• Mr.Y.Lakshmi Manoj Kumar
• Mrs. A.Sirisha

Recurrent Neural Network (RNN) based forecasting mechanisms have proved their significance to anticipate in preoperative outcomes to improve the decision making on the future course of actions. The RNN models have long been used in many application domains which needed the identification and prioritization of adverse factors. Several prediction methods are being popularly used to handle forecasting problems. This method demonstrates the capability of RNN model to forecast the electricity load of upcoming days from a city which can be considered to increase or decrease the generation of load. Our proposed method integrates a numeral of approach. In this work, we develop the project of electricity forecast which can be able to predict outcomes of total load consumed.

Recurrent Neural Network, Forecasting, Prioritization,LSTM

"Electricity Load Forecasting Using LSTM & RNN", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 2, page no.e333-e336, February-2024, Available :http://www.jetir.org/papers/JETIR2402448.pdf

"Electricity Load Forecasting Using LSTM & RNN", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 2, page no. ppe333-e336, February-2024, Available at : http://www.jetir.org/papers/JETIR2402448.pdf