Hybrid Renewable Production Scheduling for a PV–Wind-EV-Battery Architecture Using Sequential Quadratic Programming and Long Short-Term Memory–K-Nearest Neighbors Learning for Smart Buildings
Abstract
:1. Introduction
2. Hybrid System Modeling
2.1. Photovoltaic Generation
2.2. Wind Turbine Generation
2.3. Electric Vehicle
2.4. Battery Storage System
3. Methodology
3.1. Problem Formulation
3.2. Sequential Quadratic Programming
3.3. LSTM Prediction
3.4. Random Forest
3.5. Gradient Boost Machine
3.6. KNN
4. Results and Discussion
Algorithm 1: Energy-management-system linear programming |
Result: Optimal Energy Collaboration Initialization: Extract meteorological conditions G, Vwind, T; Collect SoC(t), SoCmax, SoCmin, EBattmax, EEVBatt, SoCEV(t), SoCEVmax, SoCEVmin, EEVBattmax, PLoad; Initialize SoC, SoCEV; Define P as [P(1) P(2) P(3) P(4) P(5) P(6)] |
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Parameter | Value |
---|---|
Rated turbine value | 2.4 kW |
Rotor radius | 1.86 m |
Air density | 1.225 kg/m3 |
Number of pole pairs | 8 |
Parameter | Value |
---|---|
Maximum power | 300 Wp |
Short-circuit current | 9.06 A |
Open-circuit voltage | 44.52 V |
−0.346%/°C | |
+0.036%/°C | |
Number of cells | 72 |
Ideality factor | 1.5 |
Type | Electrical Power |
---|---|
Ebatt | 5.5 kWh |
Autonomy (Max.) | 70 Km |
Speed (Max.) | 45 Km/h |
Charging | 3 h |
Model | Data Normalization | RMSE | MAE | R2_Score |
---|---|---|---|---|
Long Short-Term Memory network | None | 67.0591 | 25.0886 | 0.9533 |
Min–Max scaling | 72.7265 | 32.27 | 0.9418 | |
Z-score scaling | 70.7165 | 30.3589 | 0.9404 | |
Log transformation | Nan | Nan | 0.5292 | |
Quantile transformation | 129.6644 | 56.8839 | 0.6308 | |
Robust scaling | 148.9046 | 65.9128 | 0.4322 | |
Feature scaling to specific range | 68.3868 | 30.6786 | 0.9468 | |
Decimal scaling (3) | 146.4857 | 66.1624 | 0.4400 | |
Max absolute scaling | 71.2652 | 31.5538 | 0.9425 | |
Softmax transformation | 469.0968 | 238.6566 | −0.3430 | |
Power transformation (2) | 171.3721 | 69.1538 | 0.7624 | |
Unit vector scaling | 163.5302 | 76.9106 | 0.3916 | |
Max norm scaling | 163.0177 | 73.1468 | 0.4215 |
Model | RMSE | MAE | R2_Score |
---|---|---|---|
Random Forest | 74.6369 | 50.3825 | 0.6953 |
Gradient boosting | 87.7846 | 57.8135 | 0.7391 |
K-nearest neighbors | 56.5445 | 35.3834 | 0.8381 |
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Chakir, A.; Tabaa, M. Hybrid Renewable Production Scheduling for a PV–Wind-EV-Battery Architecture Using Sequential Quadratic Programming and Long Short-Term Memory–K-Nearest Neighbors Learning for Smart Buildings. Sustainability 2024, 16, 2218. https://doi.org/10.3390/su16052218
Chakir A, Tabaa M. Hybrid Renewable Production Scheduling for a PV–Wind-EV-Battery Architecture Using Sequential Quadratic Programming and Long Short-Term Memory–K-Nearest Neighbors Learning for Smart Buildings. Sustainability. 2024; 16(5):2218. https://doi.org/10.3390/su16052218
Chicago/Turabian StyleChakir, Asmae, and Mohamed Tabaa. 2024. "Hybrid Renewable Production Scheduling for a PV–Wind-EV-Battery Architecture Using Sequential Quadratic Programming and Long Short-Term Memory–K-Nearest Neighbors Learning for Smart Buildings" Sustainability 16, no. 5: 2218. https://doi.org/10.3390/su16052218
APA StyleChakir, A., & Tabaa, M. (2024). Hybrid Renewable Production Scheduling for a PV–Wind-EV-Battery Architecture Using Sequential Quadratic Programming and Long Short-Term Memory–K-Nearest Neighbors Learning for Smart Buildings. Sustainability, 16(5), 2218. https://doi.org/10.3390/su16052218