EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 433 (2023) E3S Web Conf., 433 (2023) 02003 References
Open Access
Issue
E3S Web Conf.
Volume 433, 2023
2023 The 6th International Conference on Renewable Energy and Environment Engineering (REEE 2023)
Article Number 02003
Number of page(s) 8
Section Renewable Energy Power Generation and Electrification
DOI https://doi.org/10.1051/e3sconf/202343302003
Published online 09 October 2023
  1. J. Rogelj et al., “Paris Agreement climate proposals need a boost to keep warming well below 2 °C,” Nature, vol. 534, no. 7609, pp. 631–639, Jun. 2016, doi: 10.1038/nature18307. [CrossRef] [PubMed] [Google Scholar]
  2. Carbon Brief, “Paris 2015: Tracking country climate pledges|Carbon Brief, ” Carbon Brief, 2015. https://www.carbonbrief.org/paris-2015-tracking-country-climate-pledges (accessed Nov. 05, 2021). [Google Scholar]
  3. Lin-Sea Lau, Yuen-Onn Choong, Suet-Ling Ching, Chooi-Yi Wei, Abdelhak Senadjki, Chee-Keong Choong, Ai-Na Seow, Expert insights on Malaysia’s residential solar-energy policies: shortcomings and recommendations, Clean Energy, Volume 6, Issue 4, August 2022, Pages 619–631, https://doi.org/10.1093/ce/zkac043 [CrossRef] [Google Scholar]
  4. G. Alkawsi, Y. Baashar, A. A. Alkahtani, C. W. Lim, S. K. Tiong, and M. Khudari, “Viability assessment of small-scale on-grid wind energy generator for households in Malaysia, ” Energies, vol. 14, no. 12, Jun. 2021, doi: 10.3390/en14123391. [CrossRef] [Google Scholar]
  5. M. Effendi Amran et al., “Photovoltaic-integrated review and expansion need in green building landscape for bridging the Malaysian RE policy, ” Indonesian Journal of Electrical Engineering and Computer Science (IJEECS), vol. 17, no. 1, pp. 27–35, Jan. 2020, doi: 10.11591/ijeecs.v17.i1.pp27-35. [CrossRef] [Google Scholar]
  6. Sustainable Energy Development Authority (SEDA) MALAYSIA. (n.d.). Net Energy Metering (NEM) 3.0. SEDA MALAYSIA. Retrieved May 05, 2022, from https://www.seda.gov.my/reportal/nem/ [Google Scholar]
  7. Shahida, Norshahidatul & Suhaime, Mohamed & Shaikh, Zishan & Safwan, Ahmad & Jarimi, Hasila & Fauzan, Ir Ts Dr Mohd Faizal & Ibrahim, Adnan & Sohif, Mat & Fazlizan, Ahmad. (2022). Energy distribution and economic analysis of a residential house with the net-energy metering scheme in Malaysia. International Journal of Electrical and Computer Engineering. 12. 2313-2322. 10.11591/ijece.v12i3.pp2313-2322. [Google Scholar]
  8. Li, Kangping, et al. “Capacity and output power estimation approach of individual behind-the-meter distributed photovoltaic system for demand response baseline estimation.” Applied Energy 253 (2019): 113595. [CrossRef] [Google Scholar]
  9. Chen, Z., Ma, M., Li, T., Wang, H., & Li, C. (2023). Long sequence time-series forecasting with deep learning: A survey. 97(April). [Google Scholar]
  10. Holmes, E. E, Ward, E. J, & Kellie, W. (2012). MARSS: multivariate autoregressive state-space models for analyzing time-series data. R J., 4(1), 11. [Google Scholar]
  11. Huang, S., Wang, D., Wu, X., & Tang, A. (2019, November). Dsanet: Dual self-attention network for multivariate time series forecasting. In Proceedings of the 28th ACM international conference on information and knowledge management (pp. 21292132). [Google Scholar]
