AI-based Short-Term Electric Time Series Forecasting
Anamika Singh1, Manish Kumar Srivastava2, Navneet Kumar Singh3

1Anamika Singh, Department of Electrical Engineering, Sam Higginbottom University of Agriculture, Technology and Sciences, Prayagraj, India.
2Manish Kumar Srivastava, Department of Electrical Engineering, Sam Higginbottom University of Agriculture, Technology and Sciences, Prayagraj, India.
3Navneet Kumar Singh, Department of Electrical Engineering, Motilal Nehru National Institute of Technology, Prayagraj, India.
Manuscript received on 09 August 2019 | Revised Manuscript received on 17 August 2019 | Manuscript published on 30 August 2019 | PP: 3255-3261 | Volume-8 Issue-10, August 2019 | Retrieval Number: J11860881019/2019©BEIESP | DOI: 10.35940/ijitee.J1186.0881019
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Abstract: 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.
Keywords: Artificial Neural Network, Mean Absolute Percentage Error, Power System Planning.

Scope of the Article: Artificial Intelligence