Advanced ML-Based Ensemble and Deep Learning Models for Short-Term Load Forecasting: Comparative Analysis Using Feature Engineering
Abstract
:1. Introduction
1.1. Prior Works
1.2. Contribution
- Bagging ensemble consisting of LR and SVR is firstly proposed to improve the forecasting accuracy by converting ML models from weak learners to strong learners.
- Advanced DL models including DNN, LSTM, and CNN-LSTM are implemented along with tuning hyperparameters for STLF to handle back-propagation learning and time series problems.
- A detailed comparative analysis of the proposed model and other DL models is provided and compared each other. The comparison is done by considering the mean absolute percentage error (MAPE), the mean absolute error (MAE), and the mean squared error (MSE) as the main performance metrics. These performance metrics are computed for the provided dataset for every month.
- The used data in this work are obtained from EGAT and are first smoothed using the filtering technique. The filtering process is done to avoid missing values and outliers.
- Different input features are applied for all models and are compared to check the correlation between load and external influential factors, because external factors like temperature, holidays, and months of the year commonly affect load demand.
1.3. Paper Organization
2. Methodology
2.1. Data Pre-Processing Module
2.1.1. Data Cleaning
2.1.2. Data Segmentation
2.1.3. Selection of Input Features
2.2. Training Module
2.2.1. Bagging Ensemble Training Process
2.2.2. LSTM Training Process
2.2.3. CNN-LSTM Training Process
2.2.4. DNN Training Process
2.3. Forecasting Module
3. Results and Discussions
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Inputs | Target | ||||||
No. | |||||||
Training Dataset | 1 | 04/01/19 (Fri) | 10/01/19 (Thur) | 10/01/19 (Thur) | 10/01/19 (Thur) | 0.98 | 11/01/19 (Fri) |
. | . | . | . | . | . | . | |
. | . | . | . | . | . | . | |
. | . | . | . | . | . | . | |
104 | 11/12/20 (Fri) | 17/12/20 (Thur) | 17/12/20 (Thur) | 17/12/20 (Thur) | 0.99 | 18/12/20 (Fri) | |
Inputs | Target | ||||||
No. | |||||||
Testing Dataset | 1 | 25/12/20 (Fri) | 31/12/20 (Thur) | 31/12/20 (Thur) | 31/12/20 (Thur) | 1.02 | 01/01/21 (Fri) |
LSTM | CNN+LSTM | DNN | Bagging Ensemble Model | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
MAPE (%) | MAE (MW) | MSE (GW) | MAPE (%) | MAE (MW) | MSE (GW) | MAPE (%) | MAE (MW) | MSE (GW) | MAPE (%) | MAE (MW) | MSE (GW) | |
Jan | 13.08 | 2235.17 | 9070.42 | 12.88 | 2185.09 | 8397.09 | 13.72 | 2359.98 | 9774.87 | 12.98 | 2093.38 | 8930.46 |
Feb | 4.84 | 1032.30 | 1496.87 | 4.73 | 1013.17 | 1579.03 | 4.76 | 1014.28 | 1456.72 | 3.29 | 680.38 | 813.99 |
Mar | 5.97 | 1445.93 | 2734.75 | 6.58 | 1594.10 | 3330.44 | 6.09 | 1472.09 | 2827.14 | 6.48 | 1569.43 | 2960.72 |
