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Demand profile analysis is very crucial in the evolution of intelligent demand side management and power dispatch planning practices. This is so, because not only a prior knowledge of the demand fluctuation is necessary, but also the sector of the demand causing the fluctuations. In this paper, a detailed demand profile analysis of Nigeria's annual electricity demand is presented using systems optimization approach of MESSAGE model. Since the MESSAGE load evaluation screen does not incorporate a module for the evaluation of peak to offpeak demand ratio, an extension of the model is proposed for this evaluation. The simulation results for the services, industrial and residential demand subsectors as well as the total demand were within expected pattern, with the services sector presenting the highest peak to off-peak demand ratio of 2.137. I. INTRODUCTIONDemand profile analysis normally involve load forecast and load characterization. Recently, systems engineering models such as ENPEP, MAED have been widely deployed for load analysis [1,2]. While these models are quite exquisite for demand projections, their extensive data requirement for a detailed demand characterization makes their deployment a daunting task. Here, an attempt has been made to carry out the demand characterization using MESSAGE systems optimization model, rather than the traditional MAED-el module being used by the Nigerian country study team. MAED-el converts the given demand to load curves using appropriate load modulation coefficients, which requires extensive data definition and computations [3,4].MESSAGE stands for a Model for Energy Supply Strategy, Alternatives and their General Environmental Impact. It is a dynamic linear programming model designed and developed at IIASA for the optimization of energy supply and utilization. The data structures have been designed to a databank based on keywords, which simplifies automated data processing. In the mathematical formulation, the multiobjective options has been employed, while the reference point optimization method, adapted to dynamic modeling into a reference trajectory optimization method, is implemented [5]. In demand characterization it offers the advantage of utilizing ratios of demand curve data, rather than absolute values. However, the results are based on real time slices as against load duration curve used in MAED, MARKAL and WASP [1,3].