In this paper, we propose an energy-efficient resource allocation algorithm in the downlink of a multicell large-scale distributed antenna system. A non-convex optimization problem is formulated to maximize the energy efficiency (EE) of the entire network. The optimization problem considers the circuit power consumption, the minimum data rate requirements, and the maximum allowed transmit power per remote radio head. The optimization problem is transformed into an equivalent optimization problem in the subtractive form using the Dinkelbach method. An iterative algorithm for the optimization solution is proposed in which antenna selection with user scheduling and power allocation are implemented separately. To tackle this significant computational complexity, a norm-based joint antenna and the user selection algorithm that uses Tabu Search are proposed to select optimum antennas and users for transmission. Next, a Lagrangian dual decomposition method is adopted to derive a power allocation strategy that can run independently in each cell. Simulation results show that the proposed algorithm can achieve its maximum EE in several iterations, and that increasing the number of antennas or users does not necessarily result in a higher EE. In addition, the proposed iterative scheme is always able to achieve a better performance than the traditional norm-based algorithm and the simplified cloud radio access network based energy efficient power allocation scheme, no matter how many antennas or users are selected. [ABSTRACT FROM AUTHOR]