Apr | 12.68 | 2652.65 | 11,081.15 | 13.12 | 2746.25 | 11,586.12 | 12.71 | 2663.27 | 11,132.87 | 9.94 | 2130.98 | 7068.72 |
May | 7.46 | 1747.65 | 4822.34 | 7.14 | 1664.28 | 4675.03 | 7.34 | 1728.00 | 4822.21 | 7.33 | 1756.00 | 4480.45 |
Jun | 4.92 | 1174.47 | 2056.87 | 5.14 | 1227.47 | 2269.68 | 4.95 | 1181.06 | 2106.70 | 4.88 | 1180.28 | 1970.48 |
Jul | 5.22 | 1128.93 | 2049.82 | 5.12 | 1111.66 | 1994.53 | 5.08 | 1099.24 | 1980.59 | 4.17 | 907.72 | 1406.96 |
Aug | 5.35 | 1201.36 | 2214.92 | 5.25 | 1179.41 | 2120.09 | 5.34 | 1197.99 | 2212.39 | 4.17 | 928.65 | 1246.86 |
Sep | 3.43 | 755.24 | 930.58 | 4.05 | 893.08 | 1239.53 | 3.40 | 748.40 | 905.92 | 2.36 | 512.57 | 421.70 |
Oct | 4.47 | 977.75 | 1439.40 | 4.54 | 996.56 | 1455.05 | 4.59 | 1004.31 | 1503.78 | 3.16 | 681.71 | 754.51 |
Nov | 4.74 | 1040.95 | 1422.87 | 4.92 | 1082.02 | 1569.09 | 4.85 | 1065.46 | 1478.61 | 3.88 | 844.52 | 928.57 |
Dec | 8.49 | 1534.56 | 5032.24 | 8.51 | 1545.56 | 5016.54 | 8.39 | 1518.97 | 4857.91 | 9.62 | 1754.26 | 5920.08 |
Average | 6.74 | 1413.74 | 3712.15 | 6.85 | 1439.48 | 3783.01 | 6.79 | 1424.50 | 3772.21 | 6.05 | 1258.98 | 3099.11 |
LSTM | CNN+LSTM | DNN | Bagging Ensemble Model | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
MAPE (%) | MAE (MW) | MSE (GW) | MAPE (%) | MAE (MW) | MSE (GW) | MAPE (%) | MAE (MW) | MSE (GW) | MAPE (%) | MAE (MW) | MSE (GW) | |
Jan | 13.47 | 2302.89 | 9434.34 | 12.82 | 2169.25 | 8326.74 | 13.66 | 2346.44 | 9702.55 | 13.30 | 2156.66 | 9328.55 |
Feb | 4.94 | 1052.54 | 1555.61 | 4.71 | 1008.24 | 1569.31 | 4.74 | 1010.59 | 1445.70 | 4.08 | 849.31 | 1041.12 |
Mar | 5.64 | 1363.73 | 2474.69 | 6.57 | 1592.86 | 3317.03 | 6.05 | 1463.81 | 2793.74 | 6.17 | 1491.13 | 2771.64 |
Apr | 12.85 | 2685.20 | 11,407.45 | 13.09 | 2737.26 | 11,567.10 | 12.68 | 2655.50 | 11,063.96 | 9.36 | 2017.90 | 6383.87 |
May | 7.38 | 1730.76 | 4733.29 | 7.15 | 1666.79 | 4664.40 | 7.33 | 1725.31 | 4803.78 | 7.60 | 1818.32 | 4736.45 |
Jun | 4.86 | 1156.05 | 2005.84 | 5.13 | 1225.57 | 2254.33 | 4.94 | 1180.03 | 2092.43 | 4.79 | 1155.41 | 1900.14 |
Jul | 5.40 | 1165.50 | 2172.26 | 5.11 | 1108.74 | 1985.82 | 5.07 | 1097.75 | 1973.20 | 4.30 | 933.69 | 1472.71 |
Aug | 5.22 | 1169.25 | 2168.62 | 5.23 | 1175.00 | 2109.25 | 5.33 | 1197.39 | 2206.47 | 4.09 | 911.47 | 1195.68 |
Sep | 3.34 | 731.34 | 859.70 | 4.04 | 891.51 | 1233.88 | 3.38 | 744.35 | 898.31 | 2.44 | 529.81 | 462.55 |
Oct | 4.40 | 958.21 | 1431.91 | 4.53 | 995.41 | 1452.99 | 4.57 | 1001.54 | 1500.10 | 3.28 | 711.41 | 794.38 |
Nov | 4.64 | 1014.20 | 1358.34 | 4.92 | 1081.30 | 1565.14 | 4.85 | 1066.07 | 1476.86 | 3.91 | 855.13 | 903.18 |
Dec | 8.68 | 1568.35 | 5228.79 | 8.50 | 1542.71 | 5014.64 | 8.36 | 1513.71 | 4840.41 | 9.33 | 1699.77 | 5656.51 |
