Short-term Price Forecasting Method in Electricity Spot Markets Based on Attention-LSTM-mTCN

2022 ◽  
Author(s):  
Guang Yang ◽  
Songhuai Du ◽  
Qingling Duan ◽  
Juan Su
Keyword(s):  
Spot Markets ◽  
Price Forecasting ◽  
Short Term ◽  
Electricity Spot Markets ◽  
Forecasting Method

scholarly journals Short-Term Electricity Price Forecasting by Employing Ensemble Empirical Mode Decomposition and Extreme Learning Machine

Forecasting ◽  
2021 ◽  
Vol 3 (3) ◽  
pp. 460-477
Author(s):  
Sajjad Khan ◽  
Shahzad Aslam ◽  
Iqra Mustafa ◽  
Sheraz Aslam
Keyword(s):  
Extreme Learning Machine ◽  
Empirical Mode Decomposition ◽  
Electricity Market ◽  
Ensemble Empirical Mode Decomposition ◽  
Electricity Price ◽  
Price Forecasting ◽  
Short Term ◽  
Mode Decomposition ◽  
Electricity Price Forecasting ◽  
Learning Machine

Day-ahead electricity price forecasting plays a critical role in balancing energy consumption and generation, optimizing the decisions of electricity market participants, formulating energy trading strategies, and dispatching independent system operators. Despite the fact that much research on price forecasting has been published in recent years, it remains a difficult task because of the challenging nature of electricity prices that includes seasonality, sharp fluctuations in price, and high volatility. This study presents a three-stage short-term electricity price forecasting model by employing ensemble empirical mode decomposition (EEMD) and extreme learning machine (ELM). In the proposed model, the EEMD is employed to decompose the actual price signals to overcome the non-linear and non-stationary components in the electricity price data. Then, a day-ahead forecasting is performed using the ELM model. We conduct several experiments on real-time data obtained from three different states of the electricity market in Australia, i.e., Queensland, New South Wales, and Victoria. We also implement various deep learning approaches as benchmark methods, i.e., recurrent neural network, multi-layer perception, support vector machine, and ELM. In order to affirm the performance of our proposed and benchmark approaches, this study performs several performance evaluation metric, including the Diebold–Mariano (DM) test. The results from the experiments show the productiveness of our developed model (in terms of higher accuracy) over its counterparts.

A hybrid model for carbon price forecasting using GARCH and long short-term memory network

2021 ◽  
Vol 285 ◽  
pp. 116485
Author(s):  
Yumeng Huang ◽  
Xingyu Dai ◽  
Qunwei Wang ◽  
Dequn Zhou
Keyword(s):  
Hybrid Model ◽  
Short Term Memory ◽  
Carbon Price ◽  
Price Forecasting ◽  
Short Term ◽  
Term Memory ◽  
Memory Network ◽  
Long Short Term Memory
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