ARCHIVES

1. Ministry of New and Renewable Energy, "State-wise Renewable Energy Capacity," MNRE India, Government Report, March 2025.
2. T. Zhou, Z. Ma, Q. Wen, X. Wang, L. Sun, and R. Jin, "FEDformer: Frequency Enhanced Decomposed Transformer for Long-term
Series Forecasting," in Proc. ICML, 2022, pp. 27268-27286.
3. T. Haarnoja, A. Zhou, K. Hartikainen, G. Tucker, S. Ha, J. Tan, V. Kumar, H. Zhu, A. Gupta, P. Abbeel, and S. Levine, "Soft Actor-
Critic Algorithms and Applications," arXiv preprint arXiv:1812.05905v2, 2021.
4. K. Antoniadou-Plytaria, D. Steen, L. A. Tuan, and O. Carlson, "Market-Based Energy Management of a Building Microgrid Including
Battery Degradation," IEEE Trans. Smart Grid, vol. 13, no. 2, pp. 1388-1398, 2022.
5. D. Zhang, X. Han, and C. Ning, "Stochastic Model Predictive Control for Energy Management in Microgrids with High Renewable
Penetration," Appl. Energy, vol. 280, p. 115934, 2020.
6. H. Li, Z. Wan, and H. He, "Constrained EV Charging Scheduling Based on Safe Deep Reinforcement Learning," IEEE Trans. Smart
Grid, vol. 11, no. 3, pp. 2427-2439, 2020.
7. R. Kumar and A. Sharma, "Microgrid Energy Management in Indian Context: Challenges and Opportunities," Renew. Sustain. Energy
Rev., vol. 145, p. 111073, 2021.
8. T. Chen, S. Bu, X. Liu, J. Kang, F. R. Yu, and Z. Han, "Deep Reinforcement Learning-Based BESS Dispatch for Energy Arbitrage in
Microgrids," IEEE Trans. Ind. Inform., vol. 16, no. 9, pp. 5909-5917, 2020.
9. Y. Wang, W. Shi, B. Sun, and D.-H. Tsang, "Multi-Agent Reinforcement Learning for Residential Demand Response Under Non-
Stationary Pricing," IEEE Trans. Smart Grid, vol. 12, no. 4, pp. 3177-3187, 2021.
10. Q. Fan, X. Fu, and A. M. Saber, "Deep Reinforcement Learning-Based Real-Time Energy Management of Microgrids," IEEE Trans.
Sustain. Energy, vol. 13, no. 4, pp. 1935-1945, 2022.
11. B. Lim, S. O. Arik, N. Loeff, and T. Pfister, "Temporal Fusion Transformers for Interpretable Multi-Horizon Time Series Forecasting,"
Int. J. Forecast., vol. 37, no. 4, pp. 1748-1764, 2021.
12. H. Zhou, S. Zhang, J. Peng, S. Zhang, J. Li, H. Xiong, and W. Zhang, "Informer: Beyond Efficient Transformer for Long Sequence
Time-Series Forecasting," in Proc. AAAI, 2021, pp. 11106-11115.
13. H. Wu, J. Xu, J. Wang, and M. Long, "Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series
Forecasting," in Proc. NeurIPS, 2021, pp. 22419-22430.
14. Y. Nie, N. H. Nguyen, P. Sinthong, and J. Kalagnanam, "A Time Series is Worth 64 Words: Long-Term Forecasting with Transformers,"
in Proc. ICLR, 2023.
15. Z. Huang, M. Wang, H. Guo, and J. Zhen, "Transfer Learning for Cross-Domain Energy Forecasting in Smart Microgrids," IEEE Trans.
Smart Grid, vol. 14, no. 3, pp. 2291-2303, 2023.
16. S. Haben, S. Arora, G. Giasemidis, M. Voss, and D. Greetham, "Review of Low Voltage Load Forecasting: Methods, Applications, and
Recommendations," Appl. Energy, vol. 304, p. 117798, 2021.
17. B. Pelletier, M. Candidate, S. Jabali, G. Laporte, and M. Veneroni, "Battery Degradation and Behaviour for Electric Vehicles: Review
and Numerical Analyses of Several Models," Transp. Res. Part B, vol. 58, pp. 299-318, 2022.
18. L. Pan, J. He, Y. Li, A. Maksimov, and A. Ribeiro, "DeepOPF+: A Deep Neural Network Approach for DC Optimal Power Flow for
Ensuring Feasibility," IEEE Trans. Power Syst., vol. 38, no. 1, pp. 118-128, 2023.
19. S. Liao, J. Xu, Y. Sun, Y. Bao, and B. Tang, "Control of Energy-Intensive Load for Power Smoothing in Photovoltaic Power Plant,"
IEEE Trans. Power Syst., vol. 38, no. 2, pp. 1014-1025, 2023.
20. X. Li, K. Thirugnanam, S. Kaur, P. Kumar, and P. T. Mosadeghy, "Graph Neural Network-Based Topology Identification in Distribution
Grids for Enhanced Battery Storage Dispatch," IEEE Trans. Smart Grid, vol. 14, no. 5, pp. 3924-3935, 2023.
21. P. Kou, D. Liang, L. Wang, and F. Gao, "Reinforcement Learning-Based Cooperative Bidding Strategy for BESS in Day-Ahead
Market," IEEE Trans. Ind. Electron., vol. 69, no. 8, pp. 8034-8044, 2022.
22. S. Dey, A. Bhatt, P. Rathore, and R. Gupta, "AI-Driven Optimization of Solar-BESS Microgrids in Indian Industrial Estates: A Case
Study," Energy Convers. Manage., vol. 295, p. 117619, 2024.
23. M. Shurrab, S. Singh, H. Otrok, R. Mizouni, V. Khadkikar, and H. Zeineldin, "An Efficient Vehicle-to-Vehicle (V2V) Energy Sharing
Framework," IEEE Internet Things J., vol. 9, no. 7, pp. 5315-5328, 2022.
24. R. Khalid, N. Javaid, M. H. Aloqaily, F. S. Al-Anzi, Y. Jararweh, and K. Gupta, "Fuzzy Energy Management Controller and Scheduler
for Smart Homes with Forecasting Adaptation," Sustainable Comput. Inform. Syst., vol. 31, p. 100592, 2021.
25. J. Zhang, F. Wen, Y. Xu, Z. Dong, T. Chen, and X. Yin, "Federated Learning-Based Battery Energy Storage Dispatch for Privacy-
Preserving Multi-Site Microgrid Management," IEEE Trans. Smart Grid, vol. 15, no. 1, pp. 743-757, 2024.