Short-Term Wind Power Forecasting: A New Hybrid Model Combined Extreme-Point Symmetric Mode Decomposition, Extreme Learning Machine and Particle Swarm Optimization

Abstract

The nonlinear and non-stationary nature of wind power creates a difficult challenge for the stable operation of the power system when it accesses the grid. Improving the prediction accuracy of short-term wind power is beneficial to the power system dispatching department in formulating a power generation plan, reducing the rotation reserve capacity and improving the safety and reliability of the power grid operation. This paper has constructed a new hybrid model, named the ESMD-PSO-ELM model, which combines Extreme-point symmetric mode decomposition (ESMD), Extreme Learning Machine (ELM) and Particle swarm optimization (PSO). Firstly, the ESMD is applied to decompose wind power into several intrinsic mode functions (IMFs) and one residual(R). Then, the PSO-ELM is applied to predict each IMF and R. Finally, the predicted values of these components are assembled into the final forecast value compared with the original wind power. To verify the predictive performance of the proposed model, this paper selects actual wind power data from 1 April 2016 to 30 April 2016 with a total of 2880 observation values located in Yunnan, China for the experimental sample. The MAPE, NMAE and NRMSE values of the proposed model are 4.76, 2.23 and 2.70, respectively, and these values are lower than those of the other eight models. The empirical study demonstrates that the proposed model is more robust and accurate in forecasting short-term wind power compared with the other eight models.

Numerical Modeling Of Pressure Source Of Sinabung Volcano Based On GPS Data In 2011-2012 Using Particle Swarm Optimization (PS0)

“…We use Particle Swarm Optimization (PSO) to characterized the pressure source parameters of Sinabung volcano. Particle Swarm Optimization (PSO) is an optimization algorithm that mimics the processes in the life survival of a flock of bird and a school of fish developed by James Kennedy and Russell Eberhart in 1995 (Martinez, 2010., Zhou et al, 2018). In PSO, the population is assumed to be a particle with certain size and located at a random location in a multidimensional space.…”

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].…”

Modeling and Analysis Framework for Investigating the Impact of Dust and Temperature on PV Systems’ Performance and Optimum Cleaning Frequency

“…Based on the performance parameters, two models are developed to predict the system output behavior employing optimized artificial neural network (ANN) and optimized extreme learning machine (ELM) models [47]. It is worth mentioning that ANN and ELM have been widely developed and applied for different applications; for instance, ANN and ELM models are used in the fields of sustainability [48] speech recognition [49] medical [50] and load monitoring [51] environment [52] and hardware [53] respectively. The two models considered the dust accumulation and the ambient temperature as the independent variables and PV conversion efficiency as the dependent variable.…”