Estimation of wind resource in a given area helps in identifying potential sites for establishing wind farm and aids in the calculation of annual energy produced. Estimation of annual energy improves the wind power penetration in the electricity grid and in electricity trading. In this paper, wind resource estimation has been carried out using wind speed forecasting models and wind turbine power curve model. The time series model of wind speed for day ahead forecasting is developed based on linear and nonlinear autoregressive models with and without exogenous variables. The daily wind speed data of five different locations in New Zealand have been used for this analysis and the annual energy produced has been obtained. The standard deviation between the mean wind speed of the previous day and the mean wind speed during corresponding day five years and ten years ago has been used as exogenous variables. The neuralnet based non-linear model built using exogenous variables (NLARX) performs better in three locations and wavenet based non-linear model performs better in the remaining two locations. Wind resource is estimated using a wind turbine power curve modeled using a five parametric logistic expression, whose parameters were solved using Differential Evolution (DE). (C) 2015 Elsevier Ltd. All rights reserved.