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Article

A High Precision Artificial Neural Networks Model for Short-Term Energy Load Forecasting

1
Computer and Intelligent Robot Program for Bachelor Degree, National Pingtung University, Pingtung 90004, Taiwan
2
School of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou 341000, Jiangxi, China
*
Author to whom correspondence should be addressed.
Energies 2018, 11(1), 213; https://doi.org/10.3390/en11010213
Received: 14 December 2017 / Revised: 6 January 2018 / Accepted: 9 January 2018 / Published: 16 January 2018
(This article belongs to the Special Issue Short-Term Load Forecasting by Artificial Intelligent Technologies)
One of the most important research topics in smart grid technology is load forecasting, because accuracy of load forecasting highly influences reliability of the smart grid systems. In the past, load forecasting was obtained by traditional analysis techniques such as time series analysis and linear regression. Since the load forecast focuses on aggregated electricity consumption patterns, researchers have recently integrated deep learning approaches with machine learning techniques. In this study, an accurate deep neural network algorithm for short-term load forecasting (STLF) is introduced. The forecasting performance of proposed algorithm is compared with performances of five artificial intelligence algorithms that are commonly used in load forecasting. The Mean Absolute Percentage Error (MAPE) and Cumulative Variation of Root Mean Square Error (CV-RMSE) are used as accuracy evaluation indexes. The experiment results show that MAPE and CV-RMSE of proposed algorithm are 9.77% and 11.66%, respectively, displaying very high forecasting accuracy. View Full-Text
Keywords: artificial intelligence; convolutional neural network; deep neural networks; short-term load forecasting artificial intelligence; convolutional neural network; deep neural networks; short-term load forecasting
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MDPI and ACS Style

Kuo, P.-H.; Huang, C.-J. A High Precision Artificial Neural Networks Model for Short-Term Energy Load Forecasting. Energies 2018, 11, 213. https://doi.org/10.3390/en11010213

AMA Style

Kuo P-H, Huang C-J. A High Precision Artificial Neural Networks Model for Short-Term Energy Load Forecasting. Energies. 2018; 11(1):213. https://doi.org/10.3390/en11010213

Chicago/Turabian Style

Kuo, Ping-Huan, and Chiou-Jye Huang. 2018. "A High Precision Artificial Neural Networks Model for Short-Term Energy Load Forecasting" Energies 11, no. 1: 213. https://doi.org/10.3390/en11010213

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