Short-Term Load Forecasting Based on Spiking Neural P Systems

Short-term load forecasting is a significant component of safe and stable operations economical reliable dispatching power grids. Precise can help to formulate reasonable effective coordination plans implementation strategies. Inspired by the spiking mechanism neurons, nonlinear neural P (NSNP) system, parallel computing model, was proposed. On basis SNP systems, this study exploits fresh short-term termed as LF-NSNP model. The model essentially recurrent-like which effectively capture correlation between temporal features electric sequence. In an effort validate effectiveness superiority proposed in tasks, tests were conducted on datasets different time variable types, predictive competence various baseline models compared.