Novel Multi-Step Short-Term Wind Power Prediction Framework Based on Chaotic Time Series Analysis and Singular Spectrum Analysis

Safari, Nima; Chung, C. Y.; Price, G. C. D.

doi:10.1109/tpwrs.2017.2694705

Cited by 135 publications

(63 citation statements)

References 38 publications

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“…During recent years, the SSA algorithm has been widely used in various industries, and Safari et al [18] even upgraded it as multi-scaled SSA (MSSSA) in making short-term forecasts for certain objects, such as wind power, which may have many chaotic components.…”

Section: On Developments and Applications Of Singular Spectrum Analysismentioning

confidence: 99%

Modeling the Trend of Credit Card Usage Behavior for Different Age Groups Based on Singular Spectrum Analysis

Nai¹,

Liu

Wang³

et al. 2018

Algorithms

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Credit card holders from different age groups have different usage behaviors, so deeply investigating the credit card usage condition and properly modeling the usage trend of all customers in different age groups from time series data is meaningful for financial institutions as well as banks. Until now, related research in trend analysis of credit card usage has mostly been focused on specific group of people, such as the behavioral tendencies of the elderly or college students, or certain behaviors, such as the increasing number of cards owned and the rise in personal card debt or bankruptcy, in which the only analysis methods employed are simply enumerating or classifying raw data; thus, there is a lack of support in specific mathematical models based on usage behavioral time series data. Considering that few systematic modeling methods have been introduced, in this paper, a novel usage trend analysis method for credit card holders in different age groups based on singular spectrum analysis (SSA) has been proposed, using the time series data from the Survey of Consumer Payment Choice (SCPC). The decomposition and reconstruction process in the method is proposed. The results show that the credit card usage frequency falls down from the age of 26 to the lowest point at around the age of 58 and then begins to increase again. At last, future work is discussed.

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Section: On Developments and Applications Of Singular Spectrum Analysismentioning

confidence: 99%

Modeling the Trend of Credit Card Usage Behavior for Different Age Groups Based on Singular Spectrum Analysis

Nai¹,

Liu

Wang³

et al. 2018

Algorithms

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“…In [13], the features are firstly extracted from the historical power generation data, and then the dataset is split into subsets based on the stationary patterns. In [14], a novel decomposition approach to fully consider the chaotic nature of wind power time series was proposed. The time series data were separated into different frequency characteristics using ensemble empirical mode decomposition (EMD) before carrying out the chaotic time series analysis and singular spectrum analysis (SSA).…”

Section: Introductionmentioning

confidence: 99%

Multi-Step Ahead Wind Power Generation Prediction Based on Hybrid Machine Learning Techniques

2018

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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.

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“…The intrinsic nature of wind power, including its stochastic fluctuation, intermittency and vector variability remains a major challenge for control optimization and grid connection in wind power generation [13]. Theoretically, to predict wind speed, wind direction and air density in advance can provide a technical solution to this problem.…”

Section: Introductionmentioning

confidence: 99%

Overview of Wind Parameters Sensing Methods and Framework of a Novel MCSPV Recombination Sensing Method for Wind Turbines

Shen

Zhou

et al. 2018

Energies

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The paper presents an overview of the traditional methods to obtain wind parameters such as wind speed, wind direction and air density. After analyzing wind turbines' arrangements and communication characteristics and the correlation of operation data between wind turbines, the paper proposes a novel recombination-sensing method route of "measuring-correlating-sharingpredicting-verifying" (MCSPV) and explores its feasibility. The analysis undertaken in the paper shows that the wind speed and wind direction instrument fixed on the wind turbine nacelle is simple and economical. However, it performs in-process measurement, which restricts the control optimization of wind turbines. The light detection and ranging (LIDAR) technology which is accurate and fast, ensures an early and super short-time sensing of wind speed and wind direction but it is costly. The wind parameter predictive perception method can predict wind speed and wind power at multiple time scales statistically, but it has limited significance for the control of the action of wind turbines. None of the traditional wind parameter-sensing methods have ever succeeded in air density sensing. The MCSPV recombination sensing method is feasible, both theoretically and in engineering, for realizing the efficient and accurate sensing and obtaining of such parameters as wind speed, wind direction and air density aimed at the control of wind turbines.

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Novel Multi-Step Short-Term Wind Power Prediction Framework Based on Chaotic Time Series Analysis and Singular Spectrum Analysis

Cited by 135 publications

References 38 publications

Modeling the Trend of Credit Card Usage Behavior for Different Age Groups Based on Singular Spectrum Analysis

Modeling the Trend of Credit Card Usage Behavior for Different Age Groups Based on Singular Spectrum Analysis

Multi-Step Ahead Wind Power Generation Prediction Based on Hybrid Machine Learning Techniques

Overview of Wind Parameters Sensing Methods and Framework of a Novel MCSPV Recombination Sensing Method for Wind Turbines

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