A High Precision Artificial Neural Networks Model for Short-Term Energy Load Forecasting
By Ping-Huan Kuo, Chiou-Jye Huang. 2018
296 citations recorded by the source corpus.
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.
Subfields: Building and Construction, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/664cd23788a216b5decbe66212bc17e94212bd37
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In Building and Construction
In Computer Vision and Pattern Recognition
In Electrical and Electronic Engineering
Citations in the corpus
- Deep Learning for Time Series Forecasting: The Electric Load Case (2019, methodology): “Still, several works tried to address the topic; in [15] a deep convolutional neural network model named DeepEnergy is presented.”
- A Short-Term Load Forecasting Method Using Integrated CNN and LSTM Network (2021, unknown): “(SVR) [11], fuzzy logic framework [12], artificial neural network (ANN) [13]–[16], radial basis functional network (RBFN) [17] and hybrid methods [18], [19].”