Researchers Develop a Framework that Emphasizes Developing Realistic AI Strategies, Data Requirements, and a Human AI Symbiosis for Photovoltaics Energy SMEs

Due to the severe environmental damage caused by traditional fossil fuel exploitation, the global shift to renewable energy sources has assumed critical importance in the twenty-first century. Solar energy is a popular option for renewable resources since it is abundant, and its harvesting is getting easier thanks to photovoltaics (PV), which use solar cells to produce power from sunlight. PV installations are anticipated to rise sharply in the upcoming years since they are the primary technological choice for a sustainable, decarbonized energy industry.

Solar energy capture necessitates a decentralized strategy, unlike conventional, centralized power production based on fossil fuels. Due to their local expertise and adaptability, small and medium-sized firms (SMEs1) can drive and accelerate the adoption of PV in this situation. However, environmental variables like seasonal and weather-dependent fluctuations significantly increase the difficulty of managing solar energy. Artificial intelligence (AI) systems capable of producing accurate supply and demand projections and various other helpful activities based on the available historical and real-time data can efficiently solve these challenges.

Research studies on AI in the solar energy sector have mostly focused on technological issues while ignoring other important variables, including ethical considerations, human factors, and organizational adoption by SMEs. Due to their lack of financial and strategic capabilities, this adoption might be difficult for many SMEs. In response to the difficulties above, models of AI maturity have been created to evaluate and categorize an organization’s overall capabilities in various AI maturity stages.

A Research group from the University of Naples, Deepkapha, and Malmo University conclude that the interaction between technological, organizational, and human factors constitutes a plausible justification for integrating disruptive breakthroughs, like AI, into company operations. This research adopts an interdisciplinary approach to address AI adoption in SMEs from several angles. The paper’s case study focuses on the Netherlands as one of the representative European electricity producers, relying primarily on conventional fossil fuels to power a highly dense country and its neighboring states while displaying a promising and rapidly developing solar industry. Despite the global scope of AI-assisted renewable electricity transition in SMEs, the Netherlands is one of the nations with the densest populations in the entire globe.

A crucial scientific and practical problem statement is the need for comprehensive assistance for SMEs in moving between AI maturity phases. They aim to close this gap by conducting interviews with top PV plant operators to develop an industry-relevant roadmap for adopting a data-driven approach to managing a solar PV plant operating SME.

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