Abstract: Smart Grid is a recent area where the key feature is shift the present power system approach. But, the challenges of upgrade this present power system are several, such as: how to add reliable links between customers' home and data centers to enable smart meter sending power consumption data? how to avoid big data and bottleneck on backbone to transmission of millions of these customers' devices? On the other hand, smart meter can be treated as a sensor network device. Thus, it can use the same data reduction mechanisms that have been studied in wireless sensor network to decrease its traffic. This paper proposes a data reduction approach based on prediction by simple linear regression to avoid flow of readings between smart meter and smart grid system. Although the approximation performed by linear regression increases the prediction error in some instances, we have implemented an adaptive mechanism (Adaptive Simple Linear Regression - ASLR) that checks if the prediction error or lack of relationship between the modeled samples is harmful to our data reduction approach. Thereby, two ways have been deployed to tune the samples window (amount of readings) for improve own approach. These samples are smart meter readings which are modeled in a linear regression function for recovering data instead of sending it to the datacenter. One mechanism adjusts samples window based on prediction error and another one adjusts samples window based on Pearson's coefficient. Also, some experiments were conducted using the Wavelet as mechanisms for data reduction but the best results were obtained using ASLR, which saves on the data transmission to a controllable level of error. This article contains all steps to reach at a more robust solution, which serves as a guide to others as lessons learned.