Multi-step ahead wind speed forecasting using an improved wavelet neural network combining variational mode decomposition and phase space reconstruction

Kho

Journal of Advanced Transportation

2019

Predicting travel speeds on urban road networks is a challenging subject due to its uncertainty stemming from travel demand, geometric condition, tra c signals, and other exogenous factors. is uncertainty appears as nonlinearity, nonstationarity, and volatility in tra c data, and it also creates a spatiotemporal heterogeneity of link travel speed by interacting with neighbor links. In this study, we propose a hybrid model using variational mode decomposition (VMD) to investigate and mitigate the uncertainty of urban travel speeds. e VMD allows the travel speed data to be divided into orthogonal and oscillatory sub-signals, called modes. e regular components are extracted as the low-frequency modes, and the irregular components presenting uncertainty are transformed into a combination of modes, which is more predictable than the original uncertainty. For the prediction, the VMD decomposes the travel speed data into modes, and these modes are predicted and summed to represent the predicted travel speed. e evaluation results on urban road networks show that, the proposed hybrid model outperforms the benchmark models both in the congested and in the overall conditions. e improvement in performance increases signi cantly over speci c link-days, which generally are hard to predict. To explain the signi cant variance of the prediction performance according to each link and each day, the correlation analysis between the properties of modes and the performance of the model are conducted. e results on correlation analysis show that the more variance of nondaily pattern is explained through the modes, the easier it was to predict the speed. Based on the results, discussions on the interpretation on the correlation analysis and future research are presented.

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Hybrid Approach Based on Variational Mode Decomposition for Analyzing and Predicting Urban Travel Speed

Kho

Journal of Advanced Transportation

2019

“…Energies 2017, 10,1976 11 of 26 at the input of the MLP and does that repetition automatically 20 times. In this step, the ARIMA + NN1 speed prediction is made and all values are analyzed based on errors, standard deviation and linear correlation.…”

Section: Arima + Nn1 + Nn2 Modelmentioning

confidence: 99%

“…According to [9], wind power temporal series always have non-linear and non-stationary characteristics and therefore it is very difficult to accurately forecast the power generated. In [10], it is established that accurate wind forecasting is decisive to have a reliable power system. However, the intermittent and unstable nature of the wind speed makes it very difficult to predict accurately.…”

Section: Introductionmentioning

confidence: 99%

Different Models for Forecasting Wind Power Generation: Case Study

et al. 2017

Generation of electric energy through wind turbines is one of the practically inexhaustible alternatives of generation. It is considered a source of clean energy, but still needs a lot of research for the development of science and technologies that ensures uniformity in generation, providing a greater participation of this source in the energy matrix, since the wind presents abrupt variations in speed, density and other important variables. In wind-based electrical systems, it is essential to predict at least one day in advance the future values of wind behavior, in order to evaluate the availability of energy for the next period, which is relevant information in the dispatch of the generating units and in the control of the electrical system. This paper develops ultra-short, short, medium and long-term prediction models of wind speed, based on computational intelligence techniques, using artificial neural network models, Autoregressive Integrated Moving Average (ARIMA) and hybrid models including forecasting using wavelets. For the application of the methodology, the meteorological variables of the database of the national organization system of environmental data (SONDA), Petrolina station, from 1 January 2004 to 31 March 2017, were used. A comparison among results by different used approaches is also done and it is also predicted the possibility of power and energy generation using a certain kind of wind generator.

“…The advantages of different prediction models are combined, and good prediction results are achieved. The authors proposed a novel hybrid model on the basis of wavelet neural network optimized by genetic algorithm, processing raw data with variational mode decomposition (VMD), and a phase space reconstruction method, which obtained better prediction results as compared with a model of PSR‐BPNN . The authors proposed a new method by assembling the two technologies of EMD and Elman neural network.…”

Section: Introductionmentioning

confidence: 99%

A prediction approach using ensemble empirical mode decomposition‐permutation entropy and regularized extreme learning machine for short‐term wind speed

Tian

Wang

2019

Wind Energy

Accurate prediction of short-term wind speed is of great significance to the operation and maintenance of wind farms, the optimal scheduling of turbines, and the safe and stable operation of power grids. A prediction approach for short-term wind speed using ensemble empirical mode decomposition-permutation entropy and regularized extreme learning machine is proposed. Firstly, wind speed time series is decomposed into several components with different frequency by ensemble empirical mode decomposition, which can reduce the non-stationarity of the original time series. The permutation entropy value for each component is used to analyze its complexity. The components can be recombined to obtain a set of new subsequences. Then, different prediction models based on regularized extreme learning machine are used to predict each subsequence. Fivefold cross validation is used to improve the reliability of the regularized extreme learning machine model. Finally, the predicted value of each subsequence is superimposed to obtain the final predictive result. Ten minutes, 30 minutes, and 1 hour short-term wind speed data from wind farms in Liaoning Province, China, are used for conducting experiments. The experimental results indicate that the values of the root mean square error of the developed prediction approach utilizing 10 minutes, 30 minutes, and 1 hour interval data are 0.5629, 0.4473, and 0.5697; mean absolute error are 0.4427, 3.0701, and 0.4897; mean absolute percentile error are 4.1456%, 16.8166%, and 6.8166%; relative root mean square are 0.0505, 0.2997, and 0.2609; square sum error are 55.5263, 59.6347, and 64.9154; and the Theil inequality coefficient are 0.0235, 0.0808, and 0.0625, which are much lower than those of the comparison methods. The values of the R square of the developed prediction approach utilizing 10 minutes, 30 minutes, and 1 hour interval data are 0.9363, 0.9161, and 0.9472, and the index of agreement are 0.9994, 0.9925, and 0.9894, whichare higher than those of the comparison methods. The Pearson's test results show that the association strength between the actual value and the predicted values of the proposed approach is stronger. Also, the proposed prediction approach in this paper has higher reliability under the same confidence level. The effectiveness of the proposed prediction approach for short-term wind speed is verified.