Abstract
Accurate power load forecasting has an important impact on power systems. In order to improve the load forecasting accuracy, a new load forecasting model, VMD–CISSA–LSSVM, is proposed. The model combines the variational modal decomposition (VMD) data preprocessing method, the sparrow search algorithm (SSA) and the least squares support vector machine (LSSVM) model. A multi-strategy improved chaotic sparrow search algorithm (CISSA) is proposed to address the shortcomings of the SSA algorithm, which is prone to local optima and a slow convergence. The initial population is generated using an improved tent chaotic mapping to enhance the quality of the initial individuals and population diversity. Second, a random following strategy is used to optimize the position update process of the followers in the sparrow search algorithm, balancing the local exploitation performance and global search capability of the algorithm. Finally, the Levy flight strategy is used to expand the search range and local search capability. The results of the benchmark test function show that the CISSA algorithm has a better search accuracy and convergence performance. The volatility of the original load sequence is reduced by using VMD. The optimal parameters of the LSSVM are optimized by the CISSA. The simulation test results demonstrate that the VMD–CISSA–LSSVM model has the highest prediction accuracy and stabler prediction results.
Highlights
Electricity load forecasting is of great importance to the development of modern power systems
The research on electricity load forecasting can be divided into traditional forecasting methods based on mathematical statistics and forecasting methods based on artificial intelligence (AI)
U n (ω ) dω where δ(t) is the unit pulse signal; n is the n-th modal component obtained after the signal decomposition; N is the total number of modal decompositions; k is the number of iterations; ωn is the central frequency of the modal; ∂t is the sign of the partial derivative operation; α is the penalty factor; j is the unity of the imaginary number; ⊗
Summary
Electricity load forecasting is of great importance to the development of modern power systems. Another study [16] proposed an electricity load forecasting model combining variational modal decomposition (VMD), maximum relevance minimum redundancy (MRMR), a BPNN neural network and the LSSVM model. Another study [20] proposed a sparrow search algorithm (SSA) and ELM neural network based electricity load forecasting model. [26] proposed a combined forecasting model based on improved empirical modal decomposition (IEMD), autoregressive integrated moving average (ARIMA) and wavelet neural network (WNN). Another study [27] proposed a novel electricity load forecasting model based on data preprocessing and a multi-objective cuckoo search algorithm based on non-dominated ranking to optimize the GRNN. [26,27,28] integrated the idea of combinatorial modeling, multi-strategy optimization and data pre-processing, the authors did not consider the endpoint effects inherent in EMD denoising that can affect the final prediction results
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