Exploiting Artificial Neural Networks for the Prediction of Ancillary Energy Market Prices
Abstract
:1. Introduction
- identify what sources of data are relevant and openly available for the predictions of the FCR-N ancillary service market.
- identify and present a methodology that can be utilized for the prediction of ancillary market prices and the key design decisions to be made, highlighting the differences between ancillary market (such as FCR-N) and spot market prices’ prediction, as well as employing the Artificial Neural Network (ANN) model in which numerous hyper-parameters are to be tuned for the ANN, with no prior work existing for ancillary service price prediction with ANN.
- evaluate the prediction performance of the FCR-N price. The experimental results show that the proposed ANN model was capable of adapting to the fast-changing price patterns of the FCR-N market. Moreover, the ANN outperforms the two state of the art models, Support Vector Regression (SVR) and the ARIMA model, in the prediction of the FCR-N prices.
2. Related Work
3. Problem Analysis
3.1. Data Collection
3.2. FCR-N Price Analysis
3.3. Autocorrelation and Variable Lag
4. Methodology
4.1. Prediction Model Formulation
4.2. Data Preprocessing
4.3. Model Validation
4.4. Prediction Performance Evaluation
4.5. Empirical Configuration of an Artificial Neural Network
5. Empirical Results and Discussion
5.1. Determining the Training Window Size
5.2. Prediction Performance Analysis
5.3. Comparison with the State of the Art
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Abbreviations
aFRR | Automatic Frequency Restoration Reserve |
ANN | Artificial Neural Network |
ARIMA | AutoRegressive Integrated Moving Average |
DER | Distributed Energy Resources |
DR | Demand Response |
EV | Electric Vehicle |
FCR | Frequency Containment Reserve |
FCR-D | Frequency Containment Reserve for Disturbance |
FCR-N | Frequency Containment Reserve for Normal operation |
MIMO | Multi-Input Multi-Output |
MSE | Mean Squared Error |
RNN | Recurrent Neural Network |
SGD | Stochastic Gradient Descent |
SVR | Support Vector Regression |
V2B | Vehicle-to-Building |
V2H | Vehicle-to-Home |
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Market | Minimum Bid | Activation Time | Activation Frequency | How Often It Is Activated |
---|---|---|---|---|
FCR-N | 0.1 MW | 3 min | Fully after a frequency step change of ± 0.1 Hz, max deadband ±0.05 Hz | Several times a day |
Category Name | # | Data Source |
---|---|---|
FCR market data | 5 | Fingrid [58] |
Electricity Import/Export | 12 | Fingrid [58] |
Electricity Load | 2 | Fingrid [58] |
Electricity Generation | 12 | Fingrid [58] and Energia.fi [59] |
Day-ahead Elspot Prices | 1 | Nord Pool [60] |
Oil Prices | 1 | |
Weather | 26 | Finnish Meteorological Institute [61] |
Calendar | 5 | |
Total | 64 |
Mean | 19.560 | Skewness | 89.335 |
Median | 17.725 | Kurtosis | 37.686 |
Min | 0.0 | Jarque–Bera | 1,065,031.561 |
Max | 500.0 | % 0-value | 29.10% |
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Giovanelli, C.; Sierla, S.; Ichise, R.; Vyatkin, V. Exploiting Artificial Neural Networks for the Prediction of Ancillary Energy Market Prices. Energies 2018, 11, 1906. https://doi.org/10.3390/en11071906
Giovanelli C, Sierla S, Ichise R, Vyatkin V. Exploiting Artificial Neural Networks for the Prediction of Ancillary Energy Market Prices. Energies. 2018; 11(7):1906. https://doi.org/10.3390/en11071906
Chicago/Turabian StyleGiovanelli, Christian, Seppo Sierla, Ryutaro Ichise, and Valeriy Vyatkin. 2018. "Exploiting Artificial Neural Networks for the Prediction of Ancillary Energy Market Prices" Energies 11, no. 7: 1906. https://doi.org/10.3390/en11071906
APA StyleGiovanelli, C., Sierla, S., Ichise, R., & Vyatkin, V. (2018). Exploiting Artificial Neural Networks for the Prediction of Ancillary Energy Market Prices. Energies, 11(7), 1906. https://doi.org/10.3390/en11071906