Application of an Optimized Version of Modified Elman Neural Network Using Improved Pelican Optimizer for Energy System Marginal Price Forecasting

Yuanchun Tang; Shichun Chen; Zhuolin Chen; Ying Shi

doi:10.31081/dmame8220251606

Authors

Yuanchun Tang State Grid Fujian Electric Power Research Institute, Fuzhou, Fujian, China.
Shichun Chen State Grid Fujian Electric Power Co., Ltd., Fuzhou, Fujian, China
Zhuolin Chen State Grid Fujian Electric Power Research Institute, Fuzhou, Fujian, China
Ying Shi State Grid Fujian Electric Power Research Institute, Fuzhou, Fujian, China.

DOI:

https://doi.org/10.31081/dmame8220251606

Keywords:

System Marginal Price, Prediction, Modified Elman Neural Network, Fréchet distance, Improved Pelican Optimization Algorithm.

Abstract

Following the liberalisation of the electricity sector and its transition to a competitive market structure, price volatility has intensified, as electricity tariffs are now formed through market-based pricing mechanisms. Electricity price time series exhibit complex properties, including pronounced instability, strong non-linearity, and substantial fluctuations. In response to these characteristics, this study concentrates on the development of a day-ahead system marginal price (SMP) forecasting framework based on an enhanced Elman Neural Network (ENN). To improve the predictive capability of the proposed ENN, a modified pelican optimisation algorithm is employed to optimise its parameters. For the selection of short-term input variables, the Pearson correlation coefficient is applied to identify the most relevant factors. In contrast, long-term input variables are determined by incorporating the discrete Fréchet distance, alongside seasonal attributes, day-type information, forecasted load, and historical SMP values. A comprehensive dataset spanning fifteen years is used to evaluate the effectiveness of the proposed model. The empirical results demonstrate that the suggested approach achieves superior forecasting accuracy compared with conventional SMP prediction methods, confirming its effectiveness for electricity market price forecasting.

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