Load forecasting is the most fundamental application in Smart-Grid, which provides essential input to Demand Response, Topology Optimization and Abnormally Detection, facilitating the integration of intermittent clean energy sources. In this work, several regression tools are analyzed using larger datasets for urban area electrical load forecasting. The regression tools which are used are Random Forest Regressor, k-Nearest Neighbour Regressor and Linear Regressor. This work explores the use of regression tool for regional electric load forecasting by correlating lower distinctive categorical level (season, day of the week) and weather parameters. The regression analysis has been done on continuous time basis as well as vertical time axis approach. The vertical time approach is considering a sample time period (e.g seasonally and weekly) of data for four years and has been tested for the same time period for the consecutive year. This work has uniqueness in electrical demand forecasting using regression tools through vertical approach and it also considers the impact of meteorological parameters. This vertical approach uses less amount of data compare to continuous time-series as well as neural network techniques. A correlation study, where both the Pearson method and visual inspection, of the vertical approach depicts meaningful relation between pre-processing of data, test methods and results, for the regressors examined through Mean Absolute Percentage Error (MAPE). By examining the structure of various regressors they are compared for the lowest MAPE. Random Forest Regressor provides better short-term load prediction (30 min) and kNN offers relatively better long-term load prediction (24 h). •Urban area electrical energy demand forecasting using regression techniques.•Regional electrical load forecasting with influence of meteorological parameters.•Vertical time approach provides meaningful data correlation.•Random Forest Regressor provides better short-term load prediction (30 min).•kNN offers relatively better long-term load prediction (24 h).