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An Optimized Forecasting Approach Based on Grey Theory and Cuckoo Search Algorithm: A Case Study for Electricity Consumption in New South Wales

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  • Ping Jiang
  • Qingping Zhou
  • Haiyan Jiang
  • Yao Dong

Abstract

With rapid economic growth, electricity demand is clearly increasing. It is difficult to store electricity for future use; thus, the electricity demand forecast, especially the electricity consumption forecast, is crucial for planning and operating a power system. Due to various unstable factors, it is challenging to forecast electricity consumption. Therefore, it is necessary to establish new models for accurate forecasts. This study proposes a hybrid model, which includes data selection, an abnormality analysis, a feasibility test, and an optimized grey model to forecast electricity consumption. First, the original electricity consumption data are selected to construct different schemes (Scheme 1: short-term selection and Scheme 2: long-term selection); next, the iterative algorithm (IA) and cuckoo search algorithm (CS) are employed to select the best parameter of GM(1,1). The forecasted day is then divided into several smooth parts because the grey model is highly accurate in the smooth rise and drop phases; thus, the best scheme for each part is determined using the grey correlation coefficient. Finally, the experimental results indicate that the GM(1,1) optimized using CS has the highest forecasting accuracy compared with the GM(1,1) and the GM(1,1) optimized using the IA and the autoregressive integrated moving average (ARIMA) model.

Suggested Citation

  • Ping Jiang & Qingping Zhou & Haiyan Jiang & Yao Dong, 2014. "An Optimized Forecasting Approach Based on Grey Theory and Cuckoo Search Algorithm: A Case Study for Electricity Consumption in New South Wales," Abstract and Applied Analysis, Hindawi, vol. 2014, pages 1-13, June.
  • Handle: RePEc:hin:jnlaaa:183095
    DOI: 10.1155/2014/183095
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    Cited by:

    1. Mehmet Fatih Bayramoglu, 2016. "Future Electricity Demand of the Emerging European Countries and the CIS Countries," International Journal of Finance & Banking Studies, Center for the Strategic Studies in Business and Finance, vol. 5(6), pages 15-23, October.
    2. Shalini Shekhawat & Akash Saxena & Ramadan A. Zeineldin & Ali Wagdy Mohamed, 2023. "Prediction of Infectious Disease to Reduce the Computation Stress on Medical and Health Care Facilitators," Mathematics, MDPI, vol. 11(2), pages 1-18, January.
    3. Tsai, Sang-Bing & Xue, Youzhi & Zhang, Jianyu & Chen, Quan & Liu, Yubin & Zhou, Jie & Dong, Weiwei, 2017. "Models for forecasting growth trends in renewable energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 1169-1178.
    4. Gangjun Gong & Xiaonan An & Nawaraj Kumar Mahato & Shuyan Sun & Si Chen & Yafeng Wen, 2019. "Research on Short-Term Load Prediction Based on Seq2seq Model," Energies, MDPI, vol. 12(16), pages 1-18, August.
    5. Akash Saxena & Ramadan A. Zeineldin & Ali Wagdy Mohamed, 2023. "Development of Grey Machine Learning Models for Forecasting of Energy Consumption, Carbon Emission and Energy Generation for the Sustainable Development of Society," Mathematics, MDPI, vol. 11(6), pages 1-13, March.

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