Average | 6.75 | 1411.22 | 3751.94 | 6.83 | 1435.82 | 3768.63 | 6.77 | 1420.27 | 3750.29 | 6.08 | 1265.55 | 3077.47 |
LSTM | CNN+LSTM | DNN | Bagging Ensemble Model | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
MAPE (%) | MAE (MW) | MSE (GW) | MAPE (%) | MAE (MW) | MSE (GW) | MAPE (%) | MAE (MW) | MSE (GW) | MAPE (%) | MAE (MW) | MSE (GW) | |
Jan | 13.86 | 2365.26 | 10,111.87 | 13.29 | 2269.25 | 9485.31 | 13.65 | 2344.11 | 9724.30 | 15.34 | 2564.18 | 10,180.74 |
Feb | 4.87 | 1034.95 | 1515.81 | 4.65 | 995.74 | 1534.25 | 4.71 | 1003.79 | 1427.19 | 5.88 | 1213.62 | 2052.17 |
Mar | 5.43 | 1311.84 | 2321.53 | 6.49 | 1572.13 | 3250.22 | 5.98 | 1445.96 | 2745.80 | 4.01 | 982.23 | 1392.94 |
Apr | 12.95 | 2698.69 | 11,588.90 | 13.15 | 2749.12 | 11,644.37 | 12.74 | 2667.98 | 11,151.51 | 7.93 | 1735.60 | 4007.19 |
May | 7.42 | 1739.76 | 4787.04 | 7.18 | 1671.46 | 4699.33 | 7.33 | 1724.78 | 4804.21 | 7.00 | 1663.69 | 4013.99 |
Jun | 4.87 | 1156.13 | 2067.77 | 5.14 | 1229.19 | 2272.48 | 4.94 | 1180.86 | 2102.16 | 4.72 | 1141.76 | 1934.57 |
Jul | 5.63 | 1215.65 | 2372.88 | 5.13 | 1114.11 | 2005.08 | 5.09 | 1101.11 | 1989.94 | 3.68 | 812.37 | 1050.80 |
Aug | 5.17 | 1155.53 | 2170.30 | 5.26 | 1182.95 | 2129.16 | 5.35 | 1201.38 | 2225.85 | 4.33 | 967.23 | 1347.89 |
Sep | 3.35 | 733.56 | 878.89 | 4.09 | 903.07 | 1259.64 | 3.40 | 749.55 | 909.87 | 2.39 | 522.24 | 441.59 |
Oct | 4.45 | 966.99 | 1479.57 | 4.57 | 1004.25 | 1472.86 | 4.61 | 1009.43 | 1514.38 | 4.13 | 907.69 | 1252.86 |
Nov | 4.67 | 1021.29 | 1381.03 | 4.95 | 1088.48 | 1585.90 | 4.88 | 1073.17 | 1499.20 | 4.13 | 915.09 | 1122.71 |
Dec | 8.86 | 1597.99 | 5527.36 | 8.50 | 1544.94 | 4965.67 | 8.36 | 1513.95 | 4818.58 | 7.22 | 1317.83 | 3443.50 |
Average | 6.82 | 1419.76 | 3868.02 | 6.89 | 1446.87 | 3874.16 | 6.77 | 1421.41 | 3759.89 | 5.91 | 1230.39 | 2700.84 |
LSTM | CNN+LSTM | DNN | Bagging Ensemble Model | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
MAPE (%) | MAE (MW) | MSE (GW) | MAPE (%) | MAE (MW) | MSE (GW) | MAPE (%) | MAE (MW) | MSE (GW) | MAPE (%) | MAE (MW) | MSE (GW) | |
Jan | 13.06 | 2222.83 | 9319.77 | 13.31 | 2278.28 | 9906.45 | 13.61 | 2336.18 | 9664.02 | 10.71 | 1771.09 | 5929.37 |
Feb | 4.60 | 977.39 | 1362.42 | 4.73 | 1012.83 | 1575.91 | 4.70 | 1001.53 | 1421.40 | 4.39 | 905.93 | 1309.20 |
Mar | 5.28 | 1276.51 | 2283.40 | 6.60 | 1600.12 | 3342.88 | 5.98 | 1445.98 | 2741.79 | 4.01 | 993.48 | 1511.03 |
Apr | 13.49 | 2847.43 | 12,129.30 | 13.07 | 2733.13 | 11,532.10 | 12.70 | 2659.44 | 11,102.30 | 9.97 | 2142.93 | 6530.93 |
May | 7.17 | 1677.72 | 4506.97 | 7.16 | 1669.31 | 4681.90 | 7.32 | 1723.51 | 4796.36 | 7.10 | 1677.81 | 4510.27 |
Jun | 4.93 | 1170.28 | 2077.95 | 5.11 | 1222.13 | 2246.29 | 4.93 | 1177.09 | 2088.14 | 4.75 | 1138.79 | 2089.53 |
