Aiming at addressing the shortcomings of short-term power load forecasting caused by random initialization of artificial neural network parameters,this work studied a short-term load forecasting method utilizing the temporal convo-lutional network optimized by improved northern goshawk optimization(INGO)and hybridized with the attention mecha-nism.The multi-strategy algorithm was used to improve the northern goshawk algorithm,and a test by benchmark func-tion verified the better optimization performance of the improved algorithm.Then the INGO algorithm was further intro-duced to TCN,and the INGO-TCN-Attention short-term power load forecasting model was established,which in the sub-sequent comparative experiment exhibited stability and prediction accuracy superior to other models.
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