  12. J. Priolkar, A. Shirodkar and E. S. Sreeraj, “Forecasting of Load and Solar PV Power to Assess Demand Response Potential, ” 2021 IEEE 18th India Council International Conference (INDICON), Guwahati, India, 2021, pp. 1-6, doi: 10.1109/INDICON52576.2021.9691655. [Google Scholar]
  13. Qu, J., Qian, Z., & Pei, Y. (2021). Day-ahead hourly photovoltaic power forecasting using attentionbased CNN-LSTM neural network embedded with multiple relevant and target variables prediction pattern. Energy, 232, 120996. [CrossRef] [Google Scholar]
  14. Kuo, W. C, Chen, C. H, Chen, S. Y, & Wang, C. C (2022). Deep learning neural networks for shortterm PV Power Forecasting via Sky Image method. Energies, 15(13), 4779. [CrossRef] [Google Scholar]
  15. Jebli, I., Belouadha, F. Z, Kabbaj, M. I, & Tilioua, A. (2021). Deep learning-based models for solar energy prediction. Advances in Science, Technology and Engineering Systems Journal, 6(1), 349-355. [CrossRef] [Google Scholar]
  16. N. Kumari, G. Shankar and S. R. Biswal, “Impact of Distinct Weather Parameters on Simultaneous Prediction of Solar Power and Load Demand using LSTM Model, ” 2022 2nd International Conference on Emerging Frontiers in Electrical and Electronic Technologies (ICEFEET), Patna, India, 2022, pp. 15, doi: 10.1109/ICEFEET51821.2022.9848169. [Google Scholar]
  17. Limouni, T., Yaagoubi, R., Bouziane, K., Guissi, K., & Baali, E. H (2023). Accurate one step and multistep forecasting of very short-term PV power using LSTM-TCN model. Renewable Energy, 205(January), 1010–1024. https://doi.org/10.1016/j.renene.2023.01.118 [CrossRef] [Google Scholar]
  18. Bounoua, Z., & Mechaqrane, A. (2023). Hourly and sub-hourly ahead global horizontal solar irradiation forecasting via a novel deep learning approach: A case study. Sustainable Materials and Technologies, 36(January 2022), e00599. https://doi.org/10.1016/j.susmat.2023.e00599 [CrossRef] [Google Scholar]
  19. N. Roy et al., “Day-ahead Solar Power Generation Forecasting using LSTM and Random Forest Methods for North Eastern Region of India, ” 2022 22nd National Power Systems Conference (NPSC), New Delhi, India, 2022, pp. 854-859, doi: 10.1109/NPSC57038.2022.10069833. [Google Scholar]
  20. National Renewable Energy Laboratory (NREL). (Accessed January 2023). National Solar Radiation Database (NSRDB). [Online]. Available: https://nsrdb.nrel.gov/ [Google Scholar]
  21. Historical and TMY Data. (n.d.). https://solcast.com/historical-and-tmy [Google Scholar]
  22. Vakitbilir, N., Hilal, A., & Direkoğlu, C. (2022). Hybrid deep learning models for multivariate forecasting of global horizontal irradiation. Neural Computing and Applications, 34(10), 8005-8026. [CrossRef] [Google Scholar]
  23. Wojtkiewicz, J., Hosseini, M., Gottumukkala, R., & Chambers, T. L (2019). Hour-ahead solar irradiance forecasting using multivariate gated recurrent units. Energies, 12(21), 4055. [CrossRef] [Google Scholar]
  24. Moreira, M. O, Kaizer, B. M, Ohishi, T., Bonatto, B. D, Zambroni de Souza, A. C, & Balestrassi, P. P (2022). Multivariate Strategy Using Artificial Neural Networks for Seasonal Photovoltaic Generation Forecasting. Energies, 16(1), 369. [CrossRef] [Google Scholar]
  25. Mecke, I. Lee, J.R. Bakerjr., M.M. Banaszak Holl, B.G. Orr, Eur. Phys. J. E 14, 7 (2004) [CrossRef] [PubMed] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.