A Compound Wind Power Forecasting Strategy Based on Clustering, Two-Stage Decomposition, Parameter Optimization, and Optimal Combination of Multiple Machine Learning Approaches

Abstract

Given the large-scale exploitation and utilization of wind power, the problems caused by the high stochastic and random characteristics of wind speed make researchers develop more reliable and precise wind power forecasting (WPF) models. To obtain better predicting accuracy, this study proposes a novel compound WPF strategy by optimal integration of four base forecasting engines. In the forecasting process, density-based spatial clustering of applications with noise (DBSCAN) is firstly employed to identify meaningful information and discard the abnormal wind power data. To eliminate the adverse influence of the missing data on the forecasting accuracy, Lagrange interpolation method is developed to get the corrected values of the missing points. Then, the two-stage decomposition (TSD) method including ensemble empirical mode decomposition (EEMD) and wavelet transform (WT) is utilized to preprocess the wind power data. In the decomposition process, the empirical wind power data are disassembled into different intrinsic mode functions (IMFs) and one residual (Res) by EEMD, and the highest frequent time series IMF1 is further broken into different components by WT. After determination of the input matrix by a partial autocorrelation function (PACF) and normalization into [0, 1], these decomposed components are used as the input variables of all the base forecasting engines, including least square support vector machine (LSSVM), wavelet neural networks (WNN), extreme learning machine (ELM) and autoregressive integrated moving average (ARIMA), to make the multistep WPF. To avoid local optima and improve the forecasting performance, the parameters in LSSVM, ELM, and WNN are tuned by backtracking search algorithm (BSA). On this basis, BSA algorithm is also employed to optimize the weighted coefficients of the individual forecasting results that produced by the four base forecasting engines to generate an ensemble of the forecasts. In the end, case studies for a certain wind farm in China are carried out to assess the proposed forecasting strategy.

Machine Learning and Metaheuristic Methods for Renewable Power Forecasting: A Recent Review

“…This means that the advantageous computational speed of ELM was lost, and the balance between both speed and accuracy remains a trade-off decision that engineers need to consider when employing such hybridized models. Another BSA application was used by Sun et al (2019b) for tuning the parameters of different ML approaches for multistep wind power forecasting. The time series data were decomposed and fed to three ML forecasting engines (namely, ELM.…”

Forecasting of 10-Second Power Demand of Highly Variable Loads for Microgrid Operation Control

“…Many different methods (models) can be used to prepare short-term or very shortterm forecasts of wind power generation. Some examples are given as follows: the model of wavelet decomposition and weighted random forest optimized by the niche immune lion algorithm utilized for ultra-short-term wind power forecasts [15]; hybrid empirical mode decomposition and ensemble empirical mode decomposition models (for wind power forecasts) [16]; a neuro fuzzy system with grid partition, a neuro fuzzy system with subtractive clustering, a least square support vector regression, and a regression tree (in the forecasting of hourly wind power) [17]; different approaches for minute-scale forecasts of wind power [18]; the Takagi-Sugeno fuzzy model utilized for ultra-short-term forecasts of wind power [19]; the integration of clustering, two-stage decomposition, parameter optimization, and optimal combination of multiple machine learning approaches (for compound wind power forecasts) [20]; hybrid model using modified long short-term memory for short-term wind power prediction [21]; extreme gradient boosting model with weather similarity analysis and feature engineering (for short-term wind power forecasts) [22]; a model for wind power forecasts utilizing a deep learning approach [23]; hybrid model using data preprocessing strategy and improved extreme learning machine with kernel (for wind power forecasts) [24]; the use of dual-Doppler radar observations of wind speed and direction for five-minute forecasts of wind power [25]; the multi-stage intelligent algorithm which combines the Beveridge-Nelson decomposition approach, the least square support vector machine, and intelligent optimization approach called the Grasshopper Optimization Algorithm (for short-term wind power forecasts) [26]; hybrid model, which combines extreme-point symmetric mode decomposition, extreme learning machine, and PSO algorithm (for short-term wind forecasts) [27]; chicken swarm algorithm optimization support vector machine model for short-term forecasts of wind power [28]; hybrid neural network based on gated recurrent unit with uncertain factors (for ultra-shortterm forecasts of wind power) [29]; discrete time Markov chain models for very short-term wind power forecasts [30].…”

Wind power prediction based on wavelet denoising and improved slime mold algorithm optimized support vector machine

“…The combination prediction model can comprehensively use the statistical information of each single prediction model, establish the combination prediction model according to the technical characteristics and advantages of each model through the idea of complementary advantages, overcome the limitations of the single prediction model, and effectively reduce the probability of large errors. Typical combination prediction models include combination prediction model based on weight coefficient (An et al, 2021; Sun et al, 2019), combination prediction model combined with data preprocessing (Wang et al, 2019; Zhang et al, 2019a), combination prediction model based on model parameter optimization (Qin et al, 2021), etc. A large number of studies show that the prediction accuracy of the combination prediction model has been improved compared with the single prediction model.…”