The present dissertation investigates the use of multivariate regression models from a Bayesian point of view. These models were used to estimate the electric load behavior of household end uses. A conditional demand structure was used considering its application to the demand management of the residential and commercial consumers. This work is divided in three main parts: a description of the classical statistical methodologies used for the electric load prediction, a study of the multivariate regression models using a Bayesian approach and a further development of the model applied to a case study. A preliminary revision of the CDA structure was done for univariate cases using multiple regression. A similar revision was done for other cases using multivariate regression (Seemingly Unrelated). In those cases, the behavior of the structure depends on the correlation between a minimization of the daily demand errors and the methodologies used for the electric load prediction. The study on multivariate regression models (Seemingly Unrelated) was done from a Bayesian point of view. This kind of study is very important for the prediction methodology. When developing the model, the electric load curves of the main household appliances were predicted using a Bayesian approach. This fact showed the performance of the metodology on the capture of two types of information: Engineering prediction and CDA prediction. The results obtained using the above method, for describing the data, were better than the classical models.