Data Science and Clean Energy
The importance of data science in clean energy – also called renewable energy — is only growing as the Internet of Things continues to expand. With improvements in sensor and connectivity technology comes the ability to collect more data. Before data analytics, energy companies did not know what to do with all this data. Now, though, with the advent of data science, important insights can be derived from this vast quantity of data.
Thanks to data science, decision-making can be powered via the data collected in the clean energy sector. Data science in the clean energy industry can be used to optimize and improve processes, for example. As such, the fields of data science and data analytics stand to play a significant role in improvement of day-to-day operations in the renewable energy sector.
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.
Data science can be used in clean energy in many ways. For example, a solar plant can collect data to optimize power performance, reduce maintenance, predict upcoming maintenance times, or make solar collection more compact. All of these applications involve the extensive collection and analysis of data. The guiding principle of data management is a continuation of data management practices already common in the non-renewable energy sector.
In this article, we’ll discuss, in more detail, about the ways in which data science can be used to support the clean energy industry.
Clean Energy – Better for the Environment and the Economy
Data science can be a great asset to your clean energy enterprise. Making optimizations to your day-to-day operations at a solar or wind farm, for example, can be important to improve efficiency and cut down on costs, which makes renewable energy a more attractive choice as a cost-effective and environmentally-friendly alternative to fossil fuels. 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.
These developments have come at an auspicious time, as the threat of climate change is more prevalent than ever before, and the instability of the fossil fuel industry is shaking up the petroleum supply chain, calling into question our ability to secure nonrenewable energy sources especially during international turmoil in the economy. Nonrenewable energy, by contrast, is not affected by global tumult in the oil industry. By transitioning towards more renewable energy sources, countries can gradually decouple their economies from overuse of natural resources and reduce dependence on importing fossil fuels from other countries. This transition towards clean energy comes with a multitude of benefits, including but not limited to, reduced greenhouse gas emissions, less pollution, and better health conditions for people.
The U.S. is Moving Towards a Clean Energy Economy
Renewable energy or clean energy are terms that both refer to energy sources that come from natural resources that are renewable or processes that reuse previous resources. According to the Natural Resources Defense Council, clean energy in the US includes solar, wind, geothermal, hydroelectric, and biomass (also called biofuel). The energy sector is the third-largest industry in the United States’ economy, and renewables are quickly gaining steam in US energy production as technology for clean energy improves. The US sits on the world’s largest estimated reserve of coal, and vast reserves of natural gas thanks to advancements in shale resource extraction.
Although fossil fuels continue to produce 60% of energy for the US economy, the US economy has seen a massive increase in renewable energy installations for both wind farms and utility scale solar. Over 3110 MW of distributed solar plants were installed in 2015 representing a 34% increase from 2014’s solar installation numbers. Meanwhile, wind energy use increased by 12% from 2014 to 2015. This has resulted in the creation of nearly 74,000 megawatts of utility-scale wind power present in 41 states and territories, yielding to enough energy to power 17 million households.
The United States is currently on track to use renewables for 30% of its energy generating capacity by 2030, given its current policies, foreign investments, and government actions. According to the International Energy Agency, the US energy sector is valued at $350 billion as per 2018, with a total foreign investment of $172.8 billion. The United States’ energy sector focuses on cost efficiency and marketization of more sustainable version of common consumer goods.
To save energy and invest in more efficient energy production technology, the US government and American companies had to have collected and managed vast quantities of data in order to assess what areas in the energy grid can be improved the most with the least amount of resources invested. One example of America’s energy saving measures in action is the introduction of more sustainable LED light bulbs. Another example of the US saving energy is in its investment in transmission & distribution (t&s) networks at 65 billion dollars as of 2017.
Data Science is Transforming the Clean Energy Industry
Big data is changing the future of the renewable energy sector. Data science can be used for weather prediction which is useful for renewable energy sources such as wind and solar power. It can also be used to streamline management and day-to-day operations, and help new clean energy ventures gain investors. 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 science can be used to improve efficiency have been borrowed from other fields in which data science has advanced very rapidly, such as healthcare. Data science 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 science practices from the medical field such as survival analysis. Survival analysis can be applied to the energy industry in terms of field equipment “survival.” Instead of estimating the survival of patients, companies and consultants analyze the cost and time needed to maintain field equipment to prevent failure.
