Abstract
A novel hybrid ensemble framework is developed to forecast the short-term wind speed, which consists of a data preprocessing technique, data-driven based forecasting algorithms, and an improved Jaya algorithm. In the data preprocessing process, the pauta criterion is employed to find out the outliers, and the variational mode decomposition algorithm decompose the original series to extract the trend and time-frequency information of the historical inputs. The data-driven forecasting algorithms, such as BP, LSSVM, ANFIS, and Elman, are exploited as the original predictor of the framework, while the weights of the predictors are computed by an improved optimization algorithm-CLSJaya. Based on the experimental results of two time-scale datasets from three sites, the proposed framework successfully overcomes the limitations of the individual forecasting models and achieves promising forecasting accuracy.
Highlights
Due to the deterioration of the environment and the depletion of conventional energy resources, wind energy has aroused widespread interest and research enthusiasm [1]
Yang and Wang [38] investigated an improved WCA to optimize the model combination coefficients of BP, RBF, wavelet neural network (WNN) and ENN; Das et al [39] establish a Jaya-ELM model in order to forecast the currency, which the results show a better result than any other modeling algorithms, as well as other optimization algorithms
For a more comprehensive explanation of the hybrid framework proposed in this paper, in section 2, the basic methodology of the framework will be introduced, which include a data preprocessing technique, forecasting algorithms and an advanced optimization algorithm based on no negative constraint theory
Summary
Due to the deterioration of the environment and the depletion of conventional energy resources, wind energy has aroused widespread interest and research enthusiasm [1]. According to the Global Wind Energy Council (GWEC), in 2018, the newly installed wind power capacity is 51.3 GW [2]. Forecasting of wind speed is imperative for an efficient and economical integration of wind energy into the electricity supply system [3].because of the randomness, intermittent, and uncontrollable feature, it is a substantial challenge to establish an accurate wind speed forecasting model [4]–[7]. Till various methods have been employed for wind speed forecasting. The proposed methods can be divided into four categories: (i) physical algorithms, (ii) conventional statistical algorithms, (iii) spatial correlation algorithms, and (iv) machine learning algorithms. Physical algorithms mainly utilize the meteorological environment data, which include temperature, speed, density, and topography information, etc. The main purpose of this category is to use the numerical weather
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