Jul | 5.67 | 1222.75 | 2355.99 | 5.10 | 1107.68 | 1979.51 | 5.08 | 1100.32 | 1984.25 | 4.97 | 1073.62 | 1766.54 |
Aug | 5.32 | 1190.37 | 2223.38 | 5.22 | 1173.59 | 2104.87 | 5.33 | 1196.94 | 2210.42 | 5.48 | 1232.26 | 2356.22 |
Sep | 3.34 | 729.84 | 858.84 | 4.04 | 890.43 | 1232.04 | 3.39 | 747.37 | 904.26 | 3.33 | 731.15 | 882.58 |
Oct | 4.42 | 959.36 | 1463.77 | 4.53 | 994.94 | 1451.84 | 4.60 | 1007.93 | 1511.07 | 4.85 | 1070.56 | 1878.72 |
Nov | 4.70 | 1026.40 | 1402.03 | 4.90 | 1078.62 | 1559.26 | 4.87 | 1069.95 | 1490.00 | 4.40 | 973.18 | 1313.48 |
Dec | 8.82 | 1590.31 | 5466.79 | 8.50 | 1542.88 | 5013.12 | 8.34 | 1511.40 | 4813.56 | 7.79 | 1423.98 | 3846.71 |
Average | 6.75 | 1410.74 | 3803.87 | 6.87 | 1445.09 | 3901.68 | 6.76 | 1418.22 | 3744.40 | 6.00 | 1264.31 | 2840.87 |
Five Inputs | Six Inputs | Nine Inputs | Ten Inputs | |||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
LSTM | CNN+LSTM | DNN | Bagging Ensemble Model | LSTM | CNN+LSTM | DNN | Bagging Ensemble Model | LSTM | CNN+LSTM | DNN | Bagging Ensemble Model | LSTM | CNN+LSTM | DNN | Bagging Ensemble Model | |
Holidays | 20.19 | 20.23 | 20.09 | 18.78 | 20.29 | 20.29 | 20.08 | 19.01 | 20.77 | 20.30 | 20.07 | 15.60 | 20.45 | 20.25 | 20.07 | 16.10 |
Bridging Holidays | 9.5 | 10.16 | 9.26 | 7.76 | 10.07 | 10.28 | 9.25 | 8.16 | 10.49 | 10.22 | 9.26 | 6.56 | 10.07 | 10.20 | 9.25 | 8.41 |
Mondays | 5.39 | 5.39 | 5.42 | 5.73 | 5.39 | 5.39 | 5.42 | 5.73 | 5.47 | 5.35 | 5.44 | 5.80 | 5.35 | 5.39 | 5.42 | 5.17 |
Weekdays | 5.6 | 5.79 | 5.63 | 4.92 | 5.62 | 5.76 | 5.60 | 4.93 | 5.61 | 5.84 | 5.61 | 4.80 | 5.64 | 5.86 | 5.59 | 5.00 |
Weekends | 6.64 | 6.67 | 6.78 | 5.66 | 6.62 | 6.64 | 6.76 | 5.68 | 6.71 | 6.70 | 6.77 | 6.02 | 6.56 | 6.61 | 6.75 | 6.10 |
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Phyo, P.-P.; Jeenanunta, C. Advanced ML-Based Ensemble and Deep Learning Models for Short-Term Load Forecasting: Comparative Analysis Using Feature Engineering. Appl. Sci. 2022, 12, 4882. https://doi.org/10.3390/app12104882
Phyo P-P, Jeenanunta C. Advanced ML-Based Ensemble and Deep Learning Models for Short-Term Load Forecasting: Comparative Analysis Using Feature Engineering. Applied Sciences. 2022; 12(10):4882. https://doi.org/10.3390/app12104882
Chicago/Turabian StylePhyo, Pyae-Pyae, and Chawalit Jeenanunta. 2022. "Advanced ML-Based Ensemble and Deep Learning Models for Short-Term Load Forecasting: Comparative Analysis Using Feature Engineering" Applied Sciences 12, no. 10: 4882. https://doi.org/10.3390/app12104882
APA StylePhyo, P.-P., & Jeenanunta, C. (2022). Advanced ML-Based Ensemble and Deep Learning Models for Short-Term Load Forecasting: Comparative Analysis Using Feature Engineering. Applied Sciences, 12(10), 4882. https://doi.org/10.3390/app12104882