Electric load forecasting in smart grids using Long-Short-Term-Memory based Recurrent Neural Network
Abstract
Electric load forecasting plays a vital role in smart grids. Short term electric load forecasting forecasts the load that is several hours to several weeks ahead. Due to the nonlinear, non-stationary and nonseasonal nature of the short term electric load time series in small scale power systems, accurate forecasting is challenging. This paper explores Long-Short-Term-Memory (LSTM) based Recurrent Neural Network (RNN) to deal with this challenge. LSTM-based RNN is able to exploit the long term dependencies in the electric load time series for more accurate forecasting. Experiments are conducted to demonstrate that LSTM-based RNN is capable of forecasting accurately the complex electric load time series with a long forecasting horizon. Its performance compares favorably to many other forecasting methods.