Hybrid Time-Series Methodologies for Short-Term Load Forecasting

  • Naimul Hasan, Ibraheem, Mohammad Sajid


The work aimed in this paper is to develop an effective integrated load forecasting technique using the real time data based on energy consumption for minute to-minute, hourly, daily, monthly and yearly at substation level. The various techniques like time series, fuzzy, artificial neural network, wavelet and hybrid time series methodologies are implemented. A comparatively analysis is also carried out as shown in the paper for the accurate prediction of short-term load forecasting. The result shown by ANN wavelet base load forecast reflect the effective trend for the future electric load demands more accurately. The analysis for the possible error in prediction of forecast by these implemented techniques is also presented considering the different criterion and objective functions.

How to Cite
Naimul Hasan, Ibraheem, Mohammad Sajid. (2020). Hybrid Time-Series Methodologies for Short-Term Load Forecasting. International Journal of Advanced Science and Technology, 29(05), 4209 - 4220. Retrieved from http://sersc.org/journals/index.php/IJAST/article/view/13691