Short-Term Seasonal Energy Forecasting

被引：0
|
作者：
Kedrowski, Reilly [1 ]
Nelson, Justin [1 ]
Nair, Arun Sukumaran [1 ]
Ranganathan, Prakash [1 ]
机构：
[1] Univ North Dakota, Dept Elect Engn & Mines, Grand Forks, ND 58202 USA
来源：
2018 IEEE INTERNATIONAL CONFERENCE ON ELECTRO/INFORMATION TECHNOLOGY (EIT) | 2018年
关键词：
Load Forecasting; ARIMA; Exponential Smoothing; Neural Networks; TensorFlow;
D O I：
暂无
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
Load Forecasting is an important tool used to predict future electrical utility data in a Smart Grid Forecasting uses past data to predict trends for future energy usage. This paper investigates the application of exponential smoothing, ARIMA and recursive neural networks in TensorFlow on utility data taken from 20 different US Utility zones over four years, from 2004-2007. These methods will be used to forecast and predict the load data for each yearly quarter in 2008.
引用
收藏
页码：696 / 700
页数：5
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