BIG DATA IN RENEWABLE ENERGY
The first and most important use of big data in the renewable eneryg sector is that companies can produce more accurate forecasts on energy generation based on past performance. They can determine the exact renewable technologies, resources or other equipment needed to produce the required energy. Big data and data science have been used by the energy sector for years, however, as we enter a new digital era, its impact on renewable energy is set to grow exponentially. As clean energy becomes more of a viable choice to reduce green house gas emissions and ultimately fight in restoring and keeping our environment habitable big data helps in more than one way to estimate and predict energy efficiency, storage and generation technologies. It is projected that clean energy will form 50% of all energy sources by 2050 and big data in the clean energy industry can be used to optimise and improve operations. Additionally, big data can play a role in reducing the cost of renewable energy by enabling more accurate predictions of energy demand. This can help energy producers to better match energy supply with demand, reducing the need for expensive energy storage solutions and improving the economic viability of renewable energy systems.
As the threat of climate change increases and fossil fuel resources become more unstable, many organisations will be looking for alternative energy sources. Improving energy efficiency — If data can continue to improve the efficiency of clean energy, then the renewable sector can expect a significant spike in popularity. To increase efficiency at a solar or wind farm, for instance, it may be necessary to make adjustments to daily operations which can be identified using data. Additionally, with the cost of renewables in steep decline due to technological advancements, the ability to get more mileage out of your solar plant or wind farm is now more desirable than ever. Predicting weather patterns — Big data can be used to forecast the supply of energy based on typical weather behaviour and to monitor the condition of solar panels. For instance, solar energy is one of the most popular forms of renewable energy and it is still expanding. However, it is reliant on sunshine, a transient natural resource. With a combination of big data, predictive analytics, and machine learning, weather and satellite data can now be combined to give accurate predictions of weather patterns.
Big Data & 5G in Renewable Energy
The architecture of a traditional energy distribution system is designed to support unidirectional energy flow from suppliers to users. With several large production points as the only energy source, the grid used to be a one-way electrical interconnection infrastructure to transmit and distribute energy. With the growing number of smaller power renewable generation plants, the industry faces the necessity to integrate a multitude of additional production points into the infrastructure. Data gathered in the smart grid network requires a thorough analysis to provide utility companies with the necessary information to drive their businesses forward. Advanced metering infrastructure collects data related to the consumption level, power quality, self-diagnostics, smart meter and device operational statuses.
While some of the data can be processed in real-time, thanks to edge computing and 5G adoption, big data is imperative for the in-depth understanding of consumer behavior, performance capabilities, and improving performance. With data analytics, energy providers can improve smart grid optimization and increase customer engagement.Data gathered in the smart grid network requires a thorough analysis to provide utility companies with the necessary information to drive their businesses forward. Advanced metering infrastructure collects data related to the consumption level, power quality, self-diagnostics, smart meter and device operational statuses.
Advances in clean energy data science result from parallel advancements in the fossil fuels and utilities industries to improve operations. For example, thanks to data science, energy companies can leverage the extensive amounts of data currently being collected to generate novel decision-making insights. Utilities companies can use data analytics to figure out peak times to set energy pricing, and fossil fuel companies can use data science to help drive refinery and distribution processes. Likewise, data plays an important role in the efficient management and regulation of clean energy.
Big Data is Transforming the Clean Energy Industry
Technologically advanced analytics can help renewable energy companies gain useful insights to help them better manage wind and solar, and be better able to predict the amount of energy that can be used in the power grid or stored for later use. Many of the ways data analytics can be used to improve efficiency have been borrowed from other fields in which data science has advanced very rapidly, such as healthcare. Big data practices in clean energy are especially important in today’s economy where low oil prices have increased the demand for cost cutting measures in order to make smarter investments, reduce risk, and improve public safety. Companies and analysts in the energy industry have borrowed data practices from the medical field such as survival analysis. Survival analysis for clean energy equipment can lead to improvements in equipment management practices where the company can proactively repair its equipment without taking an oil well offline, reducing maintenance costs while maintaining steady production. While alternative service models and new technologies may improve reliability and afford- ability, data sharing can further improve energy access. For example, advanced metering infrastructure (AMI) provides a technical means to share information between consumers and utilities. As developing countries continue with grid modernization and utility reform initiatives, upgrading their systems with digital technologies as well as introducing smart meters and other sensors, the volume of data and the opportunities they present will increase. For example, IBM Research-Africa is developing software applications to model rural electrification strategies and to predict potential economic and social benefits. Such data-driven tools, built on a mix of open and big data, could assist local governments and donors when designing energy access expansion programs
Big Data Improves Monitoring & Efficiency
Energy producers can predict the market using key indicators in real-time and respond quickly. Logical algorithms can incorporate and evaluate large volume of information for the power companies. This allows them to prepare more effective business strategies. Big data improves equipment monitoring and maintenance, to minimize production hours. Energy companies are investing more on data to monitor their entire operation because don’t want any future disaster or system failure which may cost them a lot.
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In conclusion, big data has the potential to revolutionize the renewable energy sector by enabling more efficient and cost-effective energy production, optimization of renewable energy systems, and improved predictions of energy demand. By nature, the energy sector generates vast amounts of big data through meters, sensor networks, customer payments, credit history, satellite imagery, etc. It is not surprising that private and public energy companies are turning to the idea of leveraging big data analytics for performance optimization and improved service delivery. It has been an uphill battle for renewable energies to penetrate the energy industry currently dominated by oil and gas. But as technologies advance and big data plays a big role in predictive analytics and increasing efficiency, our reliance on these harmful fossil fuels can decrease as we turn to more sustainable and clean forms of energy.
References
https://www.quanta-cs.com/blogs/2022-8/how-big-data-is-changing-renewable-energy
https://www.smartdatacollective.com/big-data-changing-future-renewable-energy-sector/
