This work proposes an adaptive data compression mechanism to reduce the flow in the communication infrastructure of the advanced metering infrastructure (AMI), providing a more agile communication and thus allowing the scalability of the internet of things (IoT) applications in the demand side management (DSM) scenario, to automate consumption management and distributed power generation. Such a mechanism is built into a smart meter (SM) and aims to reduce the amount of data sent to an electric power company (EPC). Additionally, this mechanism makes use of curve adjustment to find, in an adaptive way in run time, a functional model, among the available options, that best adapts to the consumption measures inferred by the SM. The experiments present compression rates where the data recovered by the functional model respect a pre-established error threshold. Thus, allowing the scalability of DSM applications in this infrastructure. Therefore, it can to be stated that the presence of the data compression mechanism proposed in this work is satisfactorily adequate for the DSM scenario of consumption and distributed generation achieving compression rates close to 96%, as well as provides the possibility of being adapted to other contexts, such as WSN.

这项工作提出了一种自适应数据压缩机制，以减少高级计量基础架构（AMI）的通信基础架构中的流量，提供更敏捷的通信，从而在需求方管理中实现物联网（IoT）应用程序的可伸缩性（ DSM）方案，以自动化消耗管理和分布式发电。这种机制内置于智能电表（SM）中，旨在减少发送给电力公司（EPC）的数据量。另外，该机制利用曲线调整在运行时以自适应方式在可用选项中找到最适合SM推断的消耗量的功能模型。实验给出了压缩率，其中通过功能模型恢复的数据遵守预先确定的错误阈值。因此，允许在此基础架构中扩展DSM应用程序。因此，可以说，这项工作中提出的数据压缩机制的存在足以满足DSM消耗和分布式发电实现接近96％压缩率的情况，并提供了适用于其他方面的可能性。上下文，例如WSN。