Abstract
To improve the accuracy and reliability of short-term power load forecasting and reduce the difficulty caused by load volatility and non-linearity, a hybrid forecasting model (CEEMDAN-SE-VMD-PSR-WOA-SVR) is proposed. Firstly, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is employed to generate multiple intrinsic modal functions (IMF) by decomposing the historical power load series. Then the sample entropy (SE) of each IMF is calculated to quantitatively evaluate the corresponding complexity. Afterward, variational mode decomposition (VMD) is adopted to achieve secondary decomposition for the component with the maximum sample entropy. Subsequently, the phase space reconstruction (PSR) is applied to reconstruct each IMF into a high-dimensional feature space matrix, which is formed as the input of support vector regression (SVR). Finally, SVR optimized by whale optimization algorithm (WOA) is used for the prediction, where the predicted values of all IMFs are accumulated to obtain the final prediction results. The experimental result demonstrates that the proposed hybrid model can effectively decompose the load series with non-linear characteristic and provide more accurate forecasting results by comparing the other models.
Highlights
Power load forecasting plays an important role in power system planning and economic operation
To obtain a smoother sequence and better prediction effect than a single decomposition, the two-phase decomposition framework CEEMDAN-variational mode decomposition (VMD) [24] considering sample entropy (SE) was adopted, and the ELM optimized by the differential evolutionary algorithm was employed to forecast the air quality, and the results showed the efficiency of this method
To reduce the non-linearity of load series and improve the forecasting accuracy, this paper proposes a hybrid model for short-term load forecasting, which combines two-phase decomposition and optimized support vector regression (CEMDAN-SEVMD-phase space reconstruction (PSR)-whale optimization algorithm (WOA)-SVR)
Summary
Power load forecasting plays an important role in power system planning and economic operation. The environment and human activities greatly affect the power load. The fluctuation of the short-term power load sequence occurs in real-time, and changes periodically in units of days and weeks. The time series of short-term power load has certain regularity as well as large volatility and. The commonly used power load prediction methods are mainly divided into the following two categories [1]: mathematical statistics model, and artificial intelligence algorithm model. Zhao et al [2] proposed a data-driven nonparametric autoregressive (AR) model and obtained higher short-term load forecasting accuracy by comparing AR models.
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