Parallel Multi-Model Energy Demand Forecasting with Cloud Redundancy: Leveraging Trend Correction, Feature Selection, and Machine Learning
Abstract
1. Introduction
- (a)
- Hybrid forecasting framework with deviation correction:
- (b)
- Fault Tolerance via Distributed Cloud Deployment and Voting:
- (c)
- Generalizable and Adaptable Methodology
2. Methodology
2.1. Procedures
2.2. 1-Collecting Historical Data for Load Consumption
2.3. 2-Feature Selection
Correlation Matrix (CM)
- +1: means that the two variables increase proportionally as one increases.
- −1: indicates that a perfect negative correlation exists, meaning that as one variable increases, the other variable decreases proportionally.
- A correlation of 0: there is no linear relationship between the variables. It is still possible; however, that another nonlinear relationship exists.
2.4. 3-Trend Correction
2.5. Implementation of Multiple Machine Learning Methods on Multiple Clouds
2.6. Deviation Correction (DC)
Description 1: Parameters of the Proposed Model
- Input:
- Dataset D from 1 April 2022, to 30 March 2025;
- Forecast horizon: next 24 h (31 March 2025).
- Output:
- Hourly load forecast for 31 March 2025, with refined predictions for the first 5 h of 1 April 2025.
- Data Partitioning
- 24 h Forecasting
- 2.1.
- Train the forecasting models on the training set.
- 2.2.
- Generate the forecast for the next 29 h: 24 h for 31 March 2025 (A), and the first 5 h of 1 April 2025 (B).
- Deviation Analysis and Correction
- 3.1.
- Analyze the deviation trend within the test set.
- 3.2.
- Forecast the deviation for the first 5 h of 1 April 2025 (C).
- 3.3.
- For the first 5 h, compute the corrected forecast:
- Final Forecast = B + C
- Fault Tolerance via 2-out-of-3 Voting on Three Cloud Platforms
- 4.1.
- Deploy three different models on three cloud platforms.
- 4.2.
- Determine the final prediction by majority voting (averaging the consensus) if at least two out of the three platforms produce consistent predictions.
- R represents reliability.
3. Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Nomenclature
Metric | Definition |
MSE | Mean square error |
MAE | Mean absolute error |
MAPE | Mean absolute percentage error |
RMSE | Root mean square error |
AE | Average error |
DC | Deviation correction |
ML | Machine learning |
SARIMAX | Seasonal autoregressive integrated moving average with exogenous regressors |
DT | Decision tree |
LSTM | Long short-term memory |
SVM | Support vector machine |
LR | Linear regression |
ANN | Artificial neural networks |
DEO&K Co | Duke Energy Ohio and Kentucky |
TC | Trend correction |
AWS | Amazon Web Services |
CM | Correlation matrix |
PJM | PJM is a regional transmission organization (RTO) that coordinates the movement of wholesale electricity in all or parts of 13 states and the District of Columbia |
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Features | Correlation Value | Features | Correlation Value |
---|---|---|---|
Dispatch rate | 0.33921 | Condition | −0.029156 |
Wind Speed | 0.11236 | Pressure | −0.091274 |
Wind Gust | 0.11188 | Dew Point | −0.10462 |
Humidity | 0.052206 | Temperature | −0.1339 |
Precipitation | 0.0035134 | Actual Load | 1 |
Points | Actual Data | SVM | SARIMAX | SARIMAX-SVM | LSTM | DT | LR |
---|---|---|---|---|---|---|---|
1 | 2.7142 | 2.6015 | 2.6639 | 2.6643 | 2.5890 | 2.5957 | 2.6134 |
2 | 2.4560 | 2.3966 | 2.4690 | 2.4344 | 2.5842 | 2.4083 | 2.3944 |
3 | 2.3348 | 2.3481 | 2.4151 | 2.3484 | 2.4777 | 2.3426 | 2.3371 |
4 | 2.2407 | 2.3904 | 2.3956 | 2.2992 | 2.2989 | 2.5218 | 2.3509 |
5 | 2.1184 | 2.3425 | 2.3332 | 2.2162 | 2.1163 | 2.5564 | 2.3206 |
6 | 2.0390 | 2.3999 | 2.2708 | 2.1503 | 2.0707 | 2.2839 | 2.3288 |
