Using Industry 4.0’s Big Data and IoT to Perform Feature-Based and Past Data-Based Energy Consumption Predictions

Abstract

Industry 4.0 and its technologies allow advancements in communications, production and management efficiency across several segments. In smart grids, essential parts of smart cities, smart meters act as IoT devices that can gather data and help the management of the sustainable energy matrix, a challenge that is faced worldwide. This work aims to use smart meter data and household features data to seek the most appropriate methods of energy consumption prediction. Using the Cross-Industry Standard Process for Data Mining (CRISP-DM) method, Python Platform, and several prediction methods, prediction experiments were performed with household feature data and past consumption data of over 470 smart meters that gathered data for three years. Support vector machines, random forest regression, and neural networks were the best prediction methods among the ones tested in the sample. The results help utilities (companies that maintain the infrastructure for public services) to offer better contracts to new households and to manage their smart grid infrastructure based on the forecasted demand.

Curating Multimodal Satellite Imagery for Precision Agriculture Datasets with Google Earth Engine

“…Figure 2 shows the life cycle of a data mining project, as defined as a reference model of the CRISP-DM [7]. CRISP-DM has been used in several studies such as predicting energy consumption [8] and automating seed counts [9]. In the context of a methodology, it encompasses detailed depictions of the standard project phases along with the associated tasks for each phase.…”

Machine Learning Applied to Logistics Decision Making: Improvements to the Soybean Seed Classification Process

“…Machine learning methods have been successfully applied to solve several problems in production and logistics, such as forecasting customer demands [14], predicting energy consumption [15,16], or making travel time predictions [17]. In agribusiness, machine learning technique applications include crop yield production [18], predicting soil properties [19], irrigation management [20], weather prediction [21], crop quality [22], harvesting [23], demand prediction [24], detecting vegetable diseases [25], and determining crop production [26].…”

The Role of Performance in Smart Meter’s Acceptance: A Survey in Joinville, Brazil

“…Brazil's population has already surpassed 213.3 million [26]. Against the backdrop of this population growth, the Brazilian government is keen on modernizing its energy grid for several compelling reasons, such as the increasing share of renewable energy sources in its energy matrix [27], the implementation of variable energy tariffs, comprehended locally as the white tariff [28], the increase in the number of consumers that are producing their own energy, also named of "prosumers" [29], and the decrease of non-technical energy losses [29,30]. With these foundational principles in mind, the Brazilian government actively promotes public policies and investments in smart meter adoption, thereby replacing conventional electricity meters with smart meters [31].…”