Many domains require the use of sophisticated simulators to adequately model the effect of chosen alternatives on the decision maker's value. Decision support in such complex systems brings unique challenges around efficiency, because simulating each combination of inputs can be time-consuming. In this paper, we conduct a value of information (VOI) analysis to study whether one should purchase information about critical input uncertainties in such complex systems. We propose a novel computational approach, where Gaussian processes are used to model the decision maker's profit as a function of different alternatives and uncertainties. Under this modeling assumption, the expected improvement of the profit is analytically available, which we use to approximate the VOI effectively over batches of simulations, thus avoiding too many computer-intensive evaluations of the system. We illustrate the proposed computational approach with an offshore wind farm maintenance application, where the decision maker relies on outputs from large-scale simulations to determine the optimal vessel fleet mix and number of personnel for operation and maintenance. Such computer-intensive simulations mimic long-term energy production under different input conditions. It is often not possible to explore all the alternatives exhaustively, and one must therefore guide the simulations to run on promising alternatives. We conduct a VOI analysis to study whether one should purchase information about failure rates of wind turbines for this application. The proposed methodologies are general and apply to other domains involving expensive simulators.