You should probably start with basic time-series models like ARIMA, its seasonal version (seasonality should be particularly important for electricity forecasting) and maybe exponential smoothing.

When looking for research about time-series forecasting, I somewhat often stumble upon these basic methods perform well for electricity forecasting. I can't cite any particular papers since electricity forecasting is not my area of research, but I do feel like these methods are often discussed in the context of electricity forecasting specifically. I'm not sure whether this is a general trend though.

Anyway, in time-series analysis, it's often beneficial to try the traditional models first and only then reach for machine learning. Looks like ARIMA-like models perform fairly well in many cases, so there may be no need for any complicated ML.