A Similar Day Based Short Term Load Forecasting Method Using Wavelet Transform and LSTM

2021 ◽  
Author(s):  
Ruixuan Zhang ◽  
Chuyan Zhang ◽  
Miao Yu
Keyword(s):  
Wavelet Transform ◽  
Load Forecasting ◽  
Short Term ◽  
Forecasting Method ◽  
Short Term Load Forecasting ◽  
Similar Day

scholarly journals Short-Term Load Forecasting Algorithm Using a Similar Day Selection Method Based on Reinforcement Learning

Energies ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 2640 ◽  
Author(s):  
Rae-Jun Park ◽  
Kyung-Bin Song ◽  
Bo-Sung Kwon
Keyword(s):  
Reinforcement Learning ◽  
Predictive Accuracy ◽  
Learning Algorithm ◽  
Load Forecasting ◽  
Selection Method ◽  
Selection Model ◽  
Short Term ◽  
Short Term Load Forecasting ◽  
Similar Day ◽  
Reinforcement Learning Algorithm

Short-term load forecasting (STLF) is very important for planning and operating power systems and markets. Various algorithms have been developed for STLF. However, numerous utilities still apply additional correction processes, which depend on experienced professionals. In this study, an STLF algorithm that uses a similar day selection method based on reinforcement learning is proposed to substitute the dependence on an expert’s experience. The proposed algorithm consists of the selection of similar days, which is based on the reinforcement algorithm, and the STLF, which is based on an artificial neural network. The proposed similar day selection model based on the reinforcement learning algorithm is developed based on the Deep Q-Network technique, which is a value-based reinforcement learning algorithm. The proposed similar day selection model and load forecasting model are tested using the measured load and meteorological data for Korea. The proposed algorithm shows an improvement accuracy of load forecasting over previous algorithms. The proposed STLF algorithm is expected to improve the predictive accuracy of STLF because it can be applied in a complementary manner along with other load forecasting algorithms.

A Novel Hybrid Short-Term Load Forecasting Method of Smart Grid Using MLR and LSTM Neural Network

2020 ◽  
pp. 1-1 ◽  
Author(s):  
Jian Li ◽  
Daiyu Deng ◽  
Junbo Zhao ◽  
Dongsheng Cai ◽  
Weihao Hu ◽  
...  
Keyword(s):  
Neural Network ◽  
Smart Grid ◽  
Load Forecasting ◽  
Short Term ◽  
Forecasting Method ◽  
Short Term Load Forecasting
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