A Two-Stage Multistep-Ahead Electricity Load Forecasting Scheme Based on LightGBM and Attention-BiLSTM
Abstract
:1. Introduction
- We present a forecasting model that combines an ensemble learning method and an RNN for accurate MSA forecasting;
- We show that the performance of an MSA forecasting model can be further improved by considering the prediction result of a single-output forecasting model;
- The proposed model shows very stable forecasting accuracy over the entire forecasting horizon of 96 time points at 15 min intervals.
2. Related Works
3. Data Collection and Preprocessing
3.1. Weather Data
3.2. Calendar Information and Historical Electricity Load
4. Methodology
4.1. Single-Output Forecasting
4.1.1. LightGBM
4.1.2. Time Series Cross-Validation
4.2. Attention-BiLSTM Based MSA Forecasting
4.2.1. Bidirectional Long Short-Term Memory
4.2.2. Sequence-to-Sequence Recurrent Neural Networks
4.2.3. Attention Mechanism
5. Results and Discussion
5.1. Single-Output Forecasting Results
5.2. Multistep-Ahead Forecasting Results
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
ICT | information and communications technology |
STLF | Short-term load forecasting |
AI | artificial intelligence |
MSA | multistep-ahead |
RNN | recurrent neural network |
LSTM | long short-term memory |
GRU | gated recurrent unit |
S2S | sequence-to-sequence |
ANN | artificial neural network |
ARIMA | autoregressive integrated moving average |
MLR | multiple linear regression |
PCR | principal component regression |
PCC | Pearson correlation coefficient |
WCI | windchill index |
GBM | gradient boosting machine |
ReLU | rectified linear unit |
MAE | mean absolute error |
RMSE | root mean square error |
NN | neural network |
DARNN | dual-stage attention-based recurrent neural network |
ATT-GRU | attention-based gated recurrent unit |
LightGBM | light gradient boosting machine |
TSCV | Time series cross-validation |
BiLSTM | bidirectional long short-term memory |
ATT-BiLSTM | bidirectional long short-term memory with attention mechanism |
SVR | support vector regression |
RF | random forest |
FIR | fuzzy inductive reasoning |
MLP | multilayer perceptron |
CNN | convolutional neural network |
XGB | extreme gradient boosting |
ML | machine learning |
KMA | Korea meteorological administration |
DI | discomfort index |
GBDT | gradient boosting decision tree |
BA | Bahdanau attention mechanism |
Adam | adaptive moment estimation |
MAPE | mean absolute percentage error |
NRMSE | normalized root mean square error |
RICNN | recurrent inception convolution neural network |
DALSTM | dual-stage attentional long short-term memory |
COSMOS | combination of short-term load forecasting models using a stacking ensemble approach |
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Input Variable Identifier | Description (Type) | Input Variable Identifier | Description (Type) |
---|---|---|---|
No.01 | (numeric) | No.11 | (numeric) |
No.02 | (numeric) | No.12 | (numeric) |
No.03 | (numeric) | No.13 | (numeric) |
No.04 | (numeric) | No.14 | (numeric) |
No.05 | (numeric) | No.15 | (numeric) |
No.06 | (binary) | No.16 | (numeric) |
No.07 | (numeric) | No.17 | (numeric) |
