人工神经网络在萨拉热窝地区电力消耗预测中的应用

IF 1.6 4区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Electrical Engineering Pub Date : 2024-09-16 DOI:10.1007/s00202-024-02696-y
Lena Zec, Jovan Mikulović, Mileta Žarković
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引用次数: 0

摘要

本文介绍了一种利用人工神经网络(ANN)预测电力系统耗电量的创新方法。该方法在预测波斯尼亚和黑塞哥维那萨拉热窝地区的用电量时得到了验证。耗电量是以小时为单位的日前计划。2017 年至 2020 年的气温、风速和日照测量数据被用作拟议耗电量预测方法的输入变量。使用皮尔逊相关系数分析了这些输入变量对耗电量的影响。神经网络利用 2017 年至 2020 年的输入变量和用电量数据进行了训练,随后被应用于预测 2021 年的日前用电量。由于采用了输入变量更多的神经网络,与电力公司的预测相比,2021 年的用电量预测误差更小。因此,所提出的方法可作为更可靠的日前用电预测工具。此外,随着时间的推移,有关用电量和影响变量的历史数据将不断增加,预计将进一步提高利用方差网络进行用电量预测的可靠性。
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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Application of artificial neural network to power consumption forecasting for the Sarajevo region

This paper presents an innovative method for forecasting power consumption in the power system using an artificial neural network (ANN). The method was validated in the case of predicting power consumption for the Sarajevo region in Bosnia and Herzegovina. Power consumption is planned daily for the day-ahead with hourly resolution. Measured data on air temperature, wind speed, and insolation for 2017 to 2020 were utilized as input variables in the proposed power consumption forecasting method. The influence of these input variables on power consumption was analyzed using the Pearson correlation coefficient. The neural network underwent training with data on input variables and power consumption from 2017 to 2020 and was subsequently applied to forecast day-ahead power consumption for 2021. Due to the implementation of a neural network with a greater number of input variables, a smaller error in the power consumption forecast for 2021 was achieved compared to the forecast performed by the Electric Power Company. Therefore, the proposed method can be used as a more reliable tool for day-ahead power consumption forecasting. Additionally, the continual increase in the historical data on power consumption and influencing variables over time is expected to further enhance the reliability of power consumption forecasting using ANN.

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来源期刊
Electrical Engineering
Electrical Engineering 工程技术-工程:电子与电气
CiteScore
3.60
自引率
16.70%
发文量
0
审稿时长
>12 weeks
期刊介绍: The journal “Electrical Engineering” following the long tradition of Archiv für Elektrotechnik publishes original papers of archival value in electrical engineering with a strong focus on electric power systems, smart grid approaches to power transmission and distribution, power system planning, operation and control, electricity markets, renewable power generation, microgrids, power electronics, electrical machines and drives, electric vehicles, railway electrification systems and electric transportation infrastructures, energy storage in electric power systems and vehicles, high voltage engineering, electromagnetic transients in power networks, lightning protection, electrical safety, electrical insulation systems, apparatus, devices, and components. Manuscripts describing theoretical, computer application and experimental research results are welcomed. Electrical Engineering - Archiv für Elektrotechnik is published in agreement with Verband der Elektrotechnik Elektronik Informationstechnik eV (VDE).
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