Short-term load forecasting of Gothatar feeder of Nepal Electricity Authority using Recurrent Neural Network

Abstract

This paper mainly focuses on short-term forecasting, gives an hourly demand forecast of electricity. Forecasting using Recurrent Neural Network (RNN) is helpful for making important decisions in the field of preventing misbalancing in load and power generation, scheduling, load switching strategies, preventing imbalance in the load demand and power generation, thus leading to greater power quality and network reliability. We use a method called Recurrent Neural Network (RNN) to anticipate the future hourly demand of Gothatar feeder, Nepal Electricity Authority (NEA). The RNN Network is build, trained and test with historical hourly demand data along with six different input variables and used for the prediction of day ahead hourly demand. The output from RNN model is validated with the real hourly demand data collected from NEA.