Modifying Wind Speed Data Observed from Manual Observation System to Automatic Observation System Using Wavelet Neural Network

Recently, manual observation sequence has been gradually replaced by automatic observation sequence. The difference between manual observation sequence and automatic observation sequence is somewhat inevitable. This challenges the the homogeneity and the continuity of historical weather data, and influences atmospheric researches and applications. Therefore, based on the understanding of the influence caused by the two observation sequences, how to modify the data sequence of manual observation to automatic observation sequence has become a problem. In this paper, a model, which is a neural network based on the particle swarm optimization technique (PSONN), is established to modify the wind speed data sequence from manual observation to automatic observation. The proposed model achieves 15.6% in mean absolute percentage error (MAPE) compared to manual observation data sequence. For wind speed, it could be a promising candidate for modifying manual observing data sequence to automatic observing data sequence.

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Short-term wind speed forecasting based on wavelet neural network of phase space recon-struction

JOURNAL OF ELECTRONIC MEASUREMENT AND INSTRUMENT ◽

10.3724/sp.j.1187.2012.00236 ◽

2013 ◽

Vol 26 (3) ◽

pp. 236-241 ◽

Cited By ~ 1

Author(s):

Jian Qin ◽

Jianping Wang ◽

Chongwei Zhang

Keyword(s):

Neural Network ◽

Phase Space ◽

Wind Speed ◽

Wavelet Neural Network ◽

Short Term ◽

Wind Speed Forecasting

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Wind Speed Forecasting by Wavelet Neural Networks: A Comparative Study

Mathematical Problems in Engineering ◽

10.1155/2013/395815 ◽

2013 ◽

Vol 2013 ◽

pp. 1-7 ◽

Cited By ~ 7

Author(s):

Chuanan Yao ◽

Xiankun Gao ◽

Yongchang Yu

Keyword(s):

Neural Network ◽

Neural Networks ◽

Wind Speed ◽

Vital Role ◽

Wavelet Neural Network ◽

System Stability ◽

Wavelet Neural Networks ◽

Grid Security ◽

The Third ◽

Combined Models

Due to the environmental degradation and depletion of conventional energy, much attention has been devoted to wind energy in many countries. The intermittent nature of wind power has had a great impact on power grid security. Accurate forecasting of wind speed plays a vital role in power system stability. This paper presents a comparison of three wavelet neural networks for short-term forecasting of wind speed. The first two combined models are two types of basic combinations of wavelet transform and neural network, namely, compact wavelet neural network (CWNN) and loose wavelet neural network (LWNN) in this study, and the third model is a new hybrid method based on the CWNN and LWNN models. The efficiency of the combined models has been evaluated by using actual wind speed from two test stations in North China. The results show that the forecasting performances of the CWNN and LWNN models are unstable and are affected by the test stations selected; the third model is far more accurate than the other forecasting models in spite of the drawback of lower computational efficiency.

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Wind Speed Forecast for Wind Farms Based on Phase Space Reconstruction of Wavelet Neural Network

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.433-440.840 ◽

2012 ◽

Vol 433-440 ◽

pp. 840-845 ◽

Cited By ~ 1

Author(s):

Xiao Bing Xu ◽

Jun He ◽

Jian Ping Wang

Keyword(s):

Neural Network ◽

Neural Networks ◽

Phase Space ◽

Wind Speed ◽

Network Model ◽

Neural Network Model ◽

Phase Space Reconstruction ◽

Wavelet Neural Network ◽

Non Linear ◽

Wind Speed Forecast

Wind speed forecast is a non-linear and non-smooth problem. nonlinear and non-stationary are two kinds of mathematical problem, it is difficult to model with a single method, so that, a wavelet neural network model is set, the non-linear process of wind speed is forecast by neural networks and the non-stationary process of wind speed is decomposed into quasi-stationary at different frequency scales by multi-scale characteristics of wavelet transforms. wavelet combined with neural network model avoid the neural network model that can not handle non-stationary questions .while, the effect of indefinite inputs are removed by embedding dimension of phase space to determine neural networks inputs. The simulation results show that phase space reconstruction of wavelet neural network is more accuracy than the ordinary BP neural network. It could be well applied in wind speed forecasts.

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