Green Energy Management in Manufacturing Based on Demand Prediction by Artificial Intelligence—A Review
Abstract
:1. Introduction
2. Sustainable Manufacturing System Design
- Improving system efficiency and productivity as well as cost balance;
- Minimizing the impact on the environment, also indirectly by reducing energy consumption and CO2 emissions;
- Reducing production losses and the amount of waste [4].
- The measurement of energy consumption, CO2 emissions, and other factors using various energy sources (both for powering machines, lighting, heating, water heating, cooling, etc.: crude oil, oil, solar energy, etc.);
- A mathematical model taking into account real-world constraints, including technological, economic, and ecological ones, with the objective function (with weights specific to a given enterprise/industry) minimizing costs, energy consumption, and CO2 emissions within the production system itself and the entire enterprise;
- Determining the uncertainty (also: randomness characteristics, if necessary) of input parameters using, e.g., a fuzzy model (including directed fuzzy numbers);
- Analyses and decisions regarding the number of machines/devices for each stage of the production process in connection with the amount of energy and material flow and their DT;
- The construction of an optimizing AI/ML model;
- The validation of the developed system model based on real data, e.g., case study;
- Inclusion at the earliest possible stage of planning and design;
- Allows to fully explore the inclusion of ecological (e.g., energy consumed) and economic aspects;—It involves the operations of the production system;
- Uses computer simulation tools;
- It would provide a framework for a hybrid approach to optimization;
3. Materials and Methods
3.1. Dataset
- Research Question 1 (RQ1): the evolution of research topics/issues over time;
- RQ2: the geographical distribution of research/publications;
- RQ3: the authors and publications with the greatest influence;
- RQ4: the identification of cooperation networks between researchers and institutions;
- RQ5: the topics that may shape future research agendas.
3.2. Methods
4. Results
5. Discussion
5.1. Limitations of Previous Studies
- Implementing AI and advanced demand forecasting systems requires significant investment in technology solutions and staff skills;
- Integrating AI-based systems with the existing production processes and legacy systems can be complex and time-consuming;
- Data quality and availability can be limited, and comprehensive data collection in production environments can be difficult—in addition, handling and processing large amounts of data for AI applications can create significant data privacy and security issues;
- Scaling AI solutions across different manufacturing plants and systems can be difficult due to varying levels of technological sophistication;
- AI/ML systems are difficult to understand and trust their predictions, including for critical manufacturing decisions;
- AI systems can consume significant amounts of energy;
- Rapidly changing market demands may exceed AI’s adaptability, leading to inefficiencies in energy management;
- The regulatory challenges for both the AI Act and green energy management can be complex and vary greatly from region to region/country to country;
5.2. Directions for Further Research
- Improved data collection methods, including advanced sensors and IIoT devices to improve the accuracy and detail of data collection;
- The creation of new ML algorithms tailored to complexity and specific needs;
- Hybrid models created by combining artificial intelligence with traditional forecasting methods to improve the reliability and accuracy of forecasts;
- Developing methods to increase the transparency and understanding of AI forecasts;
- Exploring ways to reduce the energy consumption of AI systems themselves;
- Developing a framework for integrating AI-based demand forecasting systems with the existing energy production and management systems;
- Strengthening cyber-security measures to protect against breaches and attacks on sensitive data;
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Journal | Number of Articles [-] |
---|---|
Sustainability [18] | 1 |
Journal of Composites Science [19] | 1 |
Applied Sciences [20] | 1 |
Journal of Electrochemical Science and Engineering [21] | 1 |
International Journal of Green Energy [22] | 1 |
International Conference on Advances in Green Energy (ICAGE) [23,24] | 2 |
Solar Energy [25] | 1 |
4th International Conference on Artificial Intelligence and Evolutionary Computations in Engineering Systems (ICAIECES) [26] | 1 |
Green Energy & Environment [27] | 1 |
Biomass Conversion and Biorefinery [28] | 1 |
ACS Applied Materials & Interfaces [29] | 1 |
Futures [30] | 1 |
IEEE Sensors Journal [31] | 1 |
Sensors [32] | 1 |
Journal of Experimental & Theoretical Artificial Intelligence [33] | 1 |
Applied Energy [34,35,36] | 3 |
Machine Learning and Computer Vision for Renewable Energy [37] | 1 |
Virtual and Physical Prototyping [38] | 1 |
Science of the Total Environment [39] | 1 |
International Conference on Artificial Intelligence and Computer Vision (AICV2020) [40] | 1 |
Engineering Applications of Artificial Intelligence [41] | 1 |
Feature | FedPT-V2G [35] | GGNet [36] | Multi-Node Load Forecasting [41] |
---|---|---|---|
Type | Security-enhanced federated transformer learning for real-time V2G dispatch with non-IID data | A novel graph structure for power forecasting in renewable power plants considering temporal lead-lag correlations | Multi-node load forecasting based on multi-task learning with modal feature extraction |
Technologies | Deep learning Federated learning Regularization to align local models | Granular-based graph neural networks Two-dimensional convolutional neural network Graph attention network | Gated temporal convolutional network |
Algorithm | Proximal algorithm and Transformer model to handle Non-IID data in V2G tas | Graph structure can adapt to the lead-lag characteristic arising from the air flow, allowing it to dynamically capture the effects of lead and lag among RPPs. | IGTCN module is designed to extract the coupling features from different node loads |
Dataset | End-to-end learning based on historical data to make V2G decisions in real-time | Real-world datasets, with wind power plants and photovoltaic power plants | Data from the New Zealand distribution network and AEMO |
Application | Load shifting and PV self-consumption under diverse uncertainties | Power forecast for each renewable power plant (RPP) in the renewable energy clusters | Multi-node load forecasting in the power system |
Performance | Achieve similar performance to centralized learning on both IID and Non-IID data | The proposed graph structure can reflect the lead-lag characteristics among RPPs caused by the atmospheric flow, obtaining better correlation representations among RPPs. | The multi-task deep neural network exhibits the accuracy of multi-node load forecasting |
Accuracy for balanced datasets: 98.93%; imbalanced datasets: 92.20% | Forecast steps decreased on average by 48.95% and 18.75%, respectively | MAPE decreased by 17.04% and 3.92% in non-aggregation and aggregation situations, respectively |
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© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
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Rojek, I.; Mikołajewski, D.; Mroziński, A.; Macko, M. Green Energy Management in Manufacturing Based on Demand Prediction by Artificial Intelligence—A Review. Electronics 2024, 13, 3338. https://doi.org/10.3390/electronics13163338
Rojek I, Mikołajewski D, Mroziński A, Macko M. Green Energy Management in Manufacturing Based on Demand Prediction by Artificial Intelligence—A Review. Electronics. 2024; 13(16):3338. https://doi.org/10.3390/electronics13163338
Chicago/Turabian StyleRojek, Izabela, Dariusz Mikołajewski, Adam Mroziński, and Marek Macko. 2024. "Green Energy Management in Manufacturing Based on Demand Prediction by Artificial Intelligence—A Review" Electronics 13, no. 16: 3338. https://doi.org/10.3390/electronics13163338