Abstract
Influenced by many uncertain and random factors, nonstationary, nonlinearity, and time-variety appear in power load series, which is difficult to forecast accurately. Aiming at locating these issues of power load forecasting, an innovative hybrid method is proposed to forecast power load in this paper. Firstly, ensemble empirical mode decomposition (EEMD) is used to decompose the power load series into a series of independent intrinsic mode functions (IMFs) and a residual term. Secondly, genetic algorithm (GA) is then applied to determine the best weights of each IMF and the residual term named ensemble empirical mode decomposition based on weight (WEEMD). Thirdly, least square support vector machine (LSSVM) and nonparametric generalized autoregressive conditional heteroscedasticity (NPGARCH) are employed to forecast the subseries, respectively, based on the characteristics of power load series. Finally, the forecasted power load of each component is summed as the final forecasted result of power load. Compared with other methods, the forecasting results of this proposed model applied to the electricity market of Pennsylvania-New Jersey-Maryland (PJM) indicate that the proposed model outperforms other models.
Highlights
In the operation of power system, power load forecasting is an important part of power system planning and one of the most influential factors for the improvement of social economy developing, which has a significant impact on generation, transmission, and distribution
The advantages of econometric models and signal processing are emphasized by this research. This hybrid method for load power forecasting includes ensemble empirical mode decomposition based on variable weights (WEEMD), genetic algorithm (GA), least square support vector machine (LSSVM), and nonparametric generalized autoregressive conditional heteroscedasticity (NPGARCH)
In [17], a combination of the wavelet transform (WT) and gray model is proposed for short-term power load forecasting, which is improved by particle swarm optimization (PSO)
Summary
In the operation of power system, power load forecasting is an important part of power system planning and one of the most influential factors for the improvement of social economy developing, which has a significant impact on generation, transmission, and distribution. The advantages of econometric models and signal processing are emphasized by this research This hybrid method for load power forecasting includes ensemble empirical mode decomposition based on variable weights (WEEMD), genetic algorithm (GA), least square support vector machine (LSSVM), and nonparametric generalized autoregressive conditional heteroscedasticity (NPGARCH). In order to test the validity and feasibility of the model, amount of historical data of power load in American Electric Power (AEP) has been adopted to apply this new hybrid method and compare it with previously well-known methods for the power load forecasting accuracy. Results indicate that this hybrid method outperforms the compared methods with the forecasting accuracy.
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