A Hybrid Model Based on A Modified Optimization Algorithm and An Artificial Intelligence Algorithm for Short-Term Wind Speed Multi-Step Ahead Forecasting

Abstract

Abstract:In the last few years, researchers have paid increasing attention to improving the accuracy of wind speed forecasting because of its vital impact on power dispatching and grid security. However, it is difficult to achieve a good forecasting performance due to the randomness and intermittency characteristics of wind speed time series. Current forecasting models based on neural network theory could adapt to various types of time series data; however, these models ignore the importance of data pre-processing and model parameter optimization, which leads to poor forecasting accuracy. In this paper, a new hybrid model is developed for short-term multi-step wind speed forecasting, which includes four modules: (1) the data pre-processing module; (2) the optimization module; (3) the hybrid nonlinear forecasting module and (4) the evaluation module. In order to estimate the forecasting ability of the proposed hybrid model, 10 min wind speed data were applied in this paper as a case study. The experimental results in six real forecasting cases indicate that the proposed hybrid model can provide not only accurate but also stable performance in terms of multi-step wind speed forecasting can be considered an effective tool in planning and dispatching for smart grids.

Designing, Developing, and Implementing a Forecasting Method for the Produced and Consumed Electricity in the Case of Small Wind Farms Situated on Quite Complex Hilly Terrain

“…When compared to our approach, Yao et al [11] developed a hybrid model in order to obtain the wind speed forecasting for a short-term horizon, while our developed method targets a short-term and medium-term forecasting horizon of the meteorological parameters (the temperature, absolute direction of the wind, and average wind speed) using a developed long short-term artificial neural network approach with exogenous variables support that makes use of the parallel processing capabilities of the CUDA architecture and of both the produced and consumed electricity at the level of the whole production group of the wind farm. In the case of our developed method, the main challenge was to attain an accurate forecast in the context of complex hilly terrain.…”

“…Yao et al developed [11] a hybrid model combining wavelet denoising, a modified ant colony optimization algorithm, and Back Propagation neural networks. Yao et al stated that their method is useful in predicting the wind speed for a short-term forecasting horizon based on a multistep approach.…”

A New Hybrid Approach for Short-Term Electric Load Forecasting Applying Support Vector Machine with Ensemble Empirical Mode Decomposition and Whale Optimization

“…To obtain an accurate forecasting result for electric power stations, many short-term predicting methods were introduced, and those can mainly be classified into three categories: conventional methods, modern methods, and hybrid methods. Firstly, conventional methods include multi-linear regression analysis, time series, state space models, general exponential smoothing, and knowledge-based methods [3][4][5][6][7][8]. However, these methods cannot provide appropriate nonlinear exiting mathematical relationships to express actual electric loads.…”

Artificial Combined Model Based on Hybrid Nonlinear Neural Network Models and Statistics Linear Models—Research and Application for Wind Speed Forecasting

“…Artificial neural network [18][19][20][21][22][23][24][25][26][27][28] has strong learning and mapping ability and can easily fit the arbitrary complex nonlinear relationship, which is very suitable for short-term wind speed forecasting, and now research with neural networks is quite active in the world. Commonly, researchers forecast wind speed using back-propagation neural networks (BPNNs).…”