AI-driven approaches for optimizing power consumption: a comprehensive survey

被引：9

作者：

Biswas, Parag ^{[1
]}

Rashid, Abdur ^{[1
]}

Biswas, Angona ^{[2
]}

Nasim, Md Abdullah Al ^{[2
]}

Chakraborty, Sovon ^{[3
]}

Gupta, Kishor Datta ^{[4
]}

George, Roy ^{[4
]}

机构：

[1] MSEM Department, Westcliff University

[2] Research and Development Department, Pioneer Alpha, Dhaka

[3] Department of Computer Science, University of Liberal Arts Bangladesh, Dhaka

[4] Department of Computer and Information Science, Clark Atlanta University

来源：

Discover Artificial Intelligence | 2024年 / 4卷 / 01期

基金：

美国国家科学基金会;

关键词：

Artificial intelligence (AI); Intelligent systems; Machine learning; Optimization; Power consumption;

D O I：

10.1007/s44163-024-00211-7

中图分类号：

学科分类号：

摘要：

Reduced environmental impacts, lower operating costs, and a stable, sustainable energy supply for current and future generations are the main reasons why power optimization is important. Power optimization ensures that energy is used more efficiently, reducing waste and optimizing the utilization of resources. In today’s world, the integration of power optimization and artificial intelligence (AI) is essential for transforming how energy is produced, used, and distributed. AI-driven algorithms and predictive analytics enable real-time monitoring and analysis of power usage trends, allowing for dynamic adjustments to effectively meet demand. Efficiency and sustainability are enhanced across various sectors by optimizing power consumption through intelligent systems. This survey paper provides an extensive review of the different AI techniques used for power optimization, along with a systematic analysis of the literature on the application of intelligent systems across diverse areas of power consumption. The literature review evaluates the performance and outcomes of 17 distinct research methodologies, highlighting their strengths and limitations. Additionally, this article outlines future directions for the integration of AI in power consumption optimization. © The Author(s) 2024.

引用

共 55 条

[1]

Ngarambe J., Yun G.Y., Santamouris M., The use of artificial intelligence (AI) methods in the prediction of thermal comfort in buildings: energy implications of AI-based thermal comfort controls, Energy Build, 211, (2020)

[2]

Sorrell S., Reducing energy demand: a review of issues, challenges and approaches, Renew Sustain Energy Rev, 47, pp. 74-82, (2015)

[3]

Economidou M., Todeschi V., Bertoldi P., D'Agostino D., Zangheri P., Castellazzi L., Review of 50 years of EU energy efficiency policies for buildings, Energy Build, 225, (2020)

[4]

Wang X., Wang H., Bhandari B., Cheng L., AI-empowered methods for smart energy consumption: a review of load forecasting, anomaly detection and demand response, Int J Precis Eng Manuf Green Technol, 11, pp. 1-31, (2023)

[5]

Mischos S., Dalagdi E., Vrakas D., Intelligent energy management systems: a review, Artif Intell Rev, 56, pp. 1-40, (2023)

[6]

Muhammad Ashraf W., Ghulam Moeen Uddin S.M.A.S.A.S.A.M.H.K., Jamil H., Optimization of a 660 MWe supercritical power plant performance—a case of industry 40 in the data-driven operational management part 1 thermal efficiency, Energies, 13, 21, (2020)

[7]

Heymann F., Quest H., Garcia T.L., Ballif C., Galus M., Reviewing 40 years of artificial intelligence applied to power systems—a taxonomic perspective, Energy AI, 15, (2024)

[8]

Boubaker S., Kamel S., Ghazouani N., Mellit A., Assessment of machine and deep learning approaches for fault diagnosis in photovoltaic systems using infrared thermography, Remote Sens, 15, 6, (2023)

[9]

Guo X., Na Z., Ma D., Lu Y., Luo X., Fault diagnosis of photovoltaic system based on machine learning model fusion, In: IOP Conference Series: Earth and Environmental Science, 467, (2020)

[10]

Agbulut U., Gurel A.E., Ergun A., Ceylan I., Performance assessment of a V-trough photovoltaic system and prediction of power output with different machine learning algorithms, J Clean Prod, 268, (2020)

← 1 2 3 4 5 6 →