7 | 2.0019 | 2.3812 | 2.2343 | 2.1183 | 2.1638 | 2.2340 | 2.3469 |
8 | 1.9885 | 2.2186 | 2.2119 | 2.1052 | 2.2506 | 2.2557 | 2.2873 |
9 | 2.0104 | 2.2542 | 2.1978 | 2.1095 | 2.2660 | 2.1821 | 2.2571 |
10 | 2.1826 | 2.3480 | 2.3251 | 2.2581 | 2.3503 | 2.2845 | 2.3648 |
11 | 2.4312 | 2.5364 | 2.5075 | 2.4677 | 2.4726 | 2.3892 | 2.5374 |
12 | 2.5925 | 2.5118 | 2.6105 | 2.5944 | 2.5176 | 2.5190 | 2.5796 |
13 | 2.6633 | 2.5209 | 2.6243 | 2.6300 | 2.5002 | 2.4803 | 2.5489 |
14 | 2.7386 | 2.5162 | 2.6607 | 2.6812 | 2.5322 | 2.4634 | 2.5464 |
15 | 2.8005 | 2.5569 | 2.7059 | 2.7328 | 2.5907 | 2.6012 | 2.5706 |
16 | 2.8049 | 2.5523 | 2.7000 | 2.7302 | 2.6029 | 2.6616 | 2.5567 |
17 | 2.8078 | 2.6193 | 2.7314 | 2.7517 | 2.6543 | 2.7387 | 2.6047 |
18 | 2.8121 | 2.8028 | 2.7914 | 2.7927 | 2.7578 | 2.9074 | 2.7348 |
19 | 2.8116 | 3.0136 | 2.8453 | 2.8277 | 2.9006 | 2.9053 | 2.9066 |
20 | 2.8131 | 2.8910 | 2.8782 | 2.8489 | 2.9880 | 2.7959 | 2.9388 |
21 | 2.8432 | 2.8151 | 2.9128 | 2.8805 | 2.9828 | 2.7885 | 2.8989 |
22 | 2.8704 | 2.7953 | 2.9080 | 2.8846 | 2.8775 | 2.7140 | 2.8317 |
23 | 2.9160 | 2.7785 | 2.9365 | 2.9175 | 2.8068 | 2.7983 | 2.8109 |
24 | 2.9439 | 2.7573 | 2.8954 | 2.9234 | 2.8334 | 2.7480 | 2.7514 |
Predicted Consumption | Predicted Deviation | ||||
---|---|---|---|---|---|
LSTM | SARIMAX | SARIMAX + SVM | LSTM | SARIMAX | SARIMAX + SVM |
2.9623 | 2.9698 | 2.9696 | 0.0118 | 0.01 | 0.0114 |
2.868 | 2.852 | 2.8763 | 0.0123 | 0.0415 | 0.0184 |
2.6871 | 2.6976 | 2.6977 | 0.0141 | 0.024 | 0.0274 |
2.5187 | 2.5452 | 2.5309 | 0.0150 | 0.0152 | 0.0361 |
2.4111 | 2.4233 | 2.4239 | 0.0154 | 0.0334 | 0.0389 |
Actual | Predicted Consumption by Proposed Methods | Predicted Consumption Without DC | |||||||
---|---|---|---|---|---|---|---|---|---|
Real Data | LSTM-DC | SARIMAX-DC | SARIMAX-SVM-DC | LSTM | SARIMAX | SARIMAX SVM | SVM | LR | DT |
2.9734 | 2.9741 | 2.9798 | 2.981 | 2.9623 | 2.9698 | 2.9696 | 2.9108 | 2.9201 | 2.9267 |
2.9042 | 2.8803 | 2.8935 | 2.8947 | 2.868 | 2.852 | 2.8763 | 2.8002 | 2.8065 | 2.7971 |
2.7497 | 2.7012 | 2.7216 | 2.7251 | 2.6871 | 2.6976 | 2.6977 | 2.5907 | 2.5951 | 2.6522 |
2.6016 | 2.5337 | 2.5604 | 2.567 | 2.5187 | 2.5452 | 2.5309 | 2.4679 | 2.4688 | 2.5314 |
2.5026 | 2.4265 | 2.4567 | 2.4628 | 2.4111 | 2.4233 | 2.4239 | 2.3556 | 2.3538 | 2.4221 |
Metric | CM-LSTM-DC | CM-SARIMAX-DC | CM-SARIMAX-SVM-DC |
---|---|---|---|
MAE | 0.0434 | 0.0264 | 0.0232 |
MSE | 0.0027 | 0.0009 | 0.0007 |
RMSE | 0.0516 | 0.0308 | 0.0266 |
MAPE | 1.65% | 1.00% | 0.88% |
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Hassanpouri Baesmat, K.; Farrokhi, Z.; Chmaj, G.; Regentova, E.E. Parallel Multi-Model Energy Demand Forecasting with Cloud Redundancy: Leveraging Trend Correction, Feature Selection, and Machine Learning. Forecasting 2025, 7, 25. https://doi.org/10.3390/forecast7020025
Hassanpouri Baesmat K, Farrokhi Z, Chmaj G, Regentova EE. Parallel Multi-Model Energy Demand Forecasting with Cloud Redundancy: Leveraging Trend Correction, Feature Selection, and Machine Learning. Forecasting. 2025; 7(2):25. https://doi.org/10.3390/forecast7020025
Chicago/Turabian StyleHassanpouri Baesmat, Kamran, Zeinab Farrokhi, Grzegorz Chmaj, and Emma E. Regentova. 2025. "Parallel Multi-Model Energy Demand Forecasting with Cloud Redundancy: Leveraging Trend Correction, Feature Selection, and Machine Learning" Forecasting 7, no. 2: 25. https://doi.org/10.3390/forecast7020025
APA StyleHassanpouri Baesmat, K., Farrokhi, Z., Chmaj, G., & Regentova, E. E. (2025). Parallel Multi-Model Energy Demand Forecasting with Cloud Redundancy: Leveraging Trend Correction, Feature Selection, and Machine Learning. Forecasting, 7(2), 25. https://doi.org/10.3390/forecast7020025