  Forecasting the actual amount of electricity with respect to the need/demand
of the load is always been a challenging task for each power plants based
generating stations. Due to uncertain demand of electricity at receiving end of
station causes several challenges such as: reduction in performance parameters
of generating and receiving end stations, minimization in revenue, increases
the jeopardize for the utility to predict the future energy need for a company
etc. With this issues, the precise forecasting of load at the receiving end
station is very consequential parameter to establish the impeccable balance
between supply and demand chain. In this paper, the load forecasting of G-20
members have been performed utilizing the Recurrent Neural Network coupled with
sliding window approach for data generation. During the experimentation we have
achieved Mean Absolute Test Error of 16.2193 TWh using LSTM.
