Open AccessAI-based Short-Term Electric Time Series Forecasting
Author(s) -
Abhishek Singh,
Manish Kumar Srivastava,
Navneet Singh
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.j1186.0881019
Subject(s) - autoregressive integrated moving average , moving average , autoregressive–moving average model , artificial neural network , demand forecasting , time series , electric power system , moving average model , autoregressive model , computer science , series (stratigraphy) , term (time) , electrical load , power (physics) , artificial intelligence , engineering , econometrics , machine learning , operations research , mathematics , voltage , paleontology , physics , quantum mechanics , biology , computer vision , electrical engineering
In current scenario of various electrical profiles like load profile, energy met profile, peak demand, etc. are very complex and therefore affected proper power system planning. Electrical forecasting is an important part in proper power system planning. Classical models, i.e., time series models and other conventional models are restricted for linear and stationary electrical profiles. Consequently, these models are not suitable for accurate electrical forecasting. In this paper, artificial neural network (ANN) based forecasting models are proposed to forecast hydro generation, energy met and peak demand. Auto-regressive (AR), moving average (MA), Auto-regressive Moving average (ARMA) and auto-regressive integrated moving average (ARIMA) are also developed to show the effectiveness of ANN based models over time series models. Additionally, best selection of hidden neurons in ANN is also shown here.