7 results on '"phase space reconstruction"'
Search Results
2. Short-Term Wind Speed Prediction Based on Variational Mode Decomposition and Linear–Nonlinear Combination Optimization Model
- Author
-
Wei Sun and Qi Gao
- Subjects
short-term wind speed production ,variational mode decomposition ,phase space reconstruction ,autoregressive integrated moving average model ,back propagation neural network ,particle swarm optimization least squares support vector machine ,Technology - Abstract
Wind power, one of renewable energy resources, is a fluctuating source of energy that prevents its further participation in the power market. To improve the stability of the wind power injected into the power grid, a short-term wind speed predicting model is proposed in this work, named VMD-P-(ARIMA, BP)-PSOLSSVM. In this model, variational mode decomposition (VMD) is combined with phase space reconstruction (P) as data processing method to determine intrinsic mode function (IMF) and its input−output matrix in the prediction model. Then, the linear model autoregressive integrated moving average model (ARIMA) and typical nonlinear model back propagation neural network (BP) are adopted to forecast each IMF separately and get the prediction of short-term wind speed by adding up the IMFs. In the final stage, particle swarm optimization least squares support vector machine (PSOLSSVM) uses the prediction results of the two separate models from previous step for the secondary prediction. For the proposed method, the PSOLSSVM employs different mathematical principles from ARIMA and BP separately, which overcome the shortcoming of using just single models. The proposed combined optimization model has been applied to two datasets with large fluctuations from a northern China wind farm to evaluate the performance. A performance comparison is conducted by comparing the error from the proposed method to six other models using single prediction techniques. The comparison result indicates the proposed combined optimization model can deliver more accurate and robust prediction than the other models; meanwhile, it means the power grid dispatching work can benefit from implementing the proposed predicting model in the system.
- Published
- 2019
- Full Text
- View/download PDF
3. Analysis and Forecasting of the Carbon Price in China’s Regional Carbon Markets Based on Fast Ensemble Empirical Mode Decomposition, Phase Space Reconstruction, and an Improved Extreme Learning Machine
- Author
-
Wei Sun and Ming Duan
- Subjects
carbon price forecasting ,decomposition ,phase space reconstruction ,maximal Lyapunov exponent ,partial autocorrelation function ,extreme learning machine optimized by particle swarm optimization ,Technology - Abstract
With the development of the carbon market in China, research on the carbon price has received more and more attention in related fields. However, due to its nonlinearity and instability, the carbon price is undoubtedly difficult to predict using a single model. This paper proposes a new hybrid model for carbon price forecasting that combines fast ensemble empirical mode decomposition, sample entropy, phase space reconstruction, a partial autocorrelation function, and an extreme learning machine that has been improved by particle swarm optimization. The original carbon price series is decomposed using the fast ensemble empirical mode decomposition and sample entropy methods, which eliminate noise interference. Then, the phase space reconstruction and partial autocorrelation function methods are combined to determine the input and output variables in the forecasting models. An extreme learning machine optimized by particle swarm optimization was employed to forecast carbon prices. An empirical study based on carbon prices in three typical regional carbon markets in China found that this new hybrid model performed better than other comparable models.
- Published
- 2019
- Full Text
- View/download PDF
4. Ultra-Short-Term Wind Power Prediction Based on Multivariate Phase Space Reconstruction and Multivariate Linear Regression
- Author
-
Rongsheng Liu, Minfang Peng, and Xianghui Xiao
- Subjects
wind power prediction ,phase space reconstruction ,multivariate linear regression ,cloud computing ,time series ,Technology - Abstract
In order to improve the accuracy of wind power prediction (WPP), we propose a WPP based on multivariate phase space reconstruction (MPSR) and multivariate linear regression (MLR). Firstly, the multivariate time series (TS) are constructed through reasonable selection of wind power and weather factors, which are closely associated with wind power. Secondly, the phase space of the multivariate time series is reconstructed based on the chaos theory and C-C method. Thirdly, an auto regression model for multivariate phase space is created by regarding phase variables as state variables, and the very-short-term wind power is predicted by using a multi-linear regression algorithm. Finally, a parallel algorithm based on map/reduce is presented to improve computing speed. A cloud computing platform, Hadoop consisting of five nodes, is established as a matter of convenience, followed by the prediction of wind power of a wind farm in the Hunan province of China. The experimental results show that the model based on MPSR and MLR is more accurate than both the continuous method and the simple approximation method, and the parallel algorithm based on map/reduce effectively accelerates the computing speed.
