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

Pan

Journal of Electrical Engineering

et al. 2019

Self 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.

Section: T H E F O R M a T I O N O F T H E L O R E N Z C O M P R E H mentioning

confidence: 99%

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

Pan

Journal of Electrical Engineering

et al. 2019

Self Cite

“…The relevance of the problem of efficient energy supply of objects from renewable sources of wind and solar energy is confirmed by the fact that individual territories have considerable uneven supply of this energy in different seasons of the calendar year, as well as with respect to their individual species [1][2][3][4][5][6][7][8][9][10][11]. In addition, the creation of energy systems for renewable energy sources and wind to meet consumer needs is a pressing issue, given the recent trends in fossil fuel prices.…”

Section: Introductionmentioning

confidence: 99%

“…Conducted scientific studies by Ukrainian and foreign scientists have made it possible to solve many problems of efficient use of non-traditional and renewable energy sources [5][6][7][8][9]. Among these are studies related to the creation of efficient energy systems based on the use of renewable wind and solar energy, operating in deterministic or stochastic conditions [10][11]. At the same time, it has been proved that there is a need to substantiate mathematical models of the changing potential of wind and solar energy in different regions of the world [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Research of the variable natural potential of the wind and energy energy in the northern strip of the ukrainian carpathians

et al. 2020

The expediency of studying the varying potential of wind and solar energy in the northern strip of the Ukrainian Carpathians is substantiated. The technique of conducting experiments to determine the natural potential of wind and solar energy is proposed and a model for determining the relative volumes of electricity generation by wind farms and solar electrical installations is developed. Based on the conducted research, according to the Drohobych meteorological station, an energy assessment of wind and solar energy resources in the northern strip of the Ukrainian Carpathians was performed. It is substantiated that the theoretical curves of distribution of wind velocity in separate quarters of the year in the territory of the northern strip of the Ukrainian Carpathians are described by the Weibull distributions. Based on the calculations, it is established that the highest total daily solar radiation is in the summer months. Daily graphs of the relative generation of electricity by wind farms and solar electric installations have been constructed. The expediency of formation of combined systems of energy supply of objects from wind and solar energy in the northern strip of the Ukrainian Carpathians has been established. The substantiated patterns of change in the natural potential of wind and solar energy, as well as the graphs of the relative generation of electricity from them, will significantly speed up managerial decisions and improve their quality.

“…The combination of PCA and independent component analysis (ICA) is used to reduce mutual interference among different parameters and retrieve the independent components. Further, these components are merged with the RBF network to predict the actual characteristics of wind farms and to improve accuracy [34].…”

Section: Introductionmentioning

confidence: 99%

A New Hybrid Approach to Forecast Wind Power for Large Scale Wind Turbine Data Using Deep Learning with TensorFlow Framework and Principal Component Analysis

2019

Wind power forecasting plays a vital role in renewable energy production. Accurately forecasting wind energy is a significant challenge due to the uncertain and complex behavior of wind signals. For this purpose, accurate prediction methods are required. This paper presents a new hybrid approach of principal component analysis (PCA) and deep learning to uncover the hidden patterns from wind data and to forecast accurate wind power. PCA is applied to wind data to extract the hidden features from wind data and to identify meaningful information. It is also used to remove high correlation among the values. Further, an optimized deep learning algorithm with a TensorFlow framework is used to accurately forecast wind power from significant features. Finally, the deep learning algorithm is fine-tuned with learning error rate, optimizer function, dropout layer, activation and loss function. The algorithm uses a neural network and intelligent algorithm to predict the wind signals. The proposed idea is applied to three different datasets (hourly, monthly, yearly) gathered from the National Renewable Energy Laboratory (NREL) transforming energy database. The forecasting results show that the proposed research can accurately predict wind power using a span ranging from hours to years. A comparison is made with popular state of the art algorithms and it is demonstrated that the proposed research yields better predictions results.