Big Data Analytics for Short and Medium-Term Electricity Load Forecasting Using an AI Techniques Ensembler
Abstract
:1. Introduction
- Hybrid of feature selection techniques; Extreme Gradient Boosting (XGB), Random Forest RF and Recursive Feature Elimination (RFE) techniques are applied to clean the huge amount of data.
- Two enhanced classifier techniques, Support Vector Machine with Grey Wolf Optimization (SVM-GWO) and Convolutional Neural Network Gated Recurrent Unit with Earth Worm Optimization (CNN-GRU-EWO) are proposed to forecast the electricity load.
- Grey Wolf Optimization (GWO) and Earth Worm Optimization (EWO) algorithms are used to tune the parameters of SVM and CNN-GRU, respectively.
- The parameters of classifiers are tuned to reduce the computational time efficiently.
- To overcome the overfitting problem, enhanced classifiers are used.
- Our proposed techniques are compared with some State Of The Art (SOTA) to prove the better performance of our enhanced techniques.
2. Related Work
3. Proposed System Model
3.1. Dataset Description
3.2. Feature Engineering
3.3. Classification and Forecasting
3.3.1. CNN-GRU-EWO
3.3.2. Gated Recurrent Unit (GRU)
3.3.3. SVM-GWO
4. Simulation Results
4.1. Average Feature Selection Based on RF and XGB
4.2. Classification and Forecasting Using SVM-GWO and CNN-GRU-EWO
5. Performance Metrics
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Abbreviations
CMI | Conditional Mutual Information |
NLSSVM | Nonlinear Least Square Support Vector Machine |
ABC | Artificial Bee Colony |
ARIMA | Autoregressive Integrated Moving Average |
IWNN | Wavelet Neural Network |
ELM | Extreme Learning Machine |
CNN | Convolutional Neural Network |
LSTM | Long Short Term Memory |
ASF | Auto Correlation Function |
IITK | India Institution of Technology Kanpoor |
ELM | Extreme Learning Machine |
XGB | Extreme Gradient Boosting |
DTC | Decision Tree Classifier |
MAE | Mean Absolute Error |
RMSE | Root Mean Square Error |
MSE | Mean Square Error |
MAPE | Mean Absolute Percentage Error |
PJM | Pennsylvania New Jersey Maryland |
CA | Correlation Analysis |
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Proposed Techniques | Objective | Dataset | Limitations |
---|---|---|---|
DA [13] | Reduce peak load | PJM | Issue in managing big data |
DLSTM [14] | Price and Load forecasting | ISO-NE | Cannot fulfill the requirement of real time data. |
CMI, NLSSVM [15,16] | Forecasting with important feature selection method | PJM | Less amount of data is taken into consideration |
GELM, IWNN [17] | Hourly price forecasting | PJM | Model complexity is considered |
CNN, LSTM [18,19] | Price forecasting | PJM | Redundancy in features are not considered |
DNN [20] | Load forecasting | Irish | Overfitting problem needed to improve |
DCNN [21] | Load forecasting of one day | Victoria | Limited use of dataset |
ESVM [22,23] | Short term load forecasting | ISO-NE | SVM is not good to deal big dataset because overfitting problem |
ANN [24] | Half hourly load forecasting | Tanzanian | Accuracy rates of their work are not satisfactory. |
MI, NN [25] | Short term forecasting | PJM | Maximize the penetration of renewable energy |
NARX, ARMAX [26] | Residential based short term load forecasting | IESCO | Model complexity increased |
GRU [27] | Load forecasting | PJM | Redundancy of features did not considered |
SVM, ANN [28] | Short term forecasting | IITK | Very small dataset is used for experiment |
ELM-K [29] | Short term forecasting | Southern China | Only one error metrics used for evaluation. |
CNN [20] | Short term forecasting | ISO-NE | Manually tuned the hyper parameters of proposed technique |
GRU-CNN [31] | Short term forecasting | Wuwei, Gansu province | Manually tuned the hyper parameters of proposed technique |
MI, ANN [32] | Day ahead load forecasting | DAYTOWN, AKPC | Feature selection need more improvement |
Target Feature | Features | Short Name | Dimension |
