Short-term wind power prediction based on Hybrid Neural Network and chaotic shark smell optimization

Abstract

By the quick growth of wind power generation in the world, this clean energy becomes an important green electrical source in many countries. However, volatile and non-dispatchable nature of this energy source motivates researchers to find accurate and robust methods to predict its future values. Because of nonlinear and complex behaviors of this signal, more efficient wind power forecast methods are still demanded. In this paper, a new forecasting engine based on Neural Network (NN) and a novel Chaotic Shark Smell Optimization (CSSO) algorithm is proposed. Choosing optimal number of nodes for the hidden layer can enhance the efficiency of the NN's training performance. Accordingly, a new meta-heuristic algorithm is presented in this paper, which is based on shark abilities in nature, for optimizing the number of hidden nodes pertaining to the NN. Effectiveness of the proposed forecasting strategy is tested on two real-world case studies for predicting wind power. The obtained results demonstrate the capability of the proposed technique to cope with the variability and intermittency of wind power time series for providing accurate predictions of its future values.

The price prediction for the energy market based on a new method

“…Besides, one has to bear in mind that different kinds of non-stationarities may exist. In order to tackle the problem of non-stationarity, wavelets have been utilised because they can produce a good local representation of the signal in both time and frequency domains (Abedinia & Amjady, 2015). For instance, in Conejo, Plazas, Espinola, and Molina (2005), the original price series has been decomposed into four components by the wavelet transform and each sub-series has been separately predicted by the ARIMA time series.…”

“…The scaling and translation parameters are functions of the integer variables m and n (a = 2 m and b = n.2 m ); t is the discrete time index. Actually, the Mallat strategy is used to implement DWT using filters (Abedinia & Amjady, 2015;Ghadimi, 2015;Mandal et al, 2007;Pedregal & Trapero, 2007;Zhang & Luh, 2005) which has two stages: decomposition and reconstruction. Figure 1 shows the wavelet decomposition and reconstruction process.…”

“…According to the fact that this algorithm is not the novelty of this paper, it was proposed (Mohammadi & Ghadimi, 2015) for readers who want to have further information of this algorithm. To effectively train each NN of CNN by the modified Particle swarm optimization (PSO), its generalisation capability should be carefully monitored along the training process to avoid overfitting, which is a serious problem for NN training (Abedinia & Amjady, 2015;Zhang & Luh, 2005). Generalisation is a measure of how well the NN performs on the actual problem once training is complete (Abedinia & Amjady, 2015).…”

“…To effectively train each NN of CNN by the modified Particle swarm optimization (PSO), its generalisation capability should be carefully monitored along the training process to avoid overfitting, which is a serious problem for NN training (Abedinia & Amjady, 2015;Zhang & Luh, 2005). Generalisation is a measure of how well the NN performs on the actual problem once training is complete (Abedinia & Amjady, 2015). When overfitting occurs in the training phase, the NN training error continues to decrease and it seems that the training process progresses, while indeed the generalisation capability of the NN degrades and it loses its prediction ability for unseen forecast samples.…”

Multi-Objective Shark Smell Optimization Algorithm Using Incorporated Composite Angle Cosine for Automatic Train Operation

“…A new model for multiyear expansion planning of distribution networks (MEPDN) is proposed, and, to solve the above MEPDN model optimization problem, a new evolutionary algorithm-based solution method called Binary Chaotic Shark Smell Optimization (BCSSO) is presented [16]. A novel forecasting algorithm based on neural network (NN) and a novel chaotic shark smell optimization (CSSO) algorithm are proposed [17].Meanwhile, the driving experience for automatic train operation (ATO) target velocity trajectory optimization should not be ignored. A considerable number of researchers are interested in researching the affect of driving experience for automatic train operation (ATO) optimization, such as in [3,4,9,10], etc.…”