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<p>To accurately predict the electricity consumption trend of individual users and even the entire industry, this paper studies an intelligent prediction algorithm for the power industry based on time series and entropy weight method. Using ARIMA model and X12 model to establish a monthly electricity consumption prediction model, the study obtains the monthly electricity consumption prediction value for the expansion of the power industry. The entropy weight method is employed to calculate the weights of two power industry expansion month electricity consumption forecasting models, thereby achieving intelligent forecasting. The experimental results demonstrate that the maximum error of the proposed method is only 1.78%, and the average time complexity and average space complexity of the proposed algorithm are both below the set threshold.</p>

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Intelligent forecasting algorithm of power industry expansion based on time series and entropy weight method

  • 期刊论文

  • 发表日期：

    2025年2月15日

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  • Guoyao Wu,
  • Zhiqiang Lan,
  • Xiaofang Wu,
  • Xiaoying Huang,
  • Linling Mao

摘要

To accurately predict the electricity consumption trend of individual users and even the entire industry, this paper studies an intelligent prediction algorithm for the power industry based on time series and entropy weight method. Using ARIMA model and X12 model to establish a monthly electricity consumption prediction model, the study obtains the monthly electricity consumption prediction value for the expansion of the power industry. The entropy weight method is employed to calculate the weights of two power industry expansion month electricity consumption forecasting models, thereby achieving intelligent forecasting. The experimental results demonstrate that the maximum error of the proposed method is only 1.78%, and the average time complexity and average space complexity of the proposed algorithm are both below the set threshold.