Big Data and Energy: A Combination for Success

Big data and energy are not opposing terms. The current situation is driving companies, energy or otherwise, to look for ways to improve service. And big data technology is revolutionizing the sector and offering better options to both sides. In other words, big data helps to save energy – here’s why it’s in your company’s interest!

Big Data and Energy Consumption: Energy Efficiency

According to a study by Indian market researcher Mordor Intelligence, the big data analytics market in the energy sector is forecast to have a compound annual growth rate of 13.15% over the period 2022-2027. Mordor Intelligence notes that after the COVID-19 pandemic the market is growing due to increased investment in alternative energy sources and increased awareness about the benefits of metering.

How Big Data Helps Save Energy

Thus, big data optimizes energy consumption in companies. Companies collect data thanks to smart meters that gather consumption from energy sources. The analysis of this data enables energy optimization and how, for example, to save electricity during the hours when there are more employees or customers in the offices. Analyzing them can also be used to review automated energy supply systems and detect possible failures or ways to improve the facilities. This improves the power supply and also the safety of the system.

At the same time, electric utilities can use big data to anticipate peaks in energy use and reinforce themselves in order to avoid service outages. Also, to prepare for a present and future of fossil fuel reduction and the growth of sources such as solar or wind. As Mordor Intelligence explains, it is precisely the depletion of these fossil fuel sources and new trends to improve efficiency and reliability in energy transmission that are other factors for the growing interest in big data analysis in the sector.

Big data also helps to plan a strategy. When thinking about new business opportunities, expansions, the creation of new factories, etc., big data analysis is used to design energy efficiency plans so that new projects have a more rigorous budget.

When applying big data to achieve energy efficiency, it is necessary to ask where the company consumes energy and why. The individual consumption of each department, machine, process… can be analyzed and compared with others to see which needs more energy or if less could be used (and, in that case, why it is not already doing so). At the same time, the consumption habits of employees can also be analyzed. All this information translates into energy reduction plans and awareness campaigns.

Big Data and Renewable Energy

When dealing specifically with the relationship between big data and renewable energy, some very important data are weather data: hours of sunshine, wind speed, waves… All this influences the generation of energy and therefore to predict whether there will be more or less production and make decisions accordingly.

Having the weather forecast serves to predict the amount of renewable energy that can be produced. With this, it is known whether it is possible to meet market demand.

When using big data for renewable energy generation, other factors that cause production to vary must also be taken into account. For example, dust in suspension, which affects the performance of solar panels or wind turbines. All this accumulation of data (meteorological, energy production, etc.) will make renewable energy generation systems more and more precise.

Moreover, beyond meteorology, data collection in the renewable energy sector is used to monitor solar panels, wind turbines and other equipment to check how they are working and whether they need to be repaired.

Plain Concepts, Big Data and Energy

At Plain Concepts we have developed projects that have linked big data with very diverse energy sources.

For example, for Repsol we created a tool that generates 3D models in order to reproduce a potential oil field and check if it is worth to start prospecting. To reach the conclusion, the software uses geological or reservoir information: rock type, porosity, permeability… The interface, generated with Evergine, was more user-friendly and intuitive.

For a leading company in photovoltaic plants, we created a SCADA system to control the facilities, with which it was possible to review up to 42,000 variables, viewable through graphs. For another renewable energy company we performed an automatic migration of virtual servers to Azure. In database migrations we also worked with a large oil company, which needed to migrate 2000 servers on two continents to Azure. This solution reduced costs while improving flexibility, efficiency and performance.

If you are an energy company and want to know how to leverage the potential of data, or a company in another sector and want to improve your energy use through big data, write to us: we’ll work with you to make the most of valuable information.