Forecasting energy consumption with no historical data when there is a trend
Most time series models assume stationarity. Thus, you should account for trend and seasonality in your data and subtract the corresponding components from your original data. After that, you can model your normalized data also with arbitrary machine learning models; and forecast with new data the following months. 

Carefully note that you may have features that explain the trend or seasonality component. If these components are also changing or expecting to change over time you could combine (e.g. add for additive time series models) predictions for all components. I.e. the sum of the trend, the seasonal and the residual prediction. 

Importance of Normalization
E.g. https://stats.stackexchange.com/q/31387/214243

Detrending
Subtract the %yoy change for each month.

Seasonal decomposition
E.g.  http://www.statsmodels.org/stable/release/version0.6.html?highlight=seasonal#seasonal-decomposition

Forecasting
There is an abundance of statistical time series models (AR, ARIMA, GARCH,...). But when data is normalized common machine learning models like decision tree or random forecast may also be applicable (see sklearn library for this)