No.08 | (numeric) | No.18 | (numeric) |
No.09 | (numeric) | No.19 | (numeric) |
No.10 | (numeric) |
Cluster A | Cluster B | Cluster C | Cluster D | |||||
---|---|---|---|---|---|---|---|---|
Training Set | Test Set | Training Set | Test Set | Training Set | Test Set | Training Set | Test Set | |
Mean | 656.499 | 586.247 | 623.109 | 670.012 | 302.790 | 322.904 | 515.166 | 489.349 |
Standard error | 0.954 | 1.418 | 0.587 | 0.929 | 0.205 | 0.323 | 0.35 | 0.515 |
Median | 553.4 | 462.7 | 543.8 | 593.3 | 292.3 | 311.8 | 478.2 | 447.3 |
Mode | 271.2 | 265.9 | 454.1 | 518.4 | 259.9 | 275.8 | 413.4 | 414.9 |
Standard deviation | 357.686 | 333.674 | 220.187 | 218.575 | 76.902 | 76.172 | 119.92 | 121.328 |
Sample variance | 127,939.5 | 111,338.3 | 48,482.45 | 47,775.23 | 5914.042 | 5802.298 | 14,380.87 | 14,720.47 |
Kurtosis | −0.721 | −0.758 | 0.352 | 0.434 | 0.375 | 0.233 | −0.597 | −0.225 |
Skewness | 0.676 | 0.736 | 1.076 | 1.096 | 0.689 | 0.716 | 0.673 | 0.871 |
Range | 1529.7 | 1350.2 | 1104 | 1017.1 | 541.8 | 476.9 | 586.8 | 556.5 |
Minimum | 195.4 | 181.4 | 296.2 | 383.2 | 114.1 | 130.7 | 300.6 | 292.5 |
Maximum | 1725.1 | 1531.6 | 1400.2 | 1400.3 | 655.9 | 607.6 | 887.4 | 849 |
Sum | 92,204,063 | 32,417,138 | 87,514,440 | 37,049,022 | 42,526,339 | 17,855,317 | 60,336,352 | 27,059,063 |
Count | 140,448 | 55,296 | 140,448 | 55,296 | 140,448 | 55,296 | 117,120 | 55,296 |
Model | Cluster A | Cluster B | Cluster C | Cluster D |
---|---|---|---|---|
LightGBM | Learning rate: | Learning rate: | Learning rate: | Learning rate: |
0.01, 0.05, 0.1 | 0.01, 0.05, 0.1 | 0.01, 0.05, 0.1 | 0.01, 0.05, 0.1 | |
No. of iterations: 500, 1000 | No. of iterations: 500, 1000 | No. of iterations: 500, 1000 | No. of iterations: 500, 1000 | |
No. of leaves: 64 | No. of leaves: 64 | No. of leaves: 64 | No. of leaves: 64 | |
Subsample: 0.5, 1.0 | Subsample: 0.5, 1.0 | Subsample: 0.5, 1.0 | Subsample: 0.5, 1.0 | |
XGBoost | Learning rate: 0.01, 0.05, 0.1 | Learning rate: 0.01, 0.05, 0.1 | Learning rate: 0.01, 0.05, 0.1 | Learning rate: 0.01, 0.05, 0.1 |
No. of iterations: 500, 1000 | No. of iterations: 500, 1000 | No. of iterations: 500, 1000 | No. of iterations: 500, 1000 | |
Subsample: 0.5, 1.0 | Subsample: 0.5, 1.0 | Subsample: 0.5, 1.0 | Subsample: 0.5, 1.0 | |
Colsample by tree: | Colsample by tree: | Colsample by tree: | Colsample by tree: | |
0.5, 1.0 | 0.5, 1.0 | 0.5, 1.0 | 0.5, 1.0 | |
NGBoost | No. of iterations: 500, 1000, 1500 | No. of iterations: 500, 1000, 1500 | No. of iterations: 500, 1000, 1500 | No. of iterations: 500, 1000, 1500 |
Random Forest | No. of trees: 64, 128 | No. of trees: 64, 128 | No. of trees: 64, 128 | No. of trees: 64, 128 |
Random state: 32, 64 | Random state: 32, 64 | Random state: 32, 64 | Random state: 32, 64 | |
MLP | No. of layers: 4, 5, 6, 7 | No. of layers: 4, 5, 6, 7 | No. of layers: 4, 5, 6, 7 | No. of layers: 4, 5, 6, 7 |
Activation function: ReLU | Activation function: ReLU | Activation function: ReLU | Activation function: ReLU | |
Optimizer: Adam | Optimizer: Adam | Optimizer: Adam | Optimizer: Adam | |
Learning rate: 0.001 | Learning rate: 0.001 | Learning rate: 0.001 | Learning rate: 0.001 |
