RAN Energy Consumption Prediction in Network Expansion Scenarios

As mobile data traffic continues to increase and network operators expand their infrastructures, energy consumption in Radio Access Networks (RAN) becomes a critical concern, particularly during network expansion. This paper addresses the problem of predicting RAN energy consumption in network expansion scenarios. We develop and evaluate different forecasting strategies, including per-site and network-wide models, as well as Machine Learning (ML)-based approaches, using real-world data from a LTE network. Our results show that while simple network-wide models perform well when the base station (BS) configurations remain constant, ML models are more effective in scenarios where BS configurations change during network expansion. The insights from this study can help mobile network operators improve energy efficiency by adapting their networks to traffic patterns and expansion processes, supporting both cost management and sustainability goals.