基于小波去噪和人工神经网络的电力系统短期负荷预测研究

Zihan Liu
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引用次数: 0

摘要

电力系统短期负荷预测对电力系统的可靠、安全、经济运行具有重要作用。电力系统负荷预测数据是电网规划、调度、营销等部门的重要依据。为了充分挖掘电力系统负荷数据中的有效信息,进行准确的短期负荷预测,本文提出了一种基于小波去噪的长短期记忆(LSTM)模型来构建短期负荷预测模型。采用小波去噪方法对数据进行预处理,以保证预测模型的准确性,同时采用LSTM方法对电力系统进行高质量的短期负荷预测。本文提出的方法具有训练学习能力强、收敛速度快、预测精度高、适应性强等优点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on Short-term Load Forecasting of Power System Based on Wavelet Denoising and Artificial Neural Network
Power system short-term load forecasting plays an important role in the reliable, safe and economic operation of power system. Power system load forecasting data is an important basis for power grid planning, scheduling, marketing and other departments. In order to fully mine the effective information in the load data of power system and carry out accurate short-term load forecasting, this paper proposes a Long Short-Term Memory (LSTM) model based on wavelet denoising to build a short-term load forecasting model. Wavelet denoising method is used for data preprocessing, so as to ensure the accuracy of the prediction model, while LSTM is used to achieve high-quality short-term load forecasting of the power system. The method proposed in this paper has the advantages of strong training and learning ability, fast convergence speed, high prediction accuracy and strong adaptability.
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