Improved clustering and deep learning based short-term wind energy forecasting in large-scale wind farms

As a promising renewable solution for sustainable power generation worldwide, wind energy is receiving continuing attention from both industry and the academic community. However, the randomness and intermittency of wind energy will affect the stable operation and stability of the power system and further affect the economic benefits of the power grid. What makes the matter worse is the inevitable coupling between each pair of wind turbines in the large-scale wind farm. Besides, the resolution of prediction is severely limited by the spatial scale of wind farms. These problems bring great difficulties for the control and scheduling of wind farms. To this end, this paper proposes a novel wind speed prediction method for wind farms by borrowing some wisdom from machine learning methods. First, density peak clustering (DPC) is employed to separate the tremendous number of scattered wind turbines into a much significantly reduced number of groups, the wind turbines in each of which are treated as a unity. Based on the priority setting of each indicator in clustering, the data are preprocessed with different weightings. Principal component analysis is utilized to avoid DPC's poor clustering effects in case the dataset is high-dimensional. Finally, by considering simultaneous effects from historical and present data, long short-term memory based deep learning neural networks are trained and used to iteratively predict the potential of the wind energy in each unit for each time slot. The effectiveness of the proposed algorithm is verified by taking an in-service wind farm in China as an example.

[1]  Alessandro Laio,et al.  Clustering by fast search and find of density peaks , 2014, Science.

[2]  Jian Weng,et al.  A Two-Layer Nonlinear Combination Method for Short-Term Wind Speed Prediction Based on ELM, ENN, and LSTM , 2019, IEEE Internet of Things Journal.

[3]  Jizhen Liu,et al.  A dynamic clustering model of wind farm based on the operation data , 2015 .

[4]  Jun Liang,et al.  Constraint-based clustering by fast search and find of density peaks , 2019, Neurocomputing.

[5]  Andrew Kusiak,et al.  Short-term prediction of wind power with a clustering approach , 2010 .

[6]  Li Fei,et al.  Multi-step wind speed prediction based on turbulence intensity and hybrid deep neural networks , 2019, Energy Conversion and Management.

[7]  Carlos Gershenson,et al.  Wind speed forecasting for wind farms: A method based on support vector regression , 2016 .

[8]  Hongjie Jia,et al.  Study on density peaks clustering based on k-nearest neighbors and principal component analysis , 2016, Knowl. Based Syst..

[9]  Wang Jilong,et al.  Short-term wind speed forecasting based on spectral clustering and optimised echo state networks , 2015 .

[10]  Kai Zhang,et al.  A composite framework coupling multiple feature selection, compound prediction models and novel hybrid swarm optimizer-based synchronization optimization strategy for multi-step ahead short-term wind speed forecasting , 2020 .

[11]  Tong Niu,et al.  A Novel System for Wind Speed Forecasting Based on Multi-Objective Optimization and Echo State Network , 2019, Sustainability.

[12]  Ali Lahouar,et al.  Hour-ahead wind power forecast based on random forests , 2017 .

[13]  Peng Ding,et al.  Optimal Operation for Economic and Exergetic Objectives of a Multiple Energy Carrier System Considering Demand Response Program , 2019 .

[14]  A. Khosravi,et al.  Time-series prediction of wind speed using machine learning algorithms: A case study Osorio wind farm, Brazil , 2018 .

[15]  Jin Wang,et al.  Improving WRF model turbine-height wind-speed forecasting using a surrogate- based automatic optimization method , 2019, Atmospheric Research.

[16]  Yingtao Jiang,et al.  A novel deep learning approach for short-term wind power forecasting based on infinite feature selection and recurrent neural network , 2018, Journal of Renewable and Sustainable Energy.

[17]  Yongqian Liu,et al.  Numerical weather prediction wind correction methods and its impact on computational fluid dynamics based wind power forecasting , 2016 .

[18]  Paul Geladi,et al.  Principal Component Analysis , 1987, Comprehensive Chemometrics.

[19]  R. B. Cal,et al.  Data-driven modeling of the wake behind a wind turbine array , 2020 .

