Deep Convolutional Neural Networks for Short-Term Multi-Energy Demand
  Prediction of Integrated Energy Systems
Corneliu Arsene
Forecasting power consumptions of integrated electrical, heat or gas network
systems is essential in order to operate more efficiently the whole energy
network. Multi-energy systems are increasingly seen as a key component of
future energy systems, and a valuable source of flexibility, which can
significantly contribute to a cleaner and more sustainable whole energy system.
Therefore, there is a stringent need for developing novel and performant models
for forecasting multi-energy demand of integrated energy systems, which to
account for the different types of interacting energy vectors and of the
coupling between them. Previous efforts in demand forecasting focused mainly on
the single electrical power consumption or, more recently, on the single heat
or gas power consumptions. In order to address this gap, in this paper six
novel prediction models based on Convolutional Neural Networks (CNNs) are
developed, for either individual or joint prediction of multi-energy power
consumptions: the single input/single output CNN model with determining the
optimum number of epochs (CNN_1), the multiple input/single output CNN model
(CNN_2), the single input/ single output CNN model with
training/validation/testing datasets (CNN_3), the joint prediction CNN model
(CNN_4), the multiple-building input/output CNN model (CNN_5) and the federated
learning CNN model (CNN_6). All six novel CNN models are applied in a
comprehensive manner on a novel integrated electrical, heat and gas network
system, which only recently has started to be used for forecasting. The
forecast horizon is short-term (next half an hour) and all the predictions
results are evaluated in terms of the Signal to Noise Ratio (SNR) and the
Normalized Root Mean Square Error (NRMSE), while the Mean Absolute Percentage
Error (MAPE) is used for comparison purposes with other existent results from
literature.