The integration of renewable energy sources and AI-driven solutions is revolutionizing the energy landscape, offering efficiency, sustainability, and innovation. This chapter explores the interplay between renewable energy sources and AI-driven solutions, highlighting its role in forecasting, real-time monitoring, predictive maintenance, and demand response. Comparative studies between traditional and novel energy technologies highlight the potential for cleaner, more efficient alternatives. Challenges of integration complexity, data privacy, and scalability are discussed, while a vision of future directions includes holistic energy systems, autonomous energy networks, and collaborative innovation. By harnessing AI's capabilities and embracing innovative power generation methods, the energy sector can create a resilient, sustainable, and intelligent energy future.
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The global energy landscape is undergoing a transformative shift driven by the increasing demand for sustainable and efficient power generation solutions. As traditional fossil fuel-based methods continue to contribute to environmental degradation and climate change, there is an urgent need to explore and adopt novel power generation technologies that harness renewable sources and minimize ecological impact. The rise of AI has significantly transformed energy systems, transforming the generation, management, and optimization of power (Abdalla et al., 2021a). The global pursuit of a sustainable and efficient energy future has spurred unprecedented innovation at the crossroads of renewable energy sources and artificial intelligence (AI) solutions. This intersection has catalysed a paradigm shift, where efficiency, sustainability, and innovation converge to reshape the energy landscape. It introduces the transformative journey of integrating renewable energy sources and AI-driven solutions, unraveling the profound impacts on power generation, consumption, and distribution(Ahmad et al., 2021).
The urgency to transition from fossil fuels to cleaner energy sources has never been more compelling. Solar, wind, hydroelectric, geothermal, and bioenergy represent a promising array of renewable options, each with its unique benefits and challenges. The transition not only mitigates environmental impact but also ensures energy security, reduces carbon emissions, and promotes sustainable economic growth(Hazra & Kumar Roy, 2023; Hazra & Roy, 2023; Roy & Hazra, 2015).
The evolution of AI from theoretical concept to practical application has unlocked an array of possibilities in the energy domain. AI's ability to analyse vast datasets, predict outcomes, and optimize operations in real-time has revolutionized energy generation, distribution, and consumption. Its integration into power systems empowers forecasting, monitoring, maintenance, and response mechanisms that optimize efficiency and resilience(Abdalla et al., 2021b). AI-enabled forecasting is a cornerstone of modern energy systems. Accurate predictions of renewable energy production based on weather patterns, historical data, and grid dynamics enable proactive grid management and demand-response strategies. Solar irradiance predictions and wind speed forecasts enable utilities to match energy supply with dynamic demand, promoting efficient resource allocation. Real-time monitoring driven by AI ensures the optimal functioning of energy infrastructure. Sensors and IoT devices provide constant streams of data, allowing AI algorithms to detect anomalies, predict equipment failures, and prescribe maintenance actions. This proactive approach minimizes downtime, maximizes equipment lifespan, and enhances overall system reliability(Arunachalam & Kumar, 2018).
AI-driven demand response mechanisms enable grids to adapt to fluctuations in energy demand and supply. By intelligently managing energy consumption during peak periods, demand response reduces strain on the grid, minimizes energy wastage, and ensures stable operations. Comparative studies contrasting traditional energy technologies with emerging alternatives offer insights into the efficiency, cost-effectiveness, and environmental impact of various approaches. These studies serve as catalysts for innovation, guiding decision-makers towards sustainable choices and future-oriented strategies. The convergence of renewable energy integration and AI solutions brings forth a spectrum of challenges, including integration complexity, data security, and scalability. However, these challenges also herald opportunities for holistic energy systems, autonomous networks, and collaborative innovation. This journey promises a future where AI, combined with innovative power generation methods, paves the way for an energy landscape that is resilient, sustainable, and intelligent. It explores the integration of renewable energy and AI solutions, focusing on topics like AI-driven wind farm control, hydroelectric plant predictive maintenance, and neural network-based solar energy forecasting. It aims to lead us towards an energy future that combines renewable sources and AI-driven intelligence (Awan et al., 2022).