Short-Term Electricity Price Forecasting by Employing Ensemble Empirical Mode Decomposition and Extreme Learning Machine
Abstract
:1. Introduction
2. Methodology
2.1. Ensemble Empirical Mode Decomposition
2.2. Extreme Learning Machine
2.3. Combined EEMD-ELM Forecasting Model
3. Results and Simulations
3.1. Experimental Design
3.2. Dataset Description
3.3. Performance Evaluation Metric
3.4. Analysis of IMFs
3.5. Forecasting Result of NSW
3.6. Forecasting Result of QLD
3.7. Forecasting Result of VIC
4. Conclusions and Future Work
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
Abbreviations
AEMO | Australian energy market operator |
AI | Artificial intelligence |
ANN | Artificial neural network |
ANFIS | Adaptive network-based fuzzy inference system |
ARMA | Autoregressive moving average |
ARIMA | Autoregressive integrated moving average |
CI | Computational intelligence |
CNN | Convolutional neural network |
DL | Deep learning |
DM | Diebold–Mariano |
EEMD | Ensemble empirical mode decomposition |
ELM | Extreme learning machine |
ENN | Elman neural network |
GBM | Gradient boosting machine |
GRNN | Generalized regression neural network |
GRU | Gated recurrent unit |
IMF | Intrinsic mode functions |
KNN | K-nearest neighbor |
LASSO | least absolute shrinkage and selection operator |
LSSVM | Least squares support vector machine |
LSTM | Long short term memory |
MAE | Mean absolute error |
MAPE | Mean absolute percentage error |
ML | Machine learning |
MLP | Multi-layer perceptron |
MSE | Mean square error |
NN | Neural network |
NSW | New South Walves |
QLD | Queensland |
RBFN | Radial basis function network |
RMSE | Root mean square error |
RNN | Recurrent neural network |
SCA | Sine-cosine algorithm |
SSA | Singular spectrum analysis |
SVM | Support vector machine |
SVR | Support vector regression |
VIC | Victoria |
VMD | Variational mode decomposition |
WNN | Wavelet neural network |
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Ref. | Model | Description & Methodology | Study Area | Remarks |
---|---|---|---|---|
[20] | SVM, RF, KNN, Regression tree | The performance of different day-ahead electricity price forecasting algorithms was evaluated using data samples from Greece and Hungarian Power Industries, as well as the impact of different training sample sizes on forecasting performance and the impact of training on an hourly clustered sample. | Long-term | Using Hungry data, hourly clustered training models perform better, while hourly non-clustered training models are better for Greece data. |
[21] | DNN | The proposed methodology for day-ahead power price forecasting solves the hyper-parameter selection problem for DL implementations by establishing a robust ex-ante hyper-parameter selection mechanism. | Long-term | The proposed method reduces the noise and outperforms LASSO estimated model and DNN with non-optimized parameters. |
[22] | SEPNet (hybrid of VMD, CNN, and GRU) | In SEPNet, the VMD decomposes the complex time series of electricity price into IMFs with different center frequencies. The CNN is employed to extract the time domain features for all IMFs in the VMD domain. Then the GRU processes and learns the time domain features extracted by the CNN which leads to the final forecasting. | Short-term | It is observed that the proposed method has higher performance over VMD in terms of MAPE and RMSE by 84% and 81%, respectively. |
[23] | SDR-MASES- SPSD method | In the proposed model, the stacked pruning sparse denoising autoencoder (SPSDAE) is used to individually reduce the noise of the data. Then, to detect the features of the input data, a maximum separation subspace (MASES) in sufficient dimension reduction (SDR) is proposed. Finally, a new multimodal combined (MMC) method is introduced to accurately predict the day-ahead electricity price. | Short-term | It is confirmed by simulation that the proposed method achieves higher performance in terms of minimum error rate compared to benchmark methods. |
[24] | Bayesian models | In the proposed model, the Bayesian jump model is used along with the double exponential model and explanatory variables to detect upward jumps, no jumps, or downward jumps in electricity price. | Short-term | Results suggest that electricity jump predictions are useful for price prediction in peak hours. |
[25] | Hybrid of SSA and radial basis function NN (RBFNN) | In the proposed method, the SSA captures the trends and oscillatory components of the time series data, while the selection of input features for the NN is based on the correlation analysis. Then, the RBFNN is used for the final prediction based on real-time load and temperature data from New York City. | Short-term | The MAPE of the proposed method is minimum and is 4.73; SSA: 7.36, NN: 7.80, KNN: 10.22, and ARIMA: 13.26 |