[20]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[21]  Yongqian Liu,et al.  Clustering methods of wind turbines and its application in short-term wind power forecasts , 2014 .

[22]  Osamah Basheer Shukur,et al.  Daily wind speed forecasting through hybrid KF-ANN model based on ARIMA , 2015 .

[23]  Ningning Liu,et al.  A novel composite electricity demand forecasting framework by data processing and optimized support vector machine , 2020 .

[24]  Francois Vallee,et al.  Sharing wind power forecasts in electricity markets: A numerical analysis , 2016 .

[25]  Qinghua Hu,et al.  Transfer learning for short-term wind speed prediction with deep neural networks , 2016 .

[26]  Haiyan Lu,et al.  A new hybrid model optimized by an intelligent optimization algorithm for wind speed forecasting , 2014 .

[27]  Raúl Bayoán Cal,et al.  Identification of Markov process within a wind turbine array boundary layer , 2014 .

[28]  Yi-Ming Wei,et al.  An adaptive hybrid model for short term wind speed forecasting , 2020 .

[29]  Haojun Tang,et al.  A novel framework for wind speed prediction based on recurrent neural networks and support vector machine , 2018, Energy Conversion and Management.

[30]  Hui Liu,et al.  Wind speed forecasting approach using secondary decomposition algorithm and Elman neural networks , 2015 .

[31]  Andrzej M. Trzynadlowski,et al.  Wind speed and wind direction forecasting using echo state network with nonlinear functions , 2019, Renewable Energy.

[32]  Matthias Wachter,et al.  Stochastic modeling and performance monitoring of wind farm power production , 2015, 1509.03061.

[33]  Wenhui Shi,et al.  Dynamic clustering equivalent model of wind turbines based on spanning tree , 2015 .

[34]  Ricardo Nicolau Nassar Koury,et al.  Prediction of wind speed and wind direction using artificial neural network, support vector regression and adaptive neuro-fuzzy inference system , 2018 .

[35]  Jie Zhang,et al.  A clustering-based scenario generation framework for power market simulation with wind integration , 2020 .

[36]  Lifang Zhang,et al.  A combined forecasting model for time series: Application to short-term wind speed forecasting , 2020 .

[37]  Kwang Y. Lee,et al.  Data-driven oxygen excess ratio control for proton exchange membrane fuel cell , 2018, Applied Energy.

[38]  Yuan Zhao,et al.  A new prediction method based on VMD-PRBF-ARMA-E model considering wind speed characteristic , 2020 .

[39]  Li Li,et al.  Short-Term Wind Power Forecasting Based on Clustering Pre-Calculated CFD Method , 2018 .

[40]  Li Li,et al.  Mid-to-long term wind and photovoltaic power generation prediction based on copula function and long short term memory network , 2019, Applied Energy.

[41]  Yanfei Li,et al.  Smart multi-step deep learning model for wind speed forecasting based on variational mode decomposition, singular spectrum analysis, LSTM network and ELM , 2018 .

[42]  Jay Lee,et al.  A combined filtering strategy for short term and long term wind speed prediction with improved accuracy , 2019, Renewable Energy.

[43]  Li Yongle,et al.  Ultra-short term wind prediction with wavelet transform, deep belief network and ensemble learning , 2020 .

[44]  Lei Wu,et al.  Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method , 2016 .

[45]  Chen Jie,et al.  Wind speed forecasting using nonlinear-learning ensemble of deep learning time series prediction and extremal optimization , 2018, Energy Conversion and Management.

[46]  T. Saaty.  How to Make a Decision: The Analytic Hierarchy Process , 1990 .

[47]  Xin Yang,et al.  Application of hybrid model based on double decomposition, error correction and deep learning in short-term wind speed prediction , 2020 .

[48]  Xin Li,et al.  Short-term wind speed prediction using an extreme learning machine model with error correction , 2018 .

[49]  Jie Li,et al.  Wind speed prediction method using Shared Weight Long Short-Term Memory Network and Gaussian Process Regression , 2019, Applied Energy.

[50]  Zhihao Shang,et al.  A novel combined model based on echo state network for multi-step ahead wind speed forecasting: A case study of NREL , 2019, Energy Conversion and Management.