Short- to Mid-Term Prediction for Electricity Consumption Using Statistical Model and Neural Networks

Abstract

Electricity is one of the key role players to build an economy. Electricity consumption and generation can affect the overall policy of the country. This opens an area for some intelligent system that can provide future insights. Intelligent management for electric power consumption requires future electricity power consumption prediction with less error. These predictions provide insights for making further decisions to smooth line the policy and help to grow economy of the country. Future prediction can be categorized into three categories namely (1) long-term, (2) short-term, and (3) mid-term predictions. For our study, we consider mid-term electricity consumption prediction. Dataset is provided by Korea Electric power supply to get insights for metropolitan city like Seoul. Dataset is in time series so we require to analyze dataset with statistical and machine learning models that can support time series dataset. This study provides experimental results from the proposed models. Our proposed models for provided dataset are ARIMA and LSTM, which look promising as RMSE for training is 0.14 and 0.20 RMSE for testing.