BRIM: An Accurate Electricity Spot Price Prediction Scheme-Based Bidirectional Recurrent Neural Network and Integrated Market
Abstract
:1. Introduction
1.1. Research Motivation
1.2. Main Contributions
- Exploiting the idea of market integration (i.e., neighboring markets play a role in improving price forecasting accuracy) and utilizing the earlier announced electricity price of another inter-connected electricity market as the future price indicator of the market to be predicted;
- Designing the bidirectional LSTM network structure, in which the historical price data are input into the conventional LSTM (forward LSTM), and the future price data are used for two purposes: one as the input features to the backward LSTM, and another as the input features to the forward LSTM.
- Thorough experiments and comparisons with typical existing schemes are conducted to verify the prediction performance of our proposal, in terms of MAE (mean absolute error) and sMAPE (symmetric mean absolute percentage error). Furthermore, the Diebold-Mariano (DM) test is used to show the difference of our proposed BRIM model from other forecasting models, and indirectly demonstrates our forecasting model’s statistical significance.
2. Related Work
2.1. Market Integration
2.2. Deep Learning-Based Prediction Models
3. Proposed BRIM Prediction Scheme
3.1. The LSTM Model
3.2. The BRIM Framework
4. Performance Evaluation
4.1. Dataset Description
- The past prices in the EPEX-FR (France), representing past information.
- The day-ahead prices in the EXAA, representing future information.
- Training set (from 26 December 2011 to 21 September 2016) is used to train forecasting models.
- Validation set (from 22 September 2016 to 21 September 2017) is used for model selection.
- Test set (from 22 September 2017 to 21 September 2018) is used to evaluate the model.
4.2. Experimental Setup
4.3. Benchmark Schemes
- Unidirectional LSTM model using only past price sequences in the forward time direction, containing 41 neurons. It is named as Uni-LSTM1 in our experiments.
- Unidirectional LSTM model using both past and future prices in the positive/forward time direction, containing 47 neurons. It is named as Uni-LSTM2 in our experiments.
- DNN model proposed by [22]. This model is a simple extension to the traditional MLP (Multi-layer Perception), containing 239 in the first layer and 162 neurons in second hidden layers. It is named as DNN in our experiments.
- LSTM-DNN model proposed by [13], containing 92 and 41 neurons in the DNN layer and LSTM layer, respectively. In detail, the past prices are processed by a forward LSTM, while the day-ahead prices representing future information are input into a DNN. Then, the outputs from these two separate networks are concatenated to determine the final prediction. It is named as LSTM-DNN in our experiments.
- Persistent model, persistent EXAA-based model, and Univariate AR() proposed by [20], respectively, abbreviated as naïve, naïve-EXAA, and AR() in our experiments. In naïve mode, the electricity price is estimated to be the same as 168 h ago (usually representing one week). In naïve-EXAA model, since the EXAA day-ahead prices are released at an earlier point in time, it simply regards that the day-ahead electricity prices in the EPEX are the same as that in the EXAA. AR() is the autoregressive process of order , which is selected by minimizing the Akaike information criterion.
4.4. Experimental Results and Analysis
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Data Division | Mean | Median | MAD 1 | Std | Min | Max |
---|---|---|---|---|---|---|
Training set | 38.95 | 37.79 | 13.19 | 22.59 | −200.00 | 1938.50 |
Validation set | 45.70 | 40.76 | 15.34 | 26.01 | −2.17 | 874.01 |
Test set | 48.16 | 47.84 | 13.38 | 17.67 | −31.82 | 195.11 |
Hyper-Parameter | Value |
---|---|
Activation function | Tanh |
Dropout | No |
Regularization | No |
Number of neurons | 47 (both forward and backward) |
Sequence length | 1 week of past prices (previous 168 h of the EPEX) + 1 day of day-ahead prices (24 h of the EXAA) |
Contrast Schemes | Uni-LSTM2 and BRIM | AR() and BRIM | Uni-LSTM1 and BRIM | DNN and BRIM | Naïve and BRIM | LSTM-DNN and BRIM | Naïve-EXAA and BRIM | Uni-LSTM1 and Uni-LSTM2 | DNN and Uni-LSTM2 |
---|---|---|---|---|---|---|---|---|---|
DM_A 1 | 6.59075 | 4.88679 | 6.58444 | 8.30636 | 6.7 | 8.49038 | 7.43447 | 4.52153 | 5.64248 |
-value_A | |||||||||
DM_S 1 | 2.8479 | 3.29291 | 4.29782 | 5.01277 | 5.14705 | 6.67746 | 6.15096 | 3.81107 | 3.74367 |
-value_S |
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Share and Cite
Chen, Y.; Wang, Y.; Ma, J.; Jin, Q. BRIM: An Accurate Electricity Spot Price Prediction Scheme-Based Bidirectional Recurrent Neural Network and Integrated Market. Energies 2019, 12, 2241. https://doi.org/10.3390/en12122241
Chen Y, Wang Y, Ma J, Jin Q. BRIM: An Accurate Electricity Spot Price Prediction Scheme-Based Bidirectional Recurrent Neural Network and Integrated Market. Energies. 2019; 12(12):2241. https://doi.org/10.3390/en12122241
Chicago/Turabian StyleChen, Yiyuan, Yufeng Wang, Jianhua Ma, and Qun Jin. 2019. "BRIM: An Accurate Electricity Spot Price Prediction Scheme-Based Bidirectional Recurrent Neural Network and Integrated Market" Energies 12, no. 12: 2241. https://doi.org/10.3390/en12122241
APA StyleChen, Y., Wang, Y., Ma, J., & Jin, Q. (2019). BRIM: An Accurate Electricity Spot Price Prediction Scheme-Based Bidirectional Recurrent Neural Network and Integrated Market. Energies, 12(12), 2241. https://doi.org/10.3390/en12122241