Introduction
Artificial Intelligence and Clean Energy Technologies are two of the most promising and rapidly developing fields of the 21st century. AI is being used extensively in various sectors, ranging from medicine to banking, to improve efficiency and lower costs. While Clean Energy Technologies have been developed to mitigate environmental challenges and reduce dependence on fossil fuels.
Artificial Intelligence and Clean Energy Technologies are two of the most promising and rapidly developing fields of the 21st century. AI is being used extensively in various sectors, ranging from medicine to banking, to improve efficiency and lower costs. While Clean Energy Technologies have been developed to mitigate environmental challenges and reduce dependence on fossil fuels.
The Clean Energy Innovation Ecosystem in Nigeria and Africa is ripe for this Artificial Intelligence (AI) revolution.
The use of AI can significantly enhance the efficiency and effectiveness of clean energy technologies, thereby accelerating the transition to a low-carbon economy.
Governments and other stakeholders in Nigeria and Africa can leverage a hybrid and multi-stakeholder approach to strengthen capabilities for harnessing new innovation. This approach involves collaboration among industry players, policymakers, and academia, which can facilitate knowledge sharing, technology transfer, and funding opportunities. The development of a robust ecosystem that supports clean energy innovation can help to drive economic growth and promote sustainable development.
The International Energy Agency (IEA) reports that Africa has the potential to become a major player in the global renewable energy market, with solar and wind energy being the most promising sources. The integration of AI can help to optimize the use of these resources, by improving the accuracy of weather forecasting and energy demand forecasting. AI can also be used to enhance the efficiency of energy storage systems and smart grid technologies.
Fig1: Parent disciplines of Artificial intelligence.
Integration of AI into Clean Energy Technologies
The Nexus between AI and clean energy is simple – AI can aid in the development of innovative technologies that combat climate change and ensure a sustainable future. By combining AI and Clean Energy Technologies, experts can leverage the potential of both fields and create a hybrid approach to address complex energy and sustainability issues.
One such approach is a hybrid intelligence approach, which aims to combine human intelligence with AI for efficient and optimal solutions to complex challenges in the field of clean energy. The hybrid intelligence approach can help improve the reliability, efficiency, and integration of renewable energy technologies.
The top applications of AI for clean technologies include customer engagement, microgrids, power theft and energy fraud detection, energy trading, energy storage, predictive analytics, production, grid management and efficiency, grid security, and smart grids.
For instance, smart grids have been developed to better manage renewable energy sources like wind and solar. These grids can monitor renewable energy sources and distribute energy more efficiently to homes and businesses, reducing the reliance on traditional power sources. AI-powered algorithms can optimize the distribution of electricity from multiple renewable sources, improving grid performance and minimizing energy waste.
Another example is the use of AI in the optimization of wind energy. Wind turbines do not generate maximum electricity at all wind speeds. Therefore, optimizing wind turbine performance requires an understanding of how the energy generated by the turbine relates to wind speed. AI algorithms can collect and analyze data from sensors placed on wind turbines and optimize generation to respond quickly to varying wind speeds.
Power Sector’s AI Adoption Rate:
As can be seen from the examples of early adopters, AI is destined to be one of the key enablers of the next wave of digital disruption. Facilitated by the rise of digital platforms and by tech giants such as Google, Facebook and Baidu, investments in AI are growing at a rapid pace. According to a recent report by McKinsey, in 2016, companies around the globe invested between USD 26 and 39 billion in AI. The majority of the funding came from tech giants who invested between USD 20 and 30 billion in the research, development and deployment of AI systems.
Outside the tech world, the adoption of AI is still in its early stages. Energy companies basically shun away from it despite the myriad of advantages AI might bring to the table. The lethargic approach to developing and adopting new solutions is one of the reasons why most utilities have been underperforming in financial markets. Further neglecting the digital reality will only increase the gap energy utilities face when compared to other businesses, such as software or the tech, media and telecom (TMT) industry. However, despite certain headwinds and the slower rate of adoption, AI-aided solutions in the power sector are a rapidly growing field with different interest areas. Figure 2 reveals major areas of AI application in the power sector.
Fig 2: (a) Main areas of application of AI companies in the power sector. (b) Main focus areas of AI companies in the power sector.
The most prolific of these applications are those focused in the field of electric mobility. There are numerous companies that use AI-aided solutions regarding electric vehicles. However, this article has considered those that focus on applications with a direct influence on the power grid. These generally include companies that develop platforms for the optimisation and/or integration of charging infrastructure and analyze how to use renewable energy for the charging process. Companies that focus on autonomous driving, upgrading EV efficiency and/or improving battery life have been disregarded. Despite not being among the top four applications, E-mobility is already on its way to widespread adoption. Looking at AI companies tied to the power sector, we believe that the future of the automotive industry is electric.
However, the major challenge to the integration of AI into the Nigerian clean energy sector is the lack of adequate local human capital. There are not enough skilled people adept in the use of AI systems and technologies. As a result, Nigeria’s educational institutions and clean energy training facilities need to include AI in their curricula.
Conclusion
The possibilities of combining AI and Clean Energy Technologies are immense, and the nexus between the two fields is a promising and fruitful one. Hybrid intelligence approaches like combining human expertise with AI algorithms can enable better energy management, optimization of renewable energy solutions, and help in the design of energy-efficient technologies. Thus, AI and Clean Energy Technologies can work together to create innovative solutions to global energy and environmental challenges, paving the way for a sustainable future. The Clean Energy Innovation Ecosystem in Nigeria (and on a larger scale, Africa) can benefit greatly from the infusion of AI in developing clean technologies.
A hybrid and multi-stakeholder approach can facilitate the collaboration necessary for knowledge sharing, technology transfer, and funding opportunities. The integration of AI can also help to optimize the use of renewable energy sources, thereby accelerating the transition to a low-carbon economy. With the right policies and investments, Nigeria and Africa can become leaders in clean energy innovation. Given the new energy paradigm driven by distributed energy sources, bidirectional flows and variable RES, it is essential to equip the distribution network with sensors that enable the application of data-driven AI techniques.
AI will certainly play a major part in the future of the power sector. However, the successful implementation of these new solutions will take a considerable amount of time and significant effort. To follow the trends set by other industries, the power sector will have to shift its current focus from providing commodities to achieving an open innovation, customer-centric business model. Research presented in the paper shows that major steps have been taken, but further progress in this direction is yet to be made.
Eucharia Ileka is Head Innovation and Growth at Clean Technology Hub.