I'm doing a machine learning time series forecast of electricity production shares by power plant (nuclear, coal-fired, gas, solar, wind, water etc.) in my country in 5 year horizon. I have historical data (weekly averages by power plants) since 2010.
Is there a way to include external factors in the forecast using ML methods such as LSTM, XGBoost, SVM, ARIMA and others? For example tell the model that certain percentage of solar power plants are expected to be installed in the future or that some coal-fired power plants will be shutdown in the next 2 years and thus this type of power plant is likely going to lose share in the overall electricity production?
I feel like the forecast can't be good if I'm relying only on historical data and not including other factors as well.
Is machine learning even good option for long term forecasts of electricity production shares?