A Short-term Load Forecasting Model Based on Neural Network Considering Weather Features

Feifei Xu, Wenjun Xu, Yidan Qiu, Mei Wu, Ruoyu Wang, Yonghui Li, Peixiao Fan, Jun Yang
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引用次数: 1

Abstract

In recent years, the grid load has been significantly affected by meteorological factors, showing strong nonlinearity and unpredictability, which brings great difficulties to load forecasting. Therefore, the use of meteorological factors for short-term load forecasting has become an indispensable factor to improve the application of smart grid. This paper first collects and analyzes the data of grid load, then disposes the abnormal load data. Secondly, meteorological factors such as temperature, humidity, wind speed, daily radiation, and rainfall are analyzed one by one, and the trend of load changes with temperature and humidity is obtained from this. Besides, these meteorological factors are coupled, and a comprehensive weather perception index is proposed to express the influence of the above factors on human skin perception. Finally, with the comprehensive weather perception index and load data as input, a BP neural network model is established for load forecasting, and actual calculation examples prove the high accuracy and rapidity of the method.
考虑天气特征的神经网络短期负荷预测模型
近年来,电网负荷受气象因素的影响较大,表现出较强的非线性和不可预测性,给负荷预测带来了很大的困难。因此,利用气象因素进行短期负荷预测已成为提高智能电网应用水平不可或缺的因素。本文首先对电网负荷数据进行采集和分析,然后对异常负荷数据进行处理。其次,对温度、湿度、风速、日辐射、降雨量等气象因子进行逐一分析,得出负荷随温度、湿度的变化趋势;此外,这些气象因素是耦合的,并提出了一个综合的天气感知指数来表达上述因素对人体皮肤感知的影响。最后,以综合天气感知指标和负荷数据为输入,建立了负荷预测的BP神经网络模型,实际算例验证了该方法的准确性和快速性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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