The forecast of electricity demand in recent years is becoming increasingly relevant because of market deregulation and the introduction of renewable resources. To meet the emerging challenges, advanced intelligent models are built to ensure precise power forecasts for multi-time horizons. The use of intelligent forecasting algorithms is a key feature of smart grids and an effective tool of resolving uncertainty for better cost and energy efficiency decisions like scheduling the generations, reliability and power optimization of the system, and economic smart grid operations. However, prediction accuracy in forecasting algorithms is highly demanded since many important activities of power operators like load dispatch depend upon the short-term forecast. This paper proposes a model for the estimation of the consumption of electricity in Agartala, Tripura in India, which can accurately predict the next 24 h of load with and estimation of load for 1 week to 1 month. A number of specific characteristics in the city have been analysed in order to extract variables that could affect the pattern of electricity consumption directly. In addition, the present paper shows the way to significantly improve the accuracy of the prediction through ensemble machine learning process. We demonstrated the performance of individual Random forest and XGBoost along with their ensemble. The RF and XGBoost ensemble obtained an accuracy with an improvement of 15–29%. The analyses or findings also provide interesting results in connection with energy consumption.