利用时间序列数据的优化递归神经网络策略预测风力发电量

Krishan Kumar, Priti Prabhakar, Avnesh Verma
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引用次数: 0

摘要

燃料价格不断上涨,使人们开始关注替代能源(RES)的利用。尽管负荷预测比风能预测更准确。为了应对电力供应方面的运营挑战,风能预测仍然至关重要。最近,某种技术已被应用于风能预测。在风力发电场,已经开发并使用了多种风能预测方法。递归网络的主要思想是在多个层和神经元之间共享参数,从而在网络的图序列中形成循环。递归网络旨在处理顺序输入。本研究提出了一种新颖的基于混合优化的 RNN 模型,用于风力发电预测。利用 SpCro 算法(一种拟议的优化方法)调整 RNN 的权重。乌鸦搜索优化算法(CSA)和麻雀搜索算法相结合,形成了 SpCro 算法(SSA)。建议的算法是利用乌鸦的记忆特性和麻雀的探测特性开发的。建议的系统在 MATLAB 中进行了仿真,通过与其他广泛使用的方法(如 CNN 和 DNN)在误差指标方面的比较,验证了建议方法的实用性。因此,与 RNN、SOA+RNN、CSO+RNN、SSA+DELM、CFU-COA 和 GWO+RNN 方法相比,建议方法的 MAE 分别高出 45%、10.02%、10.04%、33.58%、94.81% 和 10.01%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Forecasting wind power using Optimized Recurrent Neural Network strategy with time-series data

Forecasting wind power using Optimized Recurrent Neural Network strategy with time-series data
Fuel prices are rising, bringing attention to the utilization of alternative energy sources (RES). Even though load forecasting is more accurate at making predictions than wind power forecasting is. To address the operational challenges with the supply of electricity, wind energy forecasts remain essential. A certain kind of technology has recently been applied to forecast wind energy. On wind farms, a variety of wind power forecasting methods have been developed and used. The main idea underlying recurrent networks is parameter sharing across the multiple layers and neurons, which results in cycles in the network's graph sequence. Recurrent networks are designed to process sequential input. A novel hybrid optimization-based RNN model for wind power forecasting is proposed in this research. Using the SpCro algorithm, a proposed optimization method, the RNN's weights are adjusted. The Crow Search Optimization (CSA) algorithm and the Sparrow search algorithm are combined to form the SpCro Algorithm (SSA). The suggested Algorithm was developed using the crow's memory traits and the sparrow's detecting traits. The proposed system is simulated in MATLAB, and the usefulness of the suggested approach is verified by comparison with other widely used approaches, such as CNN and DNN, in terms of error metrics. Accordingly, the MAE of the proposed method is 45%, 10.02%, 10.04%, 33.58%, 94.81%, and 10.01% higher than RNN, SOA+RNN, CSO+RNN, SSA+DELM, CFU-COA, and GWO+RNN method.
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