A Comprehensive Review of Artificial Intelligence (AI) Companies in the Power Sector
被引:20
|
作者:
Franki, Vladimir
论文数: 0引用数: 0
h-index: 0
机构:
Univ Rijeka, Fac Engn Rijeka, Vukovarska 58, Rijeka 51000, Croatia
Energy Platform Living Lab, Unska 3, Zagreb 10000, CroatiaUniv Rijeka, Fac Engn Rijeka, Vukovarska 58, Rijeka 51000, Croatia
Franki, Vladimir
[1
,2
]
Majnaric, Darin
论文数: 0引用数: 0
h-index: 0
机构:
Univ Zagreb, Fac Mech Engn & Naval Architecture, Ul Ivana Lucica 5, Zagreb 10000, CroatiaUniv Rijeka, Fac Engn Rijeka, Vukovarska 58, Rijeka 51000, Croatia
Majnaric, Darin
[3
]
Viskovic, Alfredo
论文数: 0引用数: 0
h-index: 0
机构:
Univ Rijeka, Fac Engn Rijeka, Vukovarska 58, Rijeka 51000, Croatia
Energy Platform Living Lab, Unska 3, Zagreb 10000, CroatiaUniv Rijeka, Fac Engn Rijeka, Vukovarska 58, Rijeka 51000, Croatia
Viskovic, Alfredo
[1
,2
]
机构:
[1] Univ Rijeka, Fac Engn Rijeka, Vukovarska 58, Rijeka 51000, Croatia
[2] Energy Platform Living Lab, Unska 3, Zagreb 10000, Croatia
[3] Univ Zagreb, Fac Mech Engn & Naval Architecture, Ul Ivana Lucica 5, Zagreb 10000, Croatia
artificial intelligence;
power sector;
adoption rate;
application;
AI companies;
AI start-ups;
PREDICTIVE MAINTENANCE;
DIGITAL TRANSFORMATION;
DEMAND RESPONSE;
SYSTEMS;
MODELS;
MANAGEMENT;
DECARBONIZATION;
OPTIMIZATION;
TECHNOLOGIES;
PERFORMANCE;
D O I:
10.3390/en16031077
中图分类号:
TE [石油、天然气工业];
TK [能源与动力工程];
学科分类号:
0807 ;
0820 ;
摘要:
There is an ongoing, revolutionary transformation occurring across the globe. This transformation is altering established processes, disrupting traditional business models and changing how people live their lives. The power sector is no exception and is going through a radical transformation of its own. Renewable energy, distributed energy sources, electric vehicles, advanced metering and communication infrastructure, management algorithms, energy efficiency programs and new digital solutions drive change in the power sector. These changes are fundamentally altering energy supply chains, shifting geopolitical powers and revising energy landscapes. Underlying infrastructural components are expected to generate enormous amounts of data to support these applications. Facilitating a flow of information coming from the system ' s components is a prerequisite for applying Artificial Intelligence (AI) solutions in the power sector. New components, data flows and AI techniques will play a key role in demand forecasting, system optimisation, fault detection, predictive maintenance and a whole string of other areas. In this context, digitalisation is becoming one of the most important factors in the power sector ' s transformation process. Digital solutions possess significant potential in resolving multiple issues across the power supply chain. Considering the growing importance of AI, this paper explores the current status of the technology's adoption rate in the power sector. The review is conducted by analysing academic literature but also by analysing several hundred companies around the world that are developing and implementing AI solutions on the grid's edge.
机构:
SS Inst Med Sci & Res Ctr, Dept Dermatol Venerol & Leprosy, Bengaluru, Karnataka, IndiaSS Inst Med Sci & Res Ctr, Dept Dermatol Venerol & Leprosy, Bengaluru, Karnataka, India
Kololgi, Shreyas P.
Lahari, C. S.
论文数: 0引用数: 0
h-index: 0
机构:
SS Inst Med Sci & Res Ctr, Dept Dermatol Venerol & Leprosy, Bengaluru, Karnataka, IndiaSS Inst Med Sci & Res Ctr, Dept Dermatol Venerol & Leprosy, Bengaluru, Karnataka, India
机构:
Manipal Institute of Technology,Department of Chemical EngineeringManipal Institute of Technology,Department of Chemical Engineering
Devansh Gupta
Manan Shah
论文数: 0引用数: 0
h-index: 0
机构:
Pandit Deendayal Energy University,Department of Chemical Engineering, School of TechnologyManipal Institute of Technology,Department of Chemical Engineering