Goals programming multiple linear regression model for optimal estimation of Electrical Engineering staff according to load Demand

The linear multiple regression model is one of the prediction models whose parametric estimations could be achieved in different methods. Ordinary Least Square error (OLS) is most popular in this field of application. Although it could accurately achieve the estimation task, it fails in processing the multiple objective models. From the other side of view, the load demand for electrical energy continuously rises around the world. The governments always tackle the increase in electrical load demanding by establishing more electrical power plants and more power distribution directories. Future prediction for several electrical engineers to manage and provide technical supports for these plants and directories becomes, nowadays, urgent. This paper addresses the estimating drawback of OLS by employing the Goals Programming (GP) in the field of parametric estimation. The validity of the proposed method was applied to estimating the required number of electrical engineers, in the next coming years, as the electrical load considerably increases. Thereby, the GP was used, in this work, to determine the best linear representation for a set of data. The obtained results proved that the (GP) method is more flexible and efficient in dealing with such subject area especially in the case of multiple objective models.