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An improved forecasting model of short-term electric load of papermaking enterprises for production line optimization

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  • Lai, Changzhi
  • Wang, Yu
  • Fan, Kai
  • Cai, Qilin
  • Ye, Qing
  • Pang, Haoqiang
  • Wu, Xi

Abstract

The load forecasting is generally based on historical data extrapolation in most forecasting models, resulting in a poor correlation with the production information and significant application limitations. To improve this, the production information-based backpropagation neural network (BPNN) combined with genetic algorithm (GA) and particle swarm optimization (PSO) hybrid forecasting model was established for a papermaking enterprise with the three-level electric data collected. Based on this, shift electric consumption quotas and air compressor transformation energy-saving predictions were proposed. The results show that when the production management information is included, the average mean absolute percent error (MAPE) of the six forecasting results can be 1.2%, an improvement of 18.3% on average, indicating the high accuracy of our proposed model. The unit energy consumption of paper products can be reduced by 3.26% through management optimization using the proposed shift electric consumption quotas. Under the guidance of energy-saving predictions, the overall power saving rate of the production line after the enterprise reforms the air compressor is 3%. The proposed load-forecasting model and energy optimization methods have high accuracy and practical applicability.

Suggested Citation

  • Lai, Changzhi & Wang, Yu & Fan, Kai & Cai, Qilin & Ye, Qing & Pang, Haoqiang & Wu, Xi, 2022. "An improved forecasting model of short-term electric load of papermaking enterprises for production line optimization," Energy, Elsevier, vol. 245(C).
    • Handle: RePEc:eee:energy:v:245:y:2022:i:c:s0360544222001281
    DOI: 10.1016/j.energy.2022.123225
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    References listed on IDEAS

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    Cited by:

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    2. Wu, Cong & Li, Jiaxuan & Liu, Wenjin & He, Yuzhe & Nourmohammadi, Samad, 2023. "Short-term electricity demand forecasting using a hybrid ANFIS–ELM network optimised by an improved parasitism–predation algorithm," Applied Energy, Elsevier, vol. 345(C).

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