Achieving emission reduction targets and maintaining a balance between power output and consumption depend heavily on the effective implementation of electrical load forecasting. This paper introduces a short-term load fore casting method utilizing the neural prophet technique. The results indicate that employing the neural prophet framework significantly reduces parameters such as mean absolute percentage error (MAPE) and root mean square error (RMSE), leading to a notable enhancement in prediction accuracy.