As big data continues to disrupt almost every imaginable industry, the energy sector has finally started to catch up. With the recent advancements in IoT, AI and cloud computing, opportunities for more efficient energy consumption and distribution have been opened up. In this article, we explore the applications and limitations of big data in the energy context and discuss how companies can make this digital transformation a reality.
Equipment manufacturers and process plant operators always struggle to keep their machinery working as efficiently as possible while also keeping an eye on potential failures. In the context of the energy industry, equipment failure often leads to disastrous outcomes, including power outages that impact thousands of people and incur huge economic losses.
With the powerful symbiosis of IoT and machine learning, most industrial equipment units including assembly robots and maintenance vehicles can periodically send information about their condition to a centralized unit, making equipment failure much more predictable. ML-enabled software can analyze hundreds of data points including the age of the machine, its model type, repair history logs, thermodynamic and acoustic information, and turn it into a comprehensive report.
Given that there are thousands of such machines operating at the same time, advanced methods of data collection and analysis is required. This is why energy companies are turning to Hadoop consultants to streamline their data management.
Smart Supply and Demand Management
Currently, big data analytics is the most important prerequisite for accurate load forecasting. Again, with IoT and AI, companies can predict energy consumption levels based on historical data of energy usage, geographical location, weather, and energy prices. This is a win-win for both the environment and energy companies as maintenance costs are significantly reduced and carbon emissions are lowered.
On the other side of the energy supply chain, consumers can also better monitor their consumption and adapt to fluctuations accordingly. For example, with the help of Nest, a thermostat developed by Google, homeowners can monitor energy consumption and adjust their controls to achieve more cost-efficient energy use.
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In addition, businesses that have energy management high on their priority list can also benefit from advanced energy monitoring enabled by big data. For example, horticultural companies can predict when more airflow is needed based on smart sensors installed in greenhouses. When the temperature is about to rise, cooling vents can be automatically opened. This would require an almost negligible amount of energy. Conventionally, companies would rely on thermostats, which trigger very energy-demanding air conditioners to cool the room. On a bigger scale, such initiatives would also significantly lower carbon emissions.
As with many data-based initiatives, the implications are extremely promising, but only a few companies can achieve long-term success. While digital transformation has become the name of the game for many organizations that are trying to achieve operational efficiency at scale, this notion becomes much more relevant in the energy sector.
Energy companies have to face a very unique set of challenges, though.
First, energy companies are very dependent on environmental conditions. For better or worse, humans can’t control wind dynamics, sunlight, or fossil power thermodynamics, for example. This significantly hinders companies’ ability to prove that these AI-based initiatives are reliable and account for all the challenges posed by nature itself.
Second, energy companies must adhere to governmental regulations. While, for example, you can argue that every data-reliant business has to be GDPR-compliant, organizations that operate in the energy sector are at risk of taking peoples’ lives accidentally, not just disclosing someone’s personal data. That’s why any change in operations will always be accompanied by multiple rounds of regulatory processes that take a considerable amount of time.
These are the steps that energy companies might take to prepare for digital transformation:
First things first, data needs to be clean and organized. In the energy context, historical data is critically important. With the latest advancement in data analytics, collecting such data has become much more feasible than ever before. With OCR and NLP, decades-old logs of energy load, equipment maintenance, weather and meteorological data stored as text can be conveniently digitized, making it ready for analysis.
Next, it’s critical to perform health checks of energy assets and determine risks. There is no one-size-fits-all solution as every energy company has a unique set of factors. For example, some organizations will need to consider the proximity of energy assets to consumers, while others might need to consider how far the asset is from forests to determine the risk of catching fire. Asset evaluation is currently the most challenging aspect of the process as there is a multitude of variables involved and not every relationship between those variables is obvious.
Asset Replacement and Maintenance
To make utilities smart, the majority of energy assets have to be replaced or upgraded. For example, in many cases, it’s not economically efficient to upgrade old equipment that is close to being written-off. It’s better to let it complete its life cycle and install a newer model.
While it might be not so appealing to executives, applying big data in the energy sector almost always requires end-to-end revamp of the legacy workflows. For example, given that predictive maintenance is an essential part of the transformation, conventional manual equipment maintenance needs to be adjusted to accommodate it. This would call for retraining programs for maintenance professionals, too. As you can see, transforming workflows in such a large-scale industry as energy requires a solid long-term vision.
As with many other industries, the energy sector is on the verge of becoming fully data-driven. However, many challenges related to regulations and scalability emerge as we are moving forward. On a grander scale, a complete reimagining of workflows and significant technological upgrades are required.