  One of the primal challenges faced by utility companies is ensuring efficient
supply with minimal greenhouse gas emissions. The advent of smart meters and
smart grids provide an unprecedented advantage in realizing an optimised supply
of thermal energies through proactive techniques such as load forecasting. In
this paper, we propose a forecasting framework for heat demand based on neural
networks where the time series are encoded as scalograms equipped with the
capacity of embedding exogenous variables such as weather, and
holiday/non-holiday. Subsequently, CNNs are utilized to predict the heat load
multi-step ahead. Finally, the proposed framework is compared with other
state-of-the-art methods, such as SARIMAX and LSTM. The quantitative results
from retrospective experiments show that the proposed framework consistently
outperforms the state-of-the-art baseline method with real-world data acquired
from Denmark. A minimal mean error of 7.54% for MAPE and 417kW for RMSE is
achieved with the proposed framework in comparison to all other methods.
