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

Abstract

The mining industry’s increased energy consumption has resulted in a slew of climate-related effects on the environment, many of which have direct implications for humanity’s survival. The forecast of mine site energy use is one of the low-cost approaches for energy conservation. Accurate predictions do indeed assist us in better understanding the source of high energy consumption and aid in making early decisions by setting expectations. Machine Learning (ML) methods are known to be the best approach for achieving desired results in prediction tasks in this area. As a result, machine learning has been used in several research involving energy predictions in operational and residential buildings. Only few research, however, has investigated the feasibility of machine learning algorithms for predicting energy use in open-pit mines. To close this gap, this work provides an application of machine learning algorithms in the RapidMiner tool for predicting energy consumption time series using real-time data obtained from a smart grid placed in an experimental open-pit mine. This study compares the performance of four machine learning (ML) algorithms for predicting daily energy consumption: Artificial Neural Network (ANN), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF). The models were trained, tested, and then evaluated. In order to assess the models’ performance four metrics were used in this study, namely correlation (R), mean absolute error (MAE), root mean squared error (RMSE), and root relative squared error (RRSE). The performance of the models reveals RF to be the most effective predictive model for energy forecasting in similar cases.

An approach based on Machine Learning Algorithms, Geostatistical Technique, and GIS analysis to estimate phosphate ore grade at the Abu Tartur Mine, Western Desert, Egypt

“…Four metrics -R, MAE, root mean squared error (RMSE), and root relative squared errorwere used in the study to evaluate the performance of the models (RRSE). The results show that RF is the most accurate predictive model for energy forecasting [13].…”

“…The Decision Tree (DT) technique, as depicted in Figure 4, uses a flowchart representing a tree for categorizing data. A flexible method called decision trees is capable of learning from a large amount of training data [13], [26]. The DT is easier to understand than other data-driven approaches and does not require a deep understanding of calculations.…”

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.…”