Design of Short-Term Power Load Forecasting Model Based on Deep Neural Network

Q3 Environmental Science
Qinwei Duan, Zhu Chao, Cong Fu, Yashan Zhong, Jiaxin Zhuo, Ye Liao
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引用次数: 0

Abstract

In power system operation and planning, the accuracy of short-term power load forecasting is very important. Because of its powerful data processing and modeling ability, deep neural network has become an effective tool to accurately predict short-term power load. In this study, a short-term power load prediction model based on deep neural network is designed, which adopts deep long short-term memory and threshold period unit model, and combines Boosting algorithm for model fusion. The results show that the average absolute percentage error of the model fused by Boosting algorithm is 0.07%, which is 1.02% lower than the average weight method and 0.59% lower than the reciprocal error method. Boosting fusion model can effectively reduce the overall prediction error and maintain high stability of prediction error at peak, plateau and time sampling points, so as to achieve good prediction effect. Specifically, the MAPE of the model fused using Boosting algorithm is 0.07% (95% confidence), which is 1.14% higher than the average weight method and 0.79% higher than the reciprocal error method. The design of short-term power load forecasting model based on deep neural network can provide more accurate prediction for power system operation and planning, and help to improve the operation efficiency and reliability of power system. At the same time, the design and application of this model also provide a new idea and method for the application of deep learning in power system. The introduction of Boosting algorithm further improves the prediction accuracy and stability of the model, which is a major innovation in model design.
基于深度神经网络的短期电力负荷预测模型设计
在电力系统运行和规划中,短期电力负荷预测的准确性非常重要。深度神经网络具有强大的数据处理和建模能力,已成为准确预测短期电力负荷的有效工具。本研究设计了一种基于深度神经网络的短期电力负荷预测模型,该模型采用深度长短期记忆和阈值周期单元模型,并结合 Boosting 算法进行模型融合。结果表明,Boosting 算法融合模型的平均绝对百分比误差为 0.07%,比平均权重法低 1.02%,比倒易误差法低 0.59%。提升融合模型能有效降低整体预测误差,并在峰值、高原和时间采样点上保持预测误差的高度稳定性,从而达到良好的预测效果。具体来说,使用 Boosting 算法融合模型的 MAPE 为 0.07%(95% 置信度),比平均权重法高 1.14%,比倒易误差法高 0.79%。基于深度神经网络的短期电力负荷预测模型的设计可以为电力系统的运行和规划提供更准确的预测,有助于提高电力系统的运行效率和可靠性。同时,该模型的设计和应用也为深度学习在电力系统中的应用提供了新的思路和方法。Boosting算法的引入进一步提高了模型的预测精度和稳定性,是模型设计的一大创新。
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来源期刊
Strategic Planning for Energy and the Environment
Strategic Planning for Energy and the Environment Environmental Science-Environmental Science (all)
CiteScore
1.50
自引率
0.00%
发文量
25
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