Applying probability modeling helps improve performance, predict occasional failures in the functioning, and decrease maintenance costs.
Fremont, CA: The energy sector maintains a good reputation for its inventions and innovations. Energy is an uncompromisable component in industries like manufacturing, agriculture, transportation, and many others. Energy consumption is increasing with time due to the growing use to technology.
The rapid development of utilities and the energy sector plays a pivotal role in social development. People are now facing challenges in managing energy and its consumption. They are now turning to renewable energy sources for environmental protection. Utilizing smart technologies can help in overcoming this challenge.
Let us look at some data science use cases in energy and utilities:
Failure Probability Modeling
Failure probability modeling has made its way to the energy industry. The contribution of machine learning algorithms in the failure prediction is unparalleled.
Applying probability modeling helps improve performance, predict occasional failures in the functioning, and decrease maintenance costs. The energy companies invest generously into maintenance to ensure the proper functioning of their devices and machines. Unexpected failures in their operations can lead to huge financial losses. Furthermore, for individuals who depend on these companies as their energy source, the situation becomes quite critical. As a consequence, any sort of unexpected failure can hamper the general reliability and reputation of energy providers.
The output of the failure prediction model application is an important part of the decision making process for the companies. It brings a great opportunity to stay ahead for company management.
Dynamic Energy Management Systems
Dynamic energy management systems are the part of the innovative approach to managing the load. This type of management encompasses all the traditional energy management principles regarding distributed energy sources, demand, and demand-side management along with modern energy challenges such as temporary load, energy-saving, and demand reduction. Hence, smart energy management systems now distributed energy resources, combine smart end-use devices, as well as advanced control and communication.
Big data analytics plays a significant part here since it empowers dynamic management systems in Smart Grids. This helps in optimizing the energy flows between the providers and consumers. Resultantly, the efficiency of the energy management system relies on renewable energy sources and load forecasting.