• S. Prabakaran
• Muhammad Ibrahim Faruqi
• Gaurav Raj

Load forecasts are very important for the smooth functioning of the electrical industry. It has various applications, including energy purchasing and production, load switching, infrastructure development and contract evaluation. Energy planning and needs-based power generation plays an important role in this scenario. Electricity load forecasting knowledge can be used in the development of smart grids. Many mathematical methods have been developed for load prediction. This article analyzes various short-term load forecasting techniques that help predict performance for a short time. These different techniques include time series analysis, regression models, and deep learning models such as artificial neural networks and recurrent neural networks. A comparison of the merits and disadvantages of various load prediction methods is also performed.

"A Literature Survey On Short Term Electricity Load Forecasting Methods", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 4, page no.461-465, April-2019, Available :http://www.jetir.org/papers/JETIR1904D60.pdf

"A Literature Survey On Short Term Electricity Load Forecasting Methods", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 4, page no. pp461-465, April-2019, Available at : http://www.jetir.org/papers/JETIR1904D60.pdf