The State of Dark Data in the Energy Industry
When we last wrote about dark data and its energy implications, we discussed how dark data is rarely leveraged to its fullest potential, as well as the roadblocks hindering its collection across industries. Let’s take a deeper dive into the current state of dark data in energy, and understand how it can be harnessed for greater efficiency within the energy industry.
In case you need a quick refresh, dark data refers to the data organizations gather but don’t actually use to generate revenue. That can include everything from email correspondence to log files to security video footage. All of those data points could potentially be harnessed to generate key insights, but instead, they sit idle and unused. With all that unlocked potential, dark data could play a much larger role in the energy industry.
How Data Drives Energy Decisions
Dark or not, data-driven decisions are made every day in the energy industry. Data is used to identify energy consumption levels, discover peak usage times, and to set energy pricing. Data is also key in financing large energy projects and analyzing the effects of government policies on private finance on renewable energy investments.
The growth of IoT-connected devices is also driving a big data boom in the energy industry. Smart meters, cloud computing, and other emerging technologies are making data collection and analysis easier than ever, leading to greater efficiency and a reduction of maintenance and operation costs along the value chain.
Still, collecting energy distribution and consumption data is only the first step towards scaling up energy efficiency. These advances are made using data that’s readily available — but the untapped resource of dark data is barely touched.
Future-Forward Energy Requires Smarter Data Use
Mobility, for example, has been greatly transformed by increased data usage. The emergence of data-driven ride-hailing apps, like Uber and Lyft, has fundamentally shifted how people get from point A to point B. Leveraging data from local traffic patterns, GPS, public transit routes, and internal data about the length of the journey, Uber and Lyft automatically calculate fares and seamlessly connect drivers to passengers.
Public transportation has also been optimized through data use. Connected devices and data analytics streamline operating procedures and help transportation companies cut costs. That data enables companies to analyze how many passengers use which routes and when, which allows them to deploy staff and vehicles (whether buses, trains, or planes) more cost efficiently. Additionally, data helps the public transportation sector predict passenger volumes as accurately as possible.
Similarly, energy usage data can help reveal the energy patterns of different consumers, which determines energy usage behavior and can help stabilize highs and lows to avoid blackouts. Of course, we can use past trends to predict future trends, but harvesting unused data to balance supply and demand is key to ensuring energy reliability and efficiency.
With an increased usage of IoT-connected devices, suppliers could better track energy creation at its source, travel time, availability, price, and demand — all of which could be predicted in real-time. Residential smart meters, for example, generate troves of beneficial data. In the near future, using smart meter data can become a critical component to energy efficiency and demand response.
We shouldn’t just stop at using IoT and smart technology to simply generate insights, however. Just as important is learning how to share all those data insights in a cohesive system, allowing IoT-enabled devices to communicate with greater efficiency. Currently, there’s no consensus on how connected devices communicate. Nor are there incentives for data owners to share their data. Data owners, suppliers, and producers should be incentivized to share their energy data, creating opportunity for deeper insights to be unearthed by other energy solution providers.
Energy Should Keep Its Eye Towards Transformation
Today, data is leveraged across energy generation, trading, transmission, distribution, and consumption. There’s a general consensus on how this existing data can be further leveraged for future insights, as ongoing energy digitization and decarbonization creates vast opportunities for further data usage. But it’s hard to determine what data is missing or unavailable when most available energy data goes unused to begin with.
Even though the emergence of IoT devices is creating a data superbloom, much of that data is typically siloed across business realms (generation, transmission, and distribution, etc.). Those individualized silos can’t capture the value of information like aggregated and structured data does. Instead, that data becomes locked away, never to be extrapolated and analyzed for further use.
Innovative players in the energy space (like Dash Energy) are working to change that, building tools that make extracting, aggregating, analyzing, and sharing data easier than ever before. The more data is created, the more important data digitization, analysis, efficiency, and modernization becomes.
Data usage in energy is growing, but the industry still needs to catch up. It’s nowhere near where transportation and other industries are in terms of data analysis and deployment. Energy players should take advantage of connected devices and discover solutions for sharing and aggregating all collected data in order to maximize efficiency, balance demand response, and lower costs. Energy data that currently goes unused could enable new solutions for near-future challenges, such as grid congestion and optimizing battery and EV charging.
The energy industry needs to leverage the tools at its disposal to make the most of the data it generates.
When maximized to its fullest potential, data is a major driver for energy efficiency — we just have to keep pushing it to new heights for innovation.
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