Data science and AI: The twin engines powering the renewable energy revolution
This article is authored by Drumil Tejas Joshi, Analyst 1 - Monitoring and Diagnostics, Southern Power Company, USA.
Climate change faces its critical juncture today as the urgent need to exchange fossil fuels with renewable energy sources becomes apparent worldwide. The International Energy Agency (IEA) predicts that global energy demand will rise by 27% by 2040 yet effective decarbonisation techniques should be employed to prevent severe climate impacts. The conversion to renewable energy depends heavily on solar energy together with wind power and hydropower systems. One major issue remains whether we can effectively combine variable energy source integration with stable power grid operations.
The integration of data science along with artificial intelligence (AI) technologies becomes vital at this point. The renewable energy revolution relies on these technologies as its dual power source that keeps driving its advancement. Through advanced forecasting capabilities and optimised generation output and improved grid control and energy storage management, data science with AI technology drives renewable energy transformation at new levels.
Renewable energy systems produce massive amounts of data that data science operates as their fundamental basis for understanding and usage. The renewable energy sector worldwide has experienced a massive increase in data regarding energy generation and weather predictions alongside live power grid operation information. According to a McKinsey report, renewable energy systems produce extensive data that helps optimise operations, improve system integration, and forecast future energy requirements.
Energy stakeholders benefit from data science through systematic extraction of knowledge from big data to make more informed operational choices. Historical data and real-time inputs enable predictions of energy demand patterns as well as generation patterns with high accuracy levels. Energy forecasts become possible through weather data which reveals wind speed and solar radiation patterns that help utilities estimate the electricity production capabilities of wind farms and solar power systems. Grid operators use the gained knowledge to supervise energy distribution better and forecast system needs for backup power and storage systems.
The analysis of data identifies various points in energy systems where resources are being wasted inefficiently. Data-driven machine learning algorithms enhance renewable infrastructure performance by monitoring system data to identify defects and sustain proper maintenance despite minor complications.
Renewable energy systems gain their complete potential through the implementation of Artificial Intelligence beyond the fundamental understanding gained from data science. AI technology represents a distinctive capability to learn from data about making better predictions with each passing day due to machine learning and deep learning algorithms.
AI demonstrates its most important function in renewable energy management through control of power grids. The modern power grid system keeps getting smarter through its incorporation of clean energy generation facilities along with storage systems and electrical transportation components. This network benefits from AI optimisation because AI systems can accurately forecast electricity supply and demand trends across the decentralised energy flow system. The prediction of solar generation peaks by machine learning models combined with weather condition analysis allows power distribution adjustments that decrease the operational need for peak powered plants reliant on fossil fuels. The operators use AI systems to determine when they should charge or discharge their energy storage devices such as batteries for optimal performance.
The path of data science and AI have bright potential and yet they have to be faced with some challenges. The one major issue is data privacy and security. Data protection raises strong concerns regarding the vast amounts of data generated by energy systems and consumers. In addition, much investment in infrastructure and regulatory frameworks is needed to integrate AI into grid operations in a way that fair and transparent algorithms can operate.
Nevertheless, AI and data science have great opportunities. The World Economic Forum (2020) report estimates that AI could reduce global greenhouse gas emissions by as much as 10% by 2030, further accelerating the journey towards meeting climate goals. Moreover, the exploitation of AI and data science in RE industries would yield opportunities for new economic growth with jobs in data analysis, machine learning engineering, and smart grid management.
Data science and AI have a big part to play in the renewable energy revolution, and this role will only grow, as these fields grow. As machine learning, data analytics, and edge computing continue to advance, increasingly smart energy systems will be able to integrate additional types of energy sources, increase storage capacity, and optimise energy consumption on a much more granular level.
But to really drive these technologies to their potential, more work needs to be done in the form of infrastructure investments, education, and policy. Creating such AI and data science solutions is a collective effort of governments, private companies, and research institutions that businesses engaged in AI and data science will owe to implementing them. To ensure that the renewable energy revolution meets our energy needs in a sustainable, efficient and affordable way, collaboration is key.
AI and data science are not exclusive to the renewable energy sector. They are essential to its future success. The underlying technologies offer increased energy efficiency, better decision-making, and grid integration of intermittent sources. Data science and artificial intelligence will power the renewable energy revolution. Combining these technologies creates a more modern, clean, and efficient energy system capable of meeting the world's growing energy demands while simultaneously being better prepared to deal with climate change.
This article is authored by Drumil Tejas Joshi, Analyst 1 - Monitoring and Diagnostics, Southern Power Company, USA.