- Published
- 2018
- Full Text
- View/download PDF
5. Multi-Step Ahead Wind Power Generation Prediction Based on Hybrid Machine Learning Techniques
- Author
-
Wei Dong, Qiang Yang, and Xinli Fang
- Subjects
multi-step ahead prediction ,phase space reconstruction ,input variable selection ,K-means clustering ,neuro-fuzzy inference system ,wind power prediction ,Technology - Abstract
Accurate generation prediction at multiple time-steps is of paramount importance for reliable and economical operation of wind farms. This study proposed a novel algorithmic solution using various forms of machine learning techniques in a hybrid manner, including phase space reconstruction (PSR), input variable selection (IVS), K-means clustering and adaptive neuro-fuzzy inference system (ANFIS). The PSR technique transforms the historical time series into a set of phase-space variables combining with the numerical weather prediction (NWP) data to prepare candidate inputs. A minimal redundancy maximal relevance (mRMR) criterion based filtering approach is used to automatically select the optimal input variables for the multi-step ahead prediction. Then, the input instances are divided into a set of subsets using the K-means clustering to train the ANFIS. The ANFIS parameters are further optimized to improve the prediction performance by the use of particle swarm optimization (PSO) algorithm. The proposed solution is extensively evaluated through case studies of two realistic wind farms and the numerical results clearly confirm its effectiveness and improved prediction accuracy compared to benchmark solutions.
- Published
- 2018
- Full Text
- View/download PDF
6. Short-Term Wind Speed Prediction Based on Variational Mode Decomposition and Linear–Nonlinear Combination Optimization Model
- Author
-
Qi Gao and Wei Sun
- Subjects
Control and Optimization ,particle swarm optimization least squares support vector machine ,Computer science ,020209 energy ,variational mode decomposition ,Energy Engineering and Power Technology ,02 engineering and technology ,lcsh:Technology ,Wind speed ,phase space reconstruction ,Control theory ,Least squares support vector machine ,0202 electrical engineering, electronic engineering, information engineering ,short-term wind speed production ,Autoregressive integrated moving average ,Electrical and Electronic Engineering ,Engineering (miscellaneous) ,Wind power ,lcsh:T ,Renewable Energy, Sustainability and the Environment ,business.industry ,Mode (statistics) ,Particle swarm optimization ,Renewable energy ,Nonlinear system ,autoregressive integrated moving average model ,020201 artificial intelligence & image processing ,back propagation neural network ,business ,Energy (miscellaneous) - Abstract
Wind power, one of renewable energy resources, is a fluctuating source of energy that prevents its further participation in the power market. To improve the stability of the wind power injected into the power grid, a short-term wind speed predicting model is proposed in this work, named VMD-P-(ARIMA, BP)-PSOLSSVM. In this model, variational mode decomposition (VMD) is combined with phase space reconstruction (P) as data processing method to determine intrinsic mode function (IMF) and its input&ndash, output matrix in the prediction model. Then, the linear model autoregressive integrated moving average model (ARIMA) and typical nonlinear model back propagation neural network (BP) are adopted to forecast each IMF separately and get the prediction of short-term wind speed by adding up the IMFs. In the final stage, particle swarm optimization least squares support vector machine (PSOLSSVM) uses the prediction results of the two separate models from previous step for the secondary prediction. For the proposed method, the PSOLSSVM employs different mathematical principles from ARIMA and BP separately, which overcome the shortcoming of using just single models. The proposed combined optimization model has been applied to two datasets with large fluctuations from a northern China wind farm to evaluate the performance. A performance comparison is conducted by comparing the error from the proposed method to six other models using single prediction techniques. The comparison result indicates the proposed combined optimization model can deliver more accurate and robust prediction than the other models, meanwhile, it means the power grid dispatching work can benefit from implementing the proposed predicting model in the system.
- Published
- 2019
7. Analysis and Forecasting of the Carbon Price in China’s Regional Carbon Markets Based on Fast Ensemble Empirical Mode Decomposition, Phase Space Reconstruction, and an Improved Extreme Learning Machine
- Author
-
Ming Duan and Wei Sun
- Subjects
Mathematical optimization ,Control and Optimization ,Computer science ,maximal Lyapunov exponent ,020209 energy ,Energy Engineering and Power Technology ,chemistry.chemical_element ,02 engineering and technology ,010501 environmental sciences ,lcsh:Technology ,01 natural sciences ,Hilbert–Huang transform ,phase space reconstruction ,Carbon price ,0202 electrical engineering, electronic engineering, information engineering ,Electrical and Electronic Engineering ,Engineering (miscellaneous) ,carbon price forecasting ,decomposition ,partial autocorrelation function ,extreme learning machine optimized by particle swarm optimization ,0105 earth and related environmental sciences ,Extreme learning machine ,lcsh:T ,Renewable Energy, Sustainability and the Environment ,Particle swarm optimization ,Partial autocorrelation function ,chemistry ,Phase space ,Carbon ,Energy (miscellaneous) - Abstract
With the development of the carbon market in China, research on the carbon price has received more and more attention in related fields. However, due to its nonlinearity and instability, the carbon price is undoubtedly difficult to predict using a single model. This paper proposes a new hybrid model for carbon price forecasting that combines fast ensemble empirical mode decomposition, sample entropy, phase space reconstruction, a partial autocorrelation function, and an extreme learning machine that has been improved by particle swarm optimization. The original carbon price series is decomposed using the fast ensemble empirical mode decomposition and sample entropy methods, which eliminate noise interference. Then, the phase space reconstruction and partial autocorrelation function methods are combined to determine the input and output variables in the forecasting models. An extreme learning machine optimized by particle swarm optimization was employed to forecast carbon prices. An empirical study based on carbon prices in three typical regional carbon markets in China found that this new hybrid model performed better than other comparable models.
- Published
- 2019
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