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.
Highlights
The utilization of renewable energy is one of the world’s hot topics
Maatallah et al made a comparison between the Hammerstein model and an Autoregressive (HAR) approach with a classical Autoregressive Integrated Moving Average (ARIMA) model and a multi-layer perception Artificial Neural Network (ANN), and the results show that the HAR model is beneficial for wind speed forecasting [9]
The following conclusions can be drawn: the hybrid model proposed by Extreme-point symmetric mode decomposition (ESMD)-Particle swarm optimization (PSO)-Extreme Learning Machine (ELM) has the best prediction accuracy and the lowest prediction error between the actual value and predicted value
Summary
As an essential component of renewable energy, wind energy is an environmentally friendly clean energy and its development has been highly valued by all countries including China. Under the guidance and motivation of renewable energy law, the policy of energy-saving and emission-reduction in China, new energy power generation is burgeoning, especially wind power, and the account for the proportion of low valley load capacity has increased. It can seriously affect the quality of the grid power and the operation of the power system when the proportions of wind power in the network load more than a certain value. The most effective way to conquer the shortcomings is to forecast wind power, so that the electric power dispatching departments can ensure the safety of the power balance of the power grid according to the change in wind power
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