EnergySaver Software Manual

TL;DR

The paper presents a comprehensive manual for the EnergySaver software, which monitors and forecasts electric energy consumption using IoT, embedded systems, and LSTM neural networks, aimed at promoting energy efficiency and sustainability.

Contribution

It provides a detailed implementation guide for EnergySaver, integrating IoT, embedded systems, and LSTM models, facilitating future research and practical application.

Findings

EnergySaver effectively monitors energy consumption.

The software accurately forecasts next month's energy usage.

It utilizes open source IoT and neural network technologies.

Abstract

Energy efficiency is a topic that has attracted the attention of researchers in recent years, in order to seek sustainability solutions for energy production and reduction of its costs, aiming to provide a balance between development and protection of natural resources. Thus, we proposed the EnergySaver software that has as its objective the monitoring of electric energy consumption, from data capture to consumption forecast for the following month. To create Energy Saver, we used Open Source technologies applied to the Internet of Things (IoT), embedded systems, and Long Short-Term Memory Neural Networks (LSTM). However, in order to have harmony between the current researchers and those who may manipulate this software in the future, it is essential to create a Software Manual, where all the details of its implementation are described in detail. Therefore, this article describes all the steps for the implementation of the system, from the methodological scheme of the system, its modeling with UML, to the modules that compose it, becoming a Manual for its use.