Usually, disaggregated electricity load curves are estimated by using Top-Down or Bottom-Up approaches. The former requires estimating weightings for downscaling aggregated information, while the latter requires extrapolating micro-level information. In both cases, estimation would ideally be based on as much regional and sector specific information as possible, in order to obtain a realistic representation of the magnitude and temporal pattern of a regional sector's electricity consumption. Typically, such attempts are significantly hampered by issues of limited and possibly inconsistent data, differing levels of detail, and mismatching data classifications. This paper proposes a novel nonlinear programming model based on the maximum entropy approach. The model allows for electricity load curve estimation at arbitrary spatial, sectoral and temporal resolution, from partial and possibly inconsistent information. The proposed model integrates and systematically utilizes data usually used by either Top-Down or Bottom-Up approaches. In a case study using German data it is shown that the model combines the strength of both and, at the same time, overcomes the challenges specific to Top-Down or Bottom-up estimation.