  The transition to sustainable Open Radio Access Network (O-RAN) architectures
brings new challenges for resource management, especially in predicting the
utilization of Physical Resource Block (PRB)s. In this paper, we propose a
novel approach to characterize the PRB load using probabilistic forecasting
techniques. First, we provide background information on the O-RAN architecture
and components and emphasize the importance of energy/power consumption models
for sustainable implementations. The problem statement highlights the need for
accurate PRB load prediction to optimize resource allocation and power
efficiency. We then investigate probabilistic forecasting techniques, including
Simple-Feed-Forward (SFF), DeepAR, and Transformers, and discuss their
likelihood model assumptions. The simulation results show that DeepAR
estimators predict the PRBs with less uncertainty and effectively capture the
temporal dependencies in the dataset compared to SFF- and Transformer-based
models, leading to power savings. Different percentile selections can also
increase power savings, but at the cost of over-/under provisioning. At the
same time, the performance of the Long-Short Term Memory (LSTM) is shown to be
inferior to the probabilistic estimators with respect to all error metrics.
Finally, we outline the importance of probabilistic, prediction-based
characterization for sustainable O-RAN implementations and highlight avenues
for future research.
