Machine Learning and Big Data Career Opportunities in the Evolving Energy Space
The importance of cutting past the excitement and incorporating actual use cases of big data, the internet of things, and machine learning in the energy sector is increasingly becoming apparent as more and more industries take advantage of the benefits of the same (Engerati.com, 2018). The world is increasingly becoming aware of the problem-solving potential of data and understanding how to manipulate it. Modern use cases include applications in marketing, engineering, economics, and even medicine (Nicholson, et al., 2019).
Scholars in the field of medicine are doing extraordinary things with data. Nicholson, et al. (2019) studied a compelling use case for machine learning applied to scapular kinematics (a survey of the complex system that is the human shoulder and its motion). Through machine learning algorithms and testing, the paper finds an accurate link between algorithmic predictions of the relationship between the nervous system and the shoulder with critical applications in the development of advanced prosthetics (Nicholson, et al., 2019). This use case is only one among many exciting applications open to the possibilities of machine learning and data science as a whole.
Closer home, several studies exist that demonstrate the potential of machine learning in the energy sector. Torregrossa, Leopold, Hernández-Sancho, & Hansen (2018) look into energy costs modelling in wastewater treatment plants through machine learning and in so doing cover two of the most crucial considerations in energy management worldwide. Energy consumption and its cost. The study looks into modernizing energy cost models which still employ the use of logarithmic, exponential, or linear functions which have the potential to provide less than accurate results as the relationships between variables become more complex. The paper proposes a new methodology based on machine learning algorithms capable of working with increasingly complex data-sets with accurate and verifiable results. The study uses machine learning algorithms to generate high performing energy cost models for wastewater plants by processing data from 317 wastewater treatment plants located in north-west Europe.
The most important takeaway from these case studies is that the possibilities are endless with machine learning and its usefulness to the field of energy engineering can no longer be ignored. As such, this development introduces a wide variety of opportunities within the energy space for young tech-savvy scholars and engineers looking to make their mark in the industry through viable career opportunities.
Eligibility for Careers in Energy
The versatile nature of looking at the energy space with a distinct consideration for data and machine learning means that these opportunities are not limited to engineering and IT graduates. The single most vital prerequisite for potential entrepreneurs and experts looking to advance their careers along these lines is passion. Instances of scholars pursuing non-technical courses at the tertiary level and still having access to the resources necessary to become knowledgeable in more technical skill-sets such as programming are prevalent in today’s society. This assertion shows that the tools one requires to attain the necessary skill level required for a venture into machine learning and big data are a lot more available to the youth in today’s society than was the case in the past.
However, the energy sector still requires knowledge of the basic concepts crucial to energy and a degree in engineering provides just about an upper hand for young entrepreneurs keen on making a career out of opportunities emerging from this field. But only just. A degree in electrical engineering only provides a slight upper hand as trained engineers may need less guidance in decision making through the idea or algorithm development process making the process a lot faster. All in all, as indicated previously, passion is still an essential ingredient for success in this area of specialization.
What One Can Do With Machine Learning in Energy?
So far, this article has covered what the opportunities are. Now to the important bit: The how. Several opportunities are available for machine learning in energy engineering. These opportunities are present in a wide variety of areas of consumption from business looking to reduce energy use and cost to utility companies looking for viable demand management strategies (Liano, 2019). Potential entrants in the space are only limited by their imagination.
For instance, machine learning and data science opportunities exist in making green energy viable for businesses. With a vast majority of industries, companies, and even countries shifting to renewable energy sources, it is becoming increasingly important to solve problems associated with the same. One prevalent issue with renewable sources such as wind and solar energy is the erratic nature of these sources. The sun doesn’t always shine and wind energy presents with a similar unpredictability issue (Powel, 2019). This challenge presents an opportunity for machine learning and data analytics. The versatility of machine learning means it can lend its expertise in predicting the availability of renewable energy sources and in so doing, help industries and businesses in proper planning for increased efficiency and lower energy costs. This area presents viable career opportunities for passionate individuals interested in providing sustainable solutions within this context.
Similar opportunities exist across the entirety of the energy sector with the widespread availability of technological tools required for machine learning. Powel (2019) concedes that the energy sector is lagging behind other industries as far as big data and machine learning are concerned. However, the advantage is that those looking to machine learning to solve energy-related issues have access to the latest technology seen as the science has developed immensely over the past few decades. Taking this trend into consideration draws to the conclusion that opportunities are available now and will benefit those to heed the call immediately. The next few years will be crucial to the energy sector, and individuals who are brave enough to get in on the evolution will significantly benefit from the future of Energy.
Engerati.com. (2018, July 16). Machine learning, IoT and big data for energy efficiency: a use case. Retrieved from Engerati.com: https://www.engerati.com/energy-management/article/energy-efficiency/machine-learning-iot-and-big-data-energy-efficiency-use
Liano, K. (2019, February 24). How Machine Learning, Big Data, & AI Are Changing Energy. Retrieved from Rapid Miner: https://rapidminer.com/blog/machine-learning-big-data-ai-energy/
Nicholson, K. F., Richardson, R. T., Roden, E. A., Quinton, R. G., Anzilotti, K. F., & Richards, J. G. (2019). Machine learning algorithms for predicting scapular kinematics. Medical Engineering and Physics, 39-45.
Powel. (2019, February 24). Implementing machine learning – What are the benefits for the energy sector? Retrieved from Powel: https://www.powel.com/news/implementing-machine-learning–what-are-the-benefits-for-the-energy-sector/
Torregrossa, D., Leopold, U., Hernández-Sancho, F., & Hansen, J. (2018). Machine learning for energy cost modelling in wastewater treatment plants. Journal of Environmental Management, 1061-1067.
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