Short-Term Load Forecasting Based on Spiking Neural P Systems
Source abstract
Abstract
Short-term load forecasting is a significant component of safe and stable operations and economical and reliable dispatching of power grids. Precise load forecasting can help to formulate reasonable and effective coordination plans and implementation strategies. Inspired by the spiking mechanism of neurons, a nonlinear spiking neural P (NSNP) system, a parallel computing model, was proposed. On the basis of SNP systems, this study exploits a fresh short-term load forecasting model, termed as the LF-NSNP model. The LF-NSNP model is essentially a recurrent-like model, which can effectively capture the correlation between the temporal features of the electric load sequence. In an effort to validate the effectiveness and superiority of the proposed LF-NSNP model in short-term load forecasting tasks, tests were conducted on datasets of different time and different variable types, and the predictive competence of various baseline models was compared.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Neural Networks and Applications · Machine Learning and ELM · Energy Load and Power Forecasting
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Electrical Engineering and Electronic Information, Xihua University, Chengdu 610039, China
School of Computer and Software Engineering, Xihua University, Chengdu 610039, China
Funding
National Natural Science Foundation of China · Research Fund of Sichuan Science and Technology Project
References (27)
Show source-provided references
1. Monthlyelectric energy demand forecasting based on trend extraction10.1109/TPWRS.2006.883666 ↗
2. Nonparametric regression based short-term load forecasting10.1109/59.708572 ↗
3. Short-term load forecasting for the holidays using fuzzy linear regression method10.1109/TPWRS.2004.835632 ↗
4. Short-term electricity demand forecasting using double seasonal exponential smoothing10.1057/palgrave.jors.2601589 ↗
5. Electric load forecasting based on locally weighted support vector regression10.1109/TSMCC.2010.2040176 ↗
6. Short-term transmission-loss forecast for the Slovenian transmission power system based on a fuzzy-logic decision approach10.1109/TPWRS.2010.2096829 ↗
7. Neural networks for short-term load forecasting: A review and evaluation10.1109/59.910780 ↗
8. Review of smart meter data analytics: Applications, methodologies, and challenges10.1109/TSG.2018.2818167 ↗
9. Very short-term load forecasting using artificialneural networks10.1109/59.852131 ↗
10. A novel RBF training algorithm for short-term electric load forecasting and comparative studies10.1109/TIE.2015.2424399 ↗
11. Short-term load forecasting: Similar day-based wavelet neural networks10.1109/TPWRS.2009.2030426 ↗
12. Short-term load forecasting of Australian national electricity market by an ensemble model of extreme learning machine10.1049/iet-gtd.2012.0541 ↗
13. Experience with FNN models formedium term power demand predictions10.1109/59.761878 ↗
14. Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press.
15. Ryu, S., Noh, J., and Kim, H. (2016). Deep neural network based demand side short term load forecasting. Energies, 10.10.3390/en10010003 ↗
16. Merkel, G., Povinelli, R.J., and Brown, R.H. (2017, January 25–28). Deep neural network regression for short-term load forecasting of natural gas. Proceedings of the 37th Annual International Symposium on Forecasting 2017, Boston, MA, USA.
17. Conditional Residual Modeling for Probabilistic Load Forecasting10.1109/TPWRS.2018.2868167 ↗
18. A novel asynchronous deep reinforcement learning model with adaptive early forecasting method and reward incentive mechanism for short-term load forecasting10.1016/j.energy.2021.121492 ↗
19. Spiking neural P systems
20. Nonlinear spiking neural P systems10.1142/S0129065720500082 ↗
21. Spiking neural P systems with multiple channels10.1016/j.neunet.2017.08.003 ↗
22. Coupled neural P systems10.1109/TNNLS.2018.2872999 ↗
23. Dynamic threshold neural P systems10.1016/j.knosys.2018.10.016 ↗
24. Spiking neural P systems with inhibitory rules10.1016/j.knosys.2019.105064 ↗
25. Dendrite P systems10.1016/j.neunet.2020.04.014 ↗
26. ISO New England (2022, January 01). ISO New England Zonal Information. Available online: https://www.iso-ne.com/isoexpress/web/reports/load-anddemand.
27. Improving Short Term Load Forecast Accuracy via Combining Sister Forecasts10.1016/j.energy.2015.12.142 ↗