[26] | Hybrid of Generalized ELM, wavelet NN, wavelet preprocessing, and bootstrapping | In the proposed model, bootstrapping is used to implement uncertainty and a generalized ELM is used for low computational cost and fast daily price prediction. In addition, to achieve a better fit of the prediction model to the changes in time series price, wavelet preprocessing is used. To confirm the productivity of the proposed model, real datasets from Ontario and Australia electricity markets are used for implementation. | Short-term | It is confirmed through simulations that the proposed model achieves higher prediction accuracy than its counterparts. |
[27] | GRU | The objective of this study is to evaluate the performance of different neural networks in predicting the price of electricity. | Short-term | Simulation results confirm the productivity, in terms of MAE, of their proposed multilayer GRU method. |
[28] | Complementary EEMD, ELM, Gaussian process, and SVM | In the proposed model, the complementary EEMD is used to decompose the current series into a number of subseries. The subseries are predicted using ELM, gradient boosting machine, Gaussian process, and SVM. The results are integrated to output the predicted electricity price | Short-term | The proposed model outperforms the benchmark algorithms in terms of error reduction. |
[29] | Seasonal ARIMA and ANN | In the proposed work, the current series is decomposed into two components: Linear and non-linear. The linear component is forecast using ARIMA, while the non-linear component is forecast using ANN | Short-term | The proposed model shows a 30 percent improvement in terms of error reduction in forecasting compared to benchmark models. |
[30] | Hybrid of wavelet transform, SAE and LSTM | Wavelet transform is used to decompose the current series. SAE -LSTM is used to forecast each series. Then the predicted series is reconstructed. The proposed hybrid algorithms overcome the shortcomings of wavelet transform and improve the price forecasting for residential, commercial, and industrial users using an optimal and stratified model. | Short-term | In terms of MAPE reduction, the performance of the proposed model is superior compared to other algorithms. |
[31] | Hybrid of cuckoo search, SVM, and SSA | In the proposed model, electricity price forecasting is performed by analyzing seasonal trends and patterns. Moreover, a hybrid feature selection algorithm is introduced to improve the electricity price forecasting. | Short-term | MAPE and RMSE of the proposed model along with DM are significantly lower compared to other benchmark models. |
[32] | RELM, VMD, and MO-SCA | An adaptive, deterministic, and probabilistic model is used for forecasting. A divide-and-conquer strategy is used to improve price forecasting. VMD is used to decompose the current series into a number of series and each series is forecast individually. | Short-term | MAE, RMSE, MAPE, and TIC of the proposed model is significantly lower compared to benchmark models. |
[33] | LSTM and Jaya optimization algorithm | In the proposed model, the Jaya optimization algorithm is used to tune the hyperparameters of LSTM to accurately forecast the electricity load and price. | Long-term | It is observed that the proposed model achieves low error rate over benchmark models. |
[34] | VMD GRRN, and gravity search optimization | In the proposed model, a mixed approach is proposed to predict electricity load and price. A hybrid of a neural network and gravity search optimization is developed for input selection to select important features. | Short-term | It is observed from results that RMSE and TIC values of the proposed model are lower than counterparts. |
State | Tested Models | SVR | MLP | RNN | EEMD-ELM |
---|---|---|---|---|---|
NSW | ELM | 1.04 | 1.87 | 1.89 | 3.22 |
SVR | −0.82 | 0.85 | 4.07 | ||
MLP | 0.02 | 3.25 | |||
RNN | 5.12 |
State | Tested Models | SVR | MLP | RNN | EEMD-ELM |
---|---|---|---|---|---|
QLD | ELM | 1.04 | 1.87 | 2.12 | 3.00 |
SVR | −0.82 | 1.07 | 4.07 | ||
MLP | 0.25 | 3.25 | |||
RNN | 5.12 |
State | Tested Models | SVR | MLP | RNN | EEMD-ELM |
---|---|---|---|---|---|
VIC | ELM | −0.56 | 0.16 | −0.84 | 4.10 |
SVR | −0.72 | −0.27 | 3.82 | ||
MLP | −1.00 | 3.09 | |||
RNN | 3.26 |
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Khan, S.; Aslam, S.; Mustafa, I.; Aslam, S. Short-Term Electricity Price Forecasting by Employing Ensemble Empirical Mode Decomposition and Extreme Learning Machine. Forecasting 2021, 3, 460-477. https://doi.org/10.3390/forecast3030028
Khan S, Aslam S, Mustafa I, Aslam S. Short-Term Electricity Price Forecasting by Employing Ensemble Empirical Mode Decomposition and Extreme Learning Machine. Forecasting. 2021; 3(3):460-477. https://doi.org/10.3390/forecast3030028
Chicago/Turabian StyleKhan, Sajjad, Shahzad Aslam, Iqra Mustafa, and Sheraz Aslam. 2021. "Short-Term Electricity Price Forecasting by Employing Ensemble Empirical Mode Decomposition and Extreme Learning Machine" Forecasting 3, no. 3: 460-477. https://doi.org/10.3390/forecast3030028