T R A C K       P A P E R
ISSN:2394-3661 | Crossref DOI | SJIF: 5.138 | PIF: 3.854

International Journal of Engineering and Applied Sciences

(An ISO 9001:2008 Certified Online and Print Journal)

Short Term Load Forecasting Using Artificial Neural Network & Time Series Methods

( Volume 7 Issue 4,April 2020 ) OPEN ACCESS

Suman Adhikari, Prof. Dr. Laxman Poudel


Short Term Load Forecasting (STLF), Neural Network, Backpropagation algorithm, Moving average


Short term Electric load forecasting is an important aspect of power system planning and operation for utility companies. Short term load forecasting (STLF) has always been one of the most critical, sensitive and accuracy demanding factors of the power systems. An accurate STLF improves not only the systems economic viability but also its safety, stability and reliability. The researcher presented in this works support Artificial Neural Network and Time Series Methods techniques in short term forecasting. This paper presents an investigation for the short term (one day to seven days, & every months of one year) load forecasting the load demand of Nepal Electricity Authority (NEA) in Bishnumati Feeder of Balaju Substation, by using artificial neural network and time series methods.



Paper Statistics:

Total View : 24 | Downloads : 15 | Page No: 11-19 |

Cite this Article:
Click here to get all Styles of Citation using DOI of the article.