A machine learning approach based on neural networks for energy diagnosis of telecommunication sites

Multi-layer-perceptron-feedforward neural-networks (MLPFFNNs) are proposed to monitor electricity consumption of generic telecommunication sites, thus addressing well-known energy saving and environmental issues. The proposed prediction tool helps infer telecommunication site efficiency, thus enabling undertaking suitable energy intelligence actions (monitoring, control and diagnosis, supervision) as a function of a large number of data and information. A trial-and-error analysis was performed to find the most accurate neural network for such a prediction task. Physical knowledge and intrinsic behavioral features of telecommunication sites indicated that 2 electricity consumptions- and 4 weather-related variables shall populate the input layer. The number of hidden neurons, along with training and test splitting percentages, were selected as the variables to refine within the networks development task. Statistical assessment of accuracy was conducted via joint evaluation of root mean square error and coefficient of determination. The precision and generalization of the best MLPFFNN were successfully assessed on a relevant sites dataset. Prediction percentage errors set to 5.65% and 5.77% for monthly and weekly assessment, respectively, thus confirming the suitability of the proposed modelling approach to meet regulatory standards (i.e., ISO 50001), requiring reliable and continuous monitoring and diagnosis of large fleets of industrial buildings, such as central offices and datacenters.