A Comprehensive Review of Artificial Intelligence (AI) Companies in the Power Sector

Abstract

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.

Exploring the Synergy of Artificial Intelligence in Energy Storage Systems for Electric Vehicles

“…The potentiality of AI is limited by the training phase, which must be accounted for to increase its accuracy. The need for AI in this field relies mainly on performing optimization processes, data analytics, and asset management, organized into three levels as follows [47]:…”

“…The potentiality of AI is limited by the training phase, which must be accounted for to increase its accuracy. The need for AI in this field relies mainly on performing optimization processes, data analytics, and asset management, organized into three levels as follows [47]: With this classification, no link appears to underlie these fields and is related to the main topic of this manuscript. Analyzing the framework in detail makes it possible to discover its hidden rationale.…”

Leveraging Artificial Intelligence for Enhanced Supply Chain Optimization: A Comprehensive Review of Current Practices and Future Potentials

“…Demand forecasting is a critical aspect of supply chain planning, influencing inventory management, production scheduling, and distribution strategies. AI-powered demand forecasting models leverage advanced algorithms and machine learning techniques to analyze historical sales data, market trends, and external factors, such as economic indicators or weather patterns (Franki et al, 2023;Oriekhoe et al, 2024). By identifying patterns and correlations in large datasets, AI algorithms can generate more accurate demand forecasts, reducing forecasting errors and improving inventory planning (Zohdi et al, 2022).…”

Artificial Intelligence’s Evolution and Impact: From the Birth of Intelligent Agents to Navigating Sustainable and Renewable Energy in the Digital Era

“…AI and machine learning empower electricity companies and grid operators to forecast generation, schedule maintenance, and manage power flows [64,65]. Concurrently, consumers can access real-time information about supply to better manage consumption, distributed generation, and storage, consequently reducing energy bills [66].…”