Study on the Complexity Reduction of Observed Sequences Based on Different Sampling Methods: A Case of Wind Speed Data

Abstract

Many studies have confirmed that the complexity of a time sequence is closely related to its predictability, but few studies have proposed methods to reduce the time sequence complexity, which is the key to improving its predictability. This study analyzes the complexity reduction method of observed time sequences based on wind speed data. Five sampling methods, namely the random method, average method, sequential method, max method and min method, are used to obtain a new time sequence with a low resolution from a high resolution time sequence. The ideal time sequences constructed by mathematical functions and the observed wind speed time sequences are studied. The results show that the complexity of ideal time series of periodic sequences, chaotic sequences and random sequences increases in turn, and the complexity is expressed by the approximate entropy (ApEn) exponent. Furthermore, the complexity of the observed wind speed is closer to the complexity of a random sequence, which indicates that the wind speed sequence is not easy to predict. In addition, the complexity of sub-time series change with different sampling methods. The complexity of sub-time series obtained by the average method is the lowest, which indicates that the average method can reduce the complexity of observed data effectively. Therefore, the complexity of sub-time series sampled from the high-resolution wind speed data is reduced by using the average method. The method that can reduce the complexity of wind speed substantially will help to choose the appropriate wind speed data, thus improving the predictability.

Keywords

• complexity reduction
• observed data
• multi-scale
• sampling methods
• sampling interval
• ApEn