Dynamic line rating forecasting using recurrent neural networks

Dynamic thermal rating forecasting plays an important role in the efficient management of operating conditions for overhead lines on power distribution systems. In this study, a novel framework that allows generating models that accurately forecast ampacity feature of an overhead transmission line is proposed, in order to optimize the amount of transmitted energy. For this purpose, several techniques based on artificial neural networks are used in the process. The techniques range from the classic multi-layer perceptron neural network to the more advanced recurrent neural networks: long short-term memory and gated recurrent unit. The predictions from these three techniques are analyzed and compared to finally determine which of the generated models is the most appropriate to obtain ampacity forecasts at a horizon of 24 hours. The results obtained provide valuable information within the studied area and provide accurate models for ampacity forecasting.