obtains different confidence intervals for the three units according to the GMM, mixture density neural network (MDN), and relevance vector machine (RVM) model results. The LSTM method is shown to have higher accuracy and faster convergence than the other methods. However, the GMM method has better performance and evaluation than other methods and thus has practical application value for wind turbine power dispatching

A critical review of wind power forecasting methods - past, present and future

The largest obstacle that suppresses the increase of wind power penetration within the power grid is uncertainties and fluctuations in wind speeds. Therefore, accurate wind power forecasting is a challenging task, which can significantly impact the effective operation of power systems. Wind power forecasting is also vital for planning unit commitment, maintenance scheduling and profit maximisation of power traders. The current development of cost-effective operation and maintenance methods for modern wind turbines benefits from the advancement of effective and accurate wind power forecasting approaches. This paper systematically reviewed the state-of-the-art approaches of wind power forecasting with regard to physical, statistical (time series and artificial neural networks) and hybrid methods, including factors that affect accuracy and computational time in the predictive modelling efforts. Besides, this study provided a guideline for wind power forecasting process screening, allowing the wind turbine/farm operators to identify the most appropriate predictive methods based on time horizons, input features, computational time, error measurements, etc. More specifically, further recommendations for the research community of wind power forecasting were proposed based on reviewed literature

A hybridwind speed forecasting system based on a 'decomposition and ensemble' strategy and fuzzy time series

© 2017 by the authors. Licensee MDPI, Basel, Switzerland. Accurate and stable wind speed forecasting is of critical importance in the wind power industry and has measurable influence on power-system management and the stability of market economics. However, most traditional wind speed forecasting models require a large amount of historical data and face restrictions due to assumptions, such as normality postulates. Additionally, any data volatility leads to increased forecasting instability. Therefore, in this paper, a hybrid forecasting system, which combines the 'decomposition and ensemble' strategy and fuzzy time series forecasting algorithm, is proposed that comprises two modules-data pre-processing and forecasting. Moreover, the statistical model, artificial neural network, and Support Vector Regression model are employed to compare with the proposed hybrid system, which is proven to be very effective in forecasting wind speed data affected by noise and instability. The results of these comparisons demonstrate that the hybrid forecasting system can improve the forecasting accuracy and stability significantly, and supervised discretization methods outperform the unsupervised methods for fuzzy time series in most cases

Numerical weather prediction wind correction methods and its impact on computational fluid dynamics based wind power forecasting

Numerical weather prediction (NWP) of wind speed (WS) is an important input to wind power forecasting (WPF), which its accuracy will limit the WPF performance. This paper proposes three NWP correcting methods based on the multiple linear regression, a radial basis function neural network, and an Elman neural network. The proposed correction methods exhibit small sample learning and efficient computational ability. So, they are in favour of forecasting the performance of planned large-scale wind farms. To this end, a physical WPF model based on computational fluid dynamics is used to demonstrate the impact of improving the NWP WS data based forecasting. A certain wind farm located in China is selected as the case study, and the measured and NWP WS forecasts before and after correction are taken as inputs to the WPF model. Results show that all three correction methods improve the precision of the NWP WS forecasts, with the nonlinear correction models performing a little better than the linear one. Compared with the original NWP, the three corrected NWP WS have higher annual, single point, and short-term prediction accuracy. As expected, the accuracy of wind power forecasting will increase with the accuracy of the input NWP WS forecast. Moreover, the WS correction enhances the consistency of error variation trends between input WS and output wind power. The proposed WS correction methods greatly improve the accuracy of both original NWP WS and the WPF derived from them