A High Precision Artificial Neural Networks Model for Short-Term Energy Load Forecasting
AbstractOne 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
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Kuo, P.-H.; Huang, C.-J. A High Precision Artificial Neural Networks Model for Short-Term Energy Load Forecasting. Energies 2018, 11, 213.
Kuo P-H, Huang C-J. A High Precision Artificial Neural Networks Model for Short-Term Energy Load Forecasting. Energies. 2018; 11(1):213.Chicago/Turabian Style
Kuo, Ping-Huan; Huang, Chiou-Jye. 2018. "A High Precision Artificial Neural Networks Model for Short-Term Energy Load Forecasting." Energies 11, no. 1: 213.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.