Advancing further the field of artificial intelligence and its practical applications partly rests on the tremendous energy requirements needed to build and run artificial intelligence algorithms and models. This contributes to its negative environmental impact and creates concerns about its contribution to the ongoing climate emergency. However, from another side of the debate, it is important to note that AI has been used to improve energy efficiency across different situations or scenarios and in different sectors.
The Emerging Role of Artificial Intelligence in Energy Efficiency: Applications of AI in Energy Management Solutions
Overview of Capabilities
Specific artificial intelligence systems can help human operators make informed decisions. Others are even capable of producing smart and automated decisions with little to no human intervention through data-based and real-time predictions. These capabilities are critical in understanding how AI help in promoting or ensuring energy efficiency.
A specific AI system deployed and tasked with different facets of energy management enables the optimization of energy usage and aid in processes relevant to usage allocation. The following are specific capabilities of AI as applied in energy management:
• Monitoring and Control: An AI system can be used to monitor energy usage in real-time and make predictions for efficient energy use. Examples include automated scheduling of temperature adjustments in buildings based on weather or the number of occupants.
• Demand-Side Management: It can also be used to manage demand for energy and energy allocation. This can help reduce peak loads and improve the stability of a power system or an entire grid. A system can even be used to send notifications to consumers when energy prices are high to encourage them to conserve usage.
• Predictive Maintenance: The predictive capabilities of artificial intelligence can help maintenance staff know the probability of an equipment or system failure and perform needed maintenance operations before an actual failure occurs.
• Development of Technologies: Advances in the applications of AI have resulted in the development of newer technologies and relevant products. Examples include smart thermostats for automated climate control or software programs that monitor and optimize the energy usage of systems like data centers.
Real-World Examples
Understanding further the role of AI in energy efficiency requires taking a look at specific examples of its applications. Take note that its use cases include the energy production sector, especially in power grid management, specific commercial and industrial applications, and applications at the consumer level and device level.
1. Operations of Power Grids in the Energy Sector
A particular AI system can optimize grid operations, improve forecasting, optimize power distribution to prevent system losses, and develop new technologies. Several countries are developing and deploying AI strategies to manage their power grids.
The United States has been working on its Grid Modernization Initiative which aims to use artificial intelligence to improve the reliability and efficiency of the national power grid. China has planned to spend USD 1 trillion on smart grid technology while Germany is using AI to improve the integration of renewable energy sources into its power grid.
Studies in the applications of machine learning in promoting the efficient use of energy have been growing since 2014 according to a 2023 review by A. Entezari et al. AI has been positioned as a tool to simulate human decision-making and operate smart energy systems.
Researchers A. Winter, M. Igel, and P. Schenger described a method based on artificial neural networks to perform a load flow calculation and a grid state diagnosis for different switching states in distribution grids. The goal centered on the need to avoid power grid anomalies in the low-voltage-grid with high single-phase loads and generation.
Another paper by Zhi Liu, Ying Gao, and Baifen Liu proposed the use of AI in developing smart grid technologies. The proposal included different applications. These include the reduction of energy waste, optimal deployment of renewable energy into the power grid, and efficient operations of electrified modes of transportation like electric rail systems.
2. Specific Commercial and Industrial Applications
Numerous companies have also developed and implemented AI strategies as part of their respective business strategies. Consider Apple as an example. The company has a pronounced commitment to reduce its environmental impact through specific tactics and activities.
The aforementioned includes the use of AI to optimize the cooling of its data centers and the power consumption of its servers. A machine learning model is specifically used to automate climate control and power allocation. These have resulted in a 40 percent decrease in energy consumption for cooling and about an 80 percent reduction in server operations.
Researchers J. Liu et al. studied how specific AI applications help manufacturing companies in China improve their energy consumption. Results showed that using industrial robots can result in significant energy efficiency while also improving technological efficiency.
Other tech companies like Google and Amazon use AI as part of power management. These companies operate facilities equipped with systems to maximize cost savings from the efficient use of electricity through the prevention of wastage from unnecessary processes, automation of certain operations, and optimization of the operations of equipment and machines.
The predictive capabilities of specific artificial intelligence applications enable facilities like officers or office buildings and manufacturing plants to operate at their maximum capacities without unnecessary energy consumption while reducing operational costs.
3. Consumer-Level and Device-Level Technologies
It is also possible to roll out a service that can help energy consumers make informed decisions about their energy usage. For example, in residential areas, an AI-based service provided by an electric power distribution company sends out notifications to its customers regarding peak hours, price increases, and other relevant advisories.
A home energy management system is another consumer-level example. The system helps in reducing energy consumption by providing users with insights into their energy usage patterns and automating the operations of other smart devices.
There are also several AI-based technologies integrated into certain end-user or consumer products. A smart thermostat used in an air conditioning unit or an entire HVAC system is one of the best and most common examples of how AI is used to improve energy efficiency. It is an internet-connected device designed to automate temperature regulation.
Specific consumer electronics devices have also been equipped with hardware components that can help with the efficient utilization of power. There are software applications developed to monitor and control how devices use other components like processors and memory.
Modern devices have dedicated AI accelerators that work with specific power management chips that can automate power allocation and optimize battery performance. MacBooks and iPhones use machine learning in controlling processors. Several Android smartphones put certain unused apps in deep sleep to prevent them from consuming system resources.
Nevertheless, based on the aforesaid, at the consumer level and device level, artificial intelligence is used in different end-use products to automate the management of energy use and inform consumers about their energy usage patterns.
FURTHER READINGS AND REFERENCES
- Entezari, A., Aslani, A., Zahedi, R., and Noorollahi, Y. 2023. “Artificial Intelligence and Machine Learning in Energy Systems: A Bibliographic Perspective.” Energy Strategy Reviews. 45: 101017. DOI: 1016/j.esr.2022.101017
- Liu, J., Qian, Y., Yang, Y., and Yang, Z. 2022. “Can Artificial Intelligence Improve the Energy Efficiency of Manufacturing Companies? Evidence from China.” International Journal of Environmental Research and Public Health. 19(4): 2091. DOI: 3390/ijerph19042091
- Liu, Z., Gao, Y., and Liu, B. 2022. “An Artificial Intelligence-Based Electric Multiple Units Using a Smart Power Grid System. Energy Reports. 8: 13376-13388. DOI: 1016/j.egyr.2022.09.138
- Winter, A., Igel, M., and Schenger, P. 2020. Application of Artificial Intelligence in Power Grid State Analysis and Diagnosis. NEIS 2020 Conference on Sustainable Energy Supply and Energy Storage Systems. ISBN: 978-3-8007-5359-8