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. One company that has been leading the way in data driven improvements is BP. They have been especially proactive in incorporating the latest data-driven findings in its management practices since the 2010 Deepwater Horizon Disaster where neglect and mismanagement caused a high-profile oil spill and widespread criticism was laid onto the company. BP has been committed to using evidence-based management to prevent a similar disaster from happening again.
Many of the Data Analytics Used in Clean Energy Are Borrowed from Nonrenewable Energy
Many of the data-driven management improvements that have been introduced in the petroleum industry are also making gains in the renewable energy industry. Although clean energy is making in impact now that costs are lower than before and awareness of climate change has increased, there are many challenges clean energy methods must face to be economically viable let alone competitive with fossil fuels. This includes the onus on the producer to take on energy costs rather than pushing them onto consumers, the expense of building a decentralized power system, and the unfair advantages that fossil fuel companies have at their disposal. Although these challenges are persistent and considerable, the clean energy industry has embraced big data and machine learning methods to overcome these challenges and improve the profitability of clean energy. These improvements include more accurate weather prediction algorithms to reduce costs of maintaining clean energy facilities, machine-learning software to streamline the management of several wind or solar farms, and incentives to encourage investments in green energy.
One concern that many have stated about renewables is the lack of consistency due to differing weather patterns and the possibility of wasting energy when not needed. As per Ayesh Alshukri, the clean energy industry is addressing these concerns through the adoption of big data systems to better predict weather patterns based on historical data. Big data management systems can adjust power output based on a mix of weather patterns, daily energy consumption habits, and time of day to match fossil fuels in reliability. These big data systems are also helpful for managers of clean energy systems as the automated system can assess or predict when additional maintenance is needed. According to an article by AI Trends, artificial intelligence and machine learning software is being used to help optimize the energy use process for managers and consumers. Furthermore, big data software is not only a tool for renewable energy company managers can use to deal with power issues. Big data and machine learning software such as those developed by Neurio are designed to help homeowners to personally monitor their energy usage habits and ensure that their personal solar or wind power system providing energy when needed and powering down when the homeowner is not present.
Although the bulk of big data software development in renewable energy is dedicated to improving the efficiency of solar and wind power systems, there have also been efforts to develop big data analytics software to better measure the financial viability of renewable energy products. Although the falling cost of renewables has led to a steady clip in private investment, the rate of growth is still slow due to a lack of data. Companies like KwH analytics based in San Francisco, CA have developed a big data system that analyzes the financial viability of renewable energy projects. Using this data, they can ensure that solar projects have an insurance-backed production guarantee for asset owners and lenders when investing in renewables like solar. This would reduce the risk of mispricing and the risks associated with renewables by providing a more accurate, data driven picture of whether a project is viable or not.
Becoming a Data Scientist in Clean Energy
As data becomes a more integral part of optimizing clean energy facilities and usage, clean energy companies will need to hire more data scientists to help manage software and program new machine learning systems. Data scientists in the renewable energy industry are largely responsible for programming, maintaining, and analyzing large systems using complex algorithms based on artificial intelligence (AI) and machine learning. Data scientists also typically cooperate with other teams such as data engineers and other team members to acquire a better understanding on how to address customer complaints and needs more effectively.
Although data scientists in clean energy are in demand, employers may have exacting standards when they want to hire data scientists. It is often required to have a degree or several certificates in data science and mathematics to be considered for a data science position. Furthermore, data scientists, like any professional in a dynamic and exciting field, must pay attention to any changes and updates in the field to stay competitive.
Additionally, universities are beginning to adapt their course curriculum and offerings to keep up with the demand for data scientists in clean energy. One of the first to offer a data science degree in clean energy is the University of Washington’s Clean Energy Institute.
Conclusion
The energy industry has been using big data and data analytics for years to improve production and service offerings such as utilities. As clean energy becomes more profitable, more data scientists will be needed to optimize the performance of solar and wind farms. It is projected that clean energy will form 50% of all energy sources by 2050. Other technological developments, such as battery technology and long-distance energy transfers, will add more tasks for data scientists to keep tabs on when dealing with clean energy. This underscores the importance of big data in optimizing clean energy and turns it into the energy source of the future. Data science will be an in-demand field both in the near future, as well as the longer term, for clean energy.
