In this paper, we address input load/generation uncertainty in the stochastic economic dispatch and unit commitment for forecasting and then scenario generation purposes. We propose a constrained multi-variate linear regression formulation to model the joint spatial-temporal distribution of load forecasts and renewable energy productions such as solar cells and wind turbines at various locations. This model adds l 0 and l 1 constraints to simulate exact counterparts for LASSO and Ridge regression models. We eliminate heavy tails from forecasted errors using extreme value theory to generate robust solutions for stochastic economic dispatch and unit commitment. We then employ a Monte Carlo sampling technique to generate scenarios. Finally, we propose a new exact model for scenario reduction. For this paper, we used data published at NYISO ^a and ERCOT ^b websites. Our numerical results show how energy loads and renewable energies can be forecasted using our methodology and forecasted values predict actual values.