The purpose of this two-part study is to model the effects of large penetrations of offshore wind power into a large electric system using realistic wind power forecast errors and a complete model of unit commitment, economic dispatch, and power flow. The chosen electric system is PJM Interconnection, one of the largest independent system operators in the U.S. with a generation capacity of 186 Gigawatts (GW). The offshore wind resource along the U.S. East Coast is modeled at five build-out levels, varying between 7 and 70 GW of installed capacity, considering exclusion zones and conflicting water uses. This paper, Part I of the study, describes in detail the wind forecast error model; the accompanying Part II describes the modeling of PJM's sequencing of decisions and information, inclusive of day-ahead, hour ahead, and real-time commitments to energy generators with the Smart-ISO simulator and discusses the results. Wind forecasts are generated with the Weather Research and Forecasting (WRF) model, initialized every day at local noon and run for 48 h to provide midnight-to-midnight forecasts for one month per season. Due to the lack of offshore wind speed observations at hub height along the East Coast, a stochastic forecast error model for the offshore winds is constructed based on forecast errors at 23 existing PJM onshore wind farms. PJM uses an advanced, WRF-based forecast system with continuous wind farm data assimilation. The implicit (and conservative) assumption here is that the future forecast system for offshore winds will have the same performance as the current PJM's forecast system for onshore winds, thus no advances in weather forecasting techniques are assumed. Using an auto-regressive moving-average (ARMA) model, 21 equally-plausible sample paths of wind power forecast errors are generated and calibrated for each season at a control onshore wind farm, chosen because of its horizontally uniform landscape and large size. The spatial correlation between pairs of onshore wind farms is estimated with an exponential function and the matrix of error covariance is obtained. Validation at the control farm and at all other onshore farms is satisfactory. The ARMA model for the wind power forecast error is then applied to the offshore wind farms at the various build-out levels and combined with the matrix of error covariance to generate multiple samples of forecast errors at the offshore farms. The samples of forecast errors are lastly added to the WRF forecasts to generate multiple samples of synthetic, onshore-based, actual offshore wind power for use in Part II. (C) 2016 Elsevier Ltd. All rights reserved.