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A combination model with variable weight optimization for short-term electrical load forecasting

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  • Li, Wei-Qin
  • Chang, Li

Abstract

The present study establishes a robust combination forecasting model and achieves the accurate prediction of electrical load by considering the dependency of the load series and the meteorological factors. On this basis, the culture particle swarm optimization algorithm is developed to improve the accuracy of the forecast. The merit is that by the particle mutation strategy, parameter adjustment strategy dependent on the fitness and the knowledge updating strategy, particles are avoided to trap in local optimum, consequently improving the computational speed and performance. Moreover, the data preprocessing technology based on the EEMD is proposed to reduce the random noises of the load series and to improve the robust of the forecasting model. The anomaly detection model is proposed in view of the probability distribution of relative errors. To assess the applicability and accuracy of the proposed model, it is compared with ant colony optimization, genetic algorithm, simulated annealing approach, cuckoo search algorithm, differential evaluation and artificial cooperative search. Results validated by the actual data sets for Shaanxi province, China, show higher accuracy and better reliability of the proposed model in comparison with other optimization models.

Suggested Citation

  • Li, Wei-Qin & Chang, Li, 2018. "A combination model with variable weight optimization for short-term electrical load forecasting," Energy, Elsevier, vol. 164(C), pages 575-593.
    • Handle: RePEc:eee:energy:v:164:y:2018:i:c:p:575-593
    DOI: 10.1016/j.energy.2018.09.027
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    3. Haoran Zhao & Sen Guo, 2021. "Uncertain Interval Forecasting for Combined Electricity-Heat-Cooling-Gas Loads in the Integrated Energy System Based on Multi-Task Learning and Multi-Kernel Extreme Learning Machine," Mathematics, MDPI, vol. 9(14), pages 1-32, July.
    4. Tayab, Usman Bashir & Zia, Ali & Yang, Fuwen & Lu, Junwei & Kashif, Muhammad, 2020. "Short-term load forecasting for microgrid energy management system using hybrid HHO-FNN model with best-basis stationary wavelet packet transform," Energy, Elsevier, vol. 203(C).
    5. Li, Chen, 2020. "Designing a short-term load forecasting model in the urban smart grid system," Applied Energy, Elsevier, vol. 266(C).
    6. Zhang, Jinliang & Siya, Wang & Zhongfu, Tan & Anli, Sun, 2023. "An improved hybrid model for short term power load prediction," Energy, Elsevier, vol. 268(C).
    7. Kailai Ni & Jianzhou Wang & Guangyu Tang & Danxiang Wei, 2019. "Research and Application of a Novel Hybrid Model Based on a Deep Neural Network for Electricity Load Forecasting: A Case Study in Australia," Energies, MDPI, vol. 12(13), pages 1-30, June.
    8. Wu, Jinran & Cui, Zhesen & Chen, Yanyan & Kong, Demeng & Wang, You-Gan, 2019. "A new hybrid model to predict the electrical load in five states of Australia," Energy, Elsevier, vol. 166(C), pages 598-609.

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