---|---|---|---|
System Load | Day-Ahead Cleared Demand | DA_Demand | TRUE |
Regulation Market Service clearing price | Reg_Capacity_Price | TRUE | |
Real-Time Demand | RT_Demand | TRUE | |
The dewpoint temperature | Dew_Point | FALSE | |
Day-Ahead Locational Marginal Price | DA_LMP | FALSE | |
The dry-bulb temperature | Dry_Bulb | FALSE | |
Energy Component of Day-Ahead | DA_EC | FALSE | |
Marginal Loss Component of Real-Time | RT_MLC | FALSE | |
Congestion Component of Day-Ahead | DA_CC | FALSE | |
Congestion Component of Real-Time | RT_CC | FALSE | |
Marginal Loss Component of Day-Ahead | DA_MLC | FALSE | |
Energy Component of Real-Time | RT_EC | TRUE | |
Real-Time Locational Marginal Price | RT_LMP | TRUE | |
Regulation Market Capacity clearing | Reg_Service_Price | FALSE |
Techniques | Performance Metrics | |||||||
---|---|---|---|---|---|---|---|---|
F1-Score | Accuracy | Precision | Recall | MAPE | RMSE | MAE | MSE | |
CNN_GRU_EWA | 95.23 | 96.33 | 94.00 | 94.62 | 6.00 | 7.00 | 10.00 | 13.00 |
LR | 75.88 | 78.35 | 76.56 | 76.98 | 20.00 | 23.00 | 27.00 | 26.00 |
ELM | 75.00 | 78.98 | 76.45 | 22.78 | 13.00 | 12.00 | 15.00 | 18.00 |
SVM | 87.88 | 87.99 | 86.91 | 85.99 | 1.79 | 12.30 | 10.50 | 12.00 |
SVM_GWO | 90.67 | 93.99 | 91.87 | 90.99 | 1.33 | 9.12 | 10.31 | 9.75 |
CNN | 88.66 | 89.00 | 90.00 | 88.76 | 10.00 | 12.00 | 15.00 | 18.00 |
Techniques and Tests | Correlation Tests | Parametric Statistical Hypothesis Tests | Non-Parametric Statistical Hypothesis Tests | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Pearson’s Test | Spearman’s Test | Kendalla’s Test | Chi- Squared Test | Student’s Test | Paired Student’s Test | ANOVA Test | Mann- Whitney Test | Wilcoxon Test | Kruskal Test | ||
SVM | F-stastistic | −0.0404 | −0.0549 | −0.0362 | 157,449.28 | −5.5019 | −5.3941 | 30 | 225,955 | 104,549 | 26.0883 |
p-value | 0.2753 | 0.1379 | 0.1429 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | |
SVM-GWO | F-stastistic | −0.0376 | −0.0553 | −0.0362 | 164,404.40 | 0.2530 | 0.2484 | 0.0640 | 262,798 | 132,003 | 0.2949 |
p-value | 0.3106 | 0.1349 | 0.1436 | 0.0000 | 0.8003 | 0.8039 | 0.8003 | 0.2936 | 0.8054 | 0.5871 | |
CNN | F-stastistic | 0.9964 | 0.9963 | 0.9499 | 575.09 | 1.1820 | 19.2812 | 1.3971 | 257,449 | 37,953 | 1.4537 |
p-value | 0.0000 | 0.0000 | 0.0000 | 1.0000 | 0.2374 | 0.0000 | 0.2374 | 0.1140 | 0.0000 | 0.2279 | |
CNN-GRU-EWA | F-stastistic | 0.7367 | 0.7208 | 0.5321 | 37,815.93 | −0.8087 | −1.4750 | 0.6539 | 267,085 | 131,225 | 0.0001 |
p-value | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.4188 | 0.1406 | 0.4188 | 0.4953 | 0.6555 | 0.9906 | |
ELM | F-stastistic | 0.9887 | 0.9856 | 0.9143 | 1865.32 | −0.1100 | −1.0303 | 0.0121 | 26,4803 | 124,235 | 0.0868 |
p-value | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.9124 | 0.3032 | 0.9124 | 0.3842 | 0.1538 | 0.7683 | |
LG | F-stastistic | 0.2411 | 0.2033 | 0.1415 | 89,538.00 | −6.0994 | −6.9077 | 37 | 218,561 | 94,749 | 36.3238 |
p-value | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
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Ayub, N.; Irfan, M.; Awais, M.; Ali, U.; Ali, T.; Hamdi, M.; Alghamdi, A.; Muhammad, F. Big Data Analytics for Short and Medium-Term Electricity Load Forecasting Using an AI Techniques Ensembler. Energies 2020, 13, 5193. https://doi.org/10.3390/en13195193
Ayub N, Irfan M, Awais M, Ali U, Ali T, Hamdi M, Alghamdi A, Muhammad F. Big Data Analytics for Short and Medium-Term Electricity Load Forecasting Using an AI Techniques Ensembler. Energies. 2020; 13(19):5193. https://doi.org/10.3390/en13195193
Chicago/Turabian StyleAyub, Nasir, Muhammad Irfan, Muhammad Awais, Usman Ali, Tariq Ali, Mohammed Hamdi, Abdullah Alghamdi, and Fazal Muhammad. 2020. "Big Data Analytics for Short and Medium-Term Electricity Load Forecasting Using an AI Techniques Ensembler" Energies 13, no. 19: 5193. https://doi.org/10.3390/en13195193