Improved wind prediction based on the Lorenz system

Zhang, Yagang; Yang, Jingyun; Wang, Kangcheng; Wang, Zengping; Wang, Yinding

doi:10.1016/j.renene.2015.03.039

Cited by 23 publications

(12 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Changing the Rayleigh number of this equation, the Lorenz attractor will exhibit four different motion patterns. Reference [17] verifies the effectiveness and feasibility of Lorenzs atmospheric disturbance system in improving wind speed prediction accuracy. Reference [18] discusses the compensation effect on the preliminary wind speed forecast results using atmospheric disturbances with different initial values.…”

Section: Introductionmentioning

confidence: 77%

Wind speed prediction research with EMD-BP based on Lorenz disturbance

Zhang

Pan

Zhang

et al. 2019

Journal of Electrical Engineering

Self Cite

View full text Add to dashboard Cite

Wind power, as a new energy generation technology, has been applying widely and growing rapidly, which make it become the main force of renewable energy. However, wind speed sequence has its own character of the intermittent and uncertainty, which brings a great challenge to the safety and stability of the power grid, one of the valid ways solving the problem is improving the wind speed predicting accuracy. Therefore, given atmospheric disturbances, we firstly used empirical mode decomposition (EMD) to deal with the non-linear wind speed sequence, and combined with strong adaptive and self-learning ability of BP neural network, then, a wind speed prediction model, EMD-BP neural network based on Lorenz disturbance, was proposed. Finally, it was to made use of actual wind speed data to take a simulation experiment and explored the improvement effect of the preliminary forecasting sequence of wind speed influenced by Lorenz equation in the transient chaos and chaos. The results show that, the improved model weakened the random fluctuation of wind speed sequence, effectively corrected the wind speed sequences initial prediction values, and made a great improvement for the short-term wind speed prediction precision. This research work will help the power system dispatching department adjust the dispatching plan in time, formulate the wind farm control strategy reasonably, reduce the impact brought by wind power grid connection, increase the wind power penetration rate, and then promote the global energy power market innovation.

show abstract

Section: Introductionmentioning

confidence: 77%

Wind speed prediction research with EMD-BP based on Lorenz disturbance

Zhang

Pan

Zhang

et al. 2019

Journal of Electrical Engineering

Self Cite

View full text Add to dashboard Cite

show abstract

“…In order to verify the feasibility of this method, we use real wind speed data from Sotavento wind farm in Galicia, Spain to make modeling experiments. Galicia, located in northwest Spain, faces the Atlantic Ocean, which is a typical Mediterranean climate, hot and dry in summer, and mild and rainy in winter [26]. The wind speed distribution curve in summer and winter is shown in Fig.…”

Section: The Modelling Process Of Pca-ica-rbfmentioning

confidence: 99%

Wind speed prediction with RBF neural network based on PCA and ICA

Zhang

Zhao

et al. 2018

Journal of Electrical Engineering

Self Cite

View full text Add to dashboard Cite

Thanks to non-pollution and sustainability of wind energy, it has become the main source of power generation in the new era worldwide. However, the inherent random fluctuation and intermittency of wind power have negative effects on the safe and stable operation of power system and the quality of power. The key solving this problem is to improve the accuracy of wind speed prediction. In the paper, considering the forecasting accuracy is affected by many factors, we propose that, Principal Component Analysis (PCA) is combined with Independent Component Analysis (ICA) to process the sample, which can weaken the mutual interference between the various factors, extract accurately independent component reflected the characteristics of wind farm and achieve the purpose of improving the accuracy of wind speed prediction. At the same time, the adaptive and self-learning ability of neural network is more suitable for wind speed sequence prediction. The prediction results demonstrate that compared with the traditional neural network predicting model (RBF, BP, Elman), this model makes full use of the information provided by varieties of relevant factors, weakens the volatility of wind speed sequence and significantly enhances the short-term wind speed forecasting accuracy. The research work in the paper can help wind farm reasonably arrange the power dispatching plan, reduce the power operation cost and effectively boost the large-scale development and utilization of renewable energy.

show abstract

“…In general, the three months in a certain season tend to have similar weather conditions [29]. Thus, we separately choose one month from each season as their representative.…”

Section: The Combined Wind Speed Prediction Model Based On Emd (mentioning

confidence: 99%

Untitled

2018

Adv. Electr. Comp. Eng.

View full text Add to dashboard Cite

Improved wind prediction based on the Lorenz system

Cited by 23 publications

References 26 publications

Wind speed prediction research with EMD-BP based on Lorenz disturbance

Wind speed prediction research with EMD-BP based on Lorenz disturbance

Wind speed prediction with RBF neural network based on PCA and ICA

Untitled

Contact Info

Product

Resources

About