Smart Energy Management System: Design of a Monitoring and Peak Load Forecasting System for an Experimental Open-Pit Mine

Abstract

Digitization in the mining industry and machine learning applications have improved the production by showing insights in different components. Energy consumption is one of the key components to improve the industry’s performance in a smart way that requires a very low investment. This study represents a new hardware, software, and data processing infrastructure for open-pit mines to overcome the energy 4.0 transition and digital transformation. The main goal of this infrastructure is adding an artificial intelligence layer to energy use in an experimental open-pit mine and giving insights on energy consumption and electrical grid quality. The achievement of these goals will ease the decision-making stage for maintenance and energy managers according to ISO 50001 standards. In order to minimize the energy consumption, which impact directly the profit and the efficiency of the industry, a design of a monitoring and peak load forecasting system was proposed and tested on the experimental open-pit mine of Benguerir. The main challenges of the application were the monitoring of typical loads machines per stage, feeding the supervisors by real time energy data on the same process SCADA view, parallel integrating hardware solutions to the same process control system, proposing a fast forest quantile regression algorithm to predict the energy demand response based on the data of different historical scenarios, finding correlations between the KPIs of energy consumption, mine production process and giving global insights on the electrical grid quality.

Development of Surface Mining 4.0 in Terms of Technological Shock in Energy Transition: A Review

“…In particular, smart energy management systems based on artificial intelligence are gradually being introduced at surface mines, which make it possible to optimize the energy consumption of mining equipment and significantly reduce it by analyzing the incoming data in real time (Figure 18) [138].…”

Smart Energy Management: A Comparative Study of Energy Consumption Forecasting Algorithms for an Experimental Open-Pit Mine

“…In this context, there has not been much research on energy forecasting, particularly in the mining industry and more specifically in the open-pit mines. Oussama et al [24] introduced a rapid forest quantile regression technique to forecast the energy demand response based on data from different historical scenarios in an experimental open-pit mine, which is one of the new extant studies in this field. Our work comes to investigate further the energy forecasting in the open-pit mine using four well-known ML techniques namely SVM, ANN, DT, and RF to facilitate the evaluation of the energy consumption and draw early expectations for energy use within the mining industry which helps significantly in the decision-making process.…”

An AI-Layered with Multi-Agent Systems Architecture for Prognostics Health Management of Smart Transformers: A Novel Approach for Smart Grid-Ready Energy Management Systems

“…All previously discussed layers are connected to the smart energy management system to make decisions in the grid in order to maximize the lifetime of the power transformer by controlling the load connected in the grid, adjusting the power factor and proposing maintenance scheduling of the power transformer. this smart energy management system is connected to other power transformers in the grid, making the learning and the decisions distributed, and the system is also predicting the load and the power flow behavior within the grid using different algorithms developed in recent papers in different applications, for example, in the mining industry [14,15] and hotel building [72] and also predicting the defects of loads, for example, squirrel cage induction motors [25]. Therefore, the smart energy management system is a distributed system connected to different smart grid components [69].…”

“…Figure 1 shows the main components of the power transformer architecture. For self-diagnostics and reliability, which are connected to prognostic and health management proposed in [11,13], the goal is making the power transformer detect failure and act with the help of a smart energy management system which is connected to load management and energy demand and peak load prediction proposed in [14,15]. self-diagnostics and reliability, which are connected to prognostic and health management proposed in [11,13], the goal is making the power transformer detect failure and act with the help of a smart energy management system which is connected to load management and energy demand and peak load prediction proposed in [14,15].…”

“…For self-diagnostics and reliability, which are connected to prognostic and health management proposed in [11,13], the goal is making the power transformer detect failure and act with the help of a smart energy management system which is connected to load management and energy demand and peak load prediction proposed in [14,15]. self-diagnostics and reliability, which are connected to prognostic and health management proposed in [11,13], the goal is making the power transformer detect failure and act with the help of a smart energy management system which is connected to load management and energy demand and peak load prediction proposed in [14,15]. The power transformer is responsible for electrical grid stability in different power flow levels of production, transmission, and distribution while reporting and communicate all necessary data for the energy management system, such as electrical data from power meters, dissolved gas analysis from different gas sensors, and the temperature of different components.…”