Short-term electric power load forecasting using factor analysis and long short-term memory for smart cities,International Journal of Circuit Theory and Applications

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Short-term electric power load forecasting using factor analysis and long short-term memory for smart cities
International Journal of Circuit Theory and Applications ( IF 1.8 ) Pub Date : 2020-12-30 , DOI: 10.1002/cta.2928
Venkataramana Veeramsetty ₁ , D. Rakesh Chandra ₂ , Surender Reddy Salkuti ₃

Affiliation

Electric load estimation is an important activity for electrical power system operators to operate the system stably and optimally. This paper develops a machine learning model with a long short-term memory and a factor analysis to predict the load at a specific hour of the day on an electrical power substation. Historical load data from the 33-/11-kV substation near Kakatiya University in Warangal are taken at each hour of the day for the period from September 2018 to November 2018. A new long short-term memory architecture with factor analysis is being designed based on the approach used to predict substation loads by simulation in Microsoft Azure Notebooks. Based on the study, it was found that the proposed design predicts loads with good accuracy.

中文翻译：

基于因子分析和长短期记忆的智慧城市短期电力负荷预测

电力负荷估计是电力系统运营商稳定、优化运行系统的一项重要活动。本文开发了一种具有长短期记忆和因子分析的机器学习模型，以预测变电站一天中特定时间的负载。从 2018 年 9 月到 2018 年 11 月期间，瓦朗加尔卡卡蒂亚大学附近 33-/11-kV 变电站的历史负荷数据是在一天中的每个小时采集的。在 Microsoft Azure Notebooks 中通过模拟来预测变电站负荷的方法。基于研究，发现所提出的设计能够以良好的准确度预测载荷。

更新日期：2020-12-30

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