Evaluation Metric | Model | Cluster A | Cluster B | Cluster C | Cluster D |
---|---|---|---|---|---|
MAPE (%) | LightGBM | 7.01 | 4.74 | 6.98 | 4.98 |
MLP | 12.06 | 7.03 | 7.61 | 5.67 | |
RF | 7.53 | 4.98 | 7.24 | 5.38 | |
XGBoost | 7.22 | 5.12 | 7.52 | 5.29 | |
NGBoost | 9.64 | 5.44 | 7.73 | 5.90 | |
MAE (kWh) | LightGBM | 40.71 | 32.00 | 22.18 | 23.47 |
MLP | 68.59 | 45.95 | 23.10 | 27.30 | |
RF | 43.81 | 34.09 | 23.46 | 25.49 | |
XGBoost | 41.81 | 32.81 | 24.76 | 24.81 | |
NGBoost | 54.25 | 37.04 | 24.85 | 29.21 | |
RMSE (kWh) | LightGBM | 61.31 | 49.01 | 30.81 | 36.26 |
MLP | 119.07 | 75.39 | 32.5 | 41.11 | |
RF | 67.2 | 54.73 | 32.89 | 40.51 | |
XGBoost | 63.58 | 48.34 | 34.39 | 37.16 | |
NGBoost | 82.05 | 59.1 | 34.43 | 44.17 | |
NRMSE (%) | LightGBM | 9.37 | 7.63 | 9.78 | 7.25 |
MLP | 18.91 | 11.74 | 10.32 | 8.22 | |
RF | 10.67 | 8.52 | 10.45 | 8.10 | |
XGBoost | 10.09 | 7.53 | 10.92 | 7.43 | |
NGBoost | 13.03 | 9.21 | 10.93 | 8.83 |
Cluster A | Cluster B | Cluster C | Cluster D |
---|---|---|---|
0.984 | 0.978 | 0.923 | 0.957 |
Model | Package | Selected Hyperparameters |
---|---|---|
LightGBM | LightGBM Scikit-learn | Learning rate: 0.05 |
No. of iterations: 1000 | ||
No. of leaves: 32 | ||
Subsample: 0.5 | ||
Random Forest | Scikit-learn | No. of trees: 128 |
Random state: 64 | ||
S2S BiLSTM | Pytorch | No. of hidden nodes: 15 |
No. of hidden layers: 2 | ||
Activation function: ReLU | ||
Optimizer: Adam | ||
Learning rate: 0.001 | ||
No. of epochs: 350 | ||
S2S ATT-BiLSTM | Pytorch | No. of hidden nodes: 15 |
No. of hidden layers: 2 | ||
Activation function: ReLU | ||
Optimizer: Adam | ||
Learning rate: 0.001 | ||
No. of epochs: 350 | ||
ATT-GRU [30] | Pytorch | No. of hidden nodes: 15 |
No. of hidden layers: 2 | ||
Activation function: SELU | ||
Optimizer: Adam | ||
Learning rate: 0.001 | ||
No. of epochs: 150 | ||
DALSTM [33] Stage 1: LSTM Stage 2: DARNN | Pytorch | LSTM |
No. of hidden nodes: 15 | ||
No. of hidden layers: 2 | ||
Activation function: ReLU | ||
Optimizer: Adam | ||
Learning rate: 0.001 | ||
No. of epochs: 350 | ||
DARNN | ||
No. of hidden nodes: 64 | ||
Time steps: 96 | ||
Optimizer: Adam | ||
Learning rate: 0.001 | ||
No. of epochs: 150 | ||
COSMOS [34] Stage 1: MLP Stage 2: PCR | Scikit-learn | MLP |
No. of hidden nodes: 15 | ||
No. of hidden layers: 4, 5, 6, 7 | ||
Activation function: ReLU | ||
Optimizer: Adam | ||
Learning rate: 0.001 | ||
No. of epochs: 150 | ||
PCR | ||
Principal components: 1 | ||
Sliding window size: 672 |
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Share and Cite
Park, J.; Hwang, E. A Two-Stage Multistep-Ahead Electricity Load Forecasting Scheme Based on LightGBM and Attention-BiLSTM. Sensors 2021, 21, 7697. https://doi.org/10.3390/s21227697
Park J, Hwang E. A Two-Stage Multistep-Ahead Electricity Load Forecasting Scheme Based on LightGBM and Attention-BiLSTM. Sensors. 2021; 21(22):7697. https://doi.org/10.3390/s21227697
Chicago/Turabian StylePark, Jinwoong, and Eenjun Hwang. 2021. "A Two-Stage Multistep-Ahead Electricity Load Forecasting Scheme Based on LightGBM and Attention-BiLSTM" Sensors 21, no. 22: 7697. https://doi.org/10.3390/s21227697