Renewable energy companies have experienced strong global growth over the last several years, but they face pressure to improve profitability and productivity as the industry continues to scale globally. That said, the energy business can no longer be differentiated by simply applying more improvements via mechanical engineering and physics.
The next wave of innovation will be driven by sensors and data – in other words, the Internet of Things (IoT).
To keep up with strong demand, renewable energy companies have been greatly increasing capacity. According to World Wind Energy Association, cumulative capacity in the wind energy sector has increased from 24 GW in 2001 to 370 GW in 2014.
However, with strong growth comes the challenge of scaling operational excellence and maintaining profitability. Companies seek improved methods to manage much larger capacities, with many more physical assets, located in widely distributed and remote areas – a very complex situation.
IoT analytics can help. Today’s IoT data can be analyzed at near real-time speeds due, in part, to the growth of complex event processing systems. In fact, one could argue that the effective use of IoT will be a key differentiator for the winners in this next phase of growth in the renewable energy sector.
I believe there are at least four growth areas of IoT analytics use in this vertical:
- Big data, fast data and more analytics
- Horizontal integration and vertical application
- Integration of advanced analytics and machine learning
Big Data, Fast Data and More Analytics
The good news is that we will generally see more demand for IoT analytics. Due to increased competitive pressure, organizations are forced to optimize processes and products. The key to identify optimization opportunities and track improvements is in the IoT data itself. But, as sensors and data processing becomes cheaper every day, we will see more data available, and the expectation is that it will be processed in near-real-time.
In addition to analyzing data for learning, near-real-time analytics allows companies to react quickly – to avoid problems, address them before they become serious, offer help or simply better prepare in advance for an emerging issue to reduce its impact.
Take a large wind turbine farm, for example. A sensor that collects real-time data from turbines that is quickly analyzed and turned into actionable insights is a key competitive advantage. Companies are already using advanced sensors to continually assess acceleration, temperature and vibration. Extracting data from wind turbines uncovers trends for performance optimization to increase productivity and predictive maintenance to minimize downtime.
Horizontal Integration, Vertical Application
Today’s business world is seeing a tremendous shift in the speed at which we have to respond to market changes, leading to the need for a very flexible solution that can be adapted on the fly, ideally by business people, and not requiring complex tasks such as IT-support, coding or deployment.
I believe we will see more horizontal components with tremendous market success. Organizations will use building blocks optimized for certain infrastructural tasks, including device management, data collection, storage, analytics and application management. However, we will also see vertical applications, such as in the renewable energy sector, because it is the best way to put data in context and convey insight to a specific, unique user-group.
For instance, analyzing turbine sensor data with greater granularity shows the relevant information in a way that the target audience easily understands. Analytic front ends, as beautiful as they may be, are only good for analytically minded people. In my experience, not everyone enjoys “surfing data;” most prefer a context-specific presentation of just the most relevant data.
We will see much growth in data volume coming from disparate devices that require both real-time and post-mortem analysis. Knowing the importance of efficiency, do we really want to transfer all of the (low-value) log data from all sensors, machines and switches to a central data analytics installation?
The costs of transferring data from all over the world in real-time to a central location is much higher than the savings through the economy-of-scale of a centralized solution. Further, network latencies and interruptions omit the usage of centralized solutions. A fundamental principle for fast and efficient data processing is to move the query to the data.
With IoT, where data is created from geographically distributed devices, we will see a decentralization of data storage, processing and analytics. Technology in renewable energy running on a system’s edge (the sensors on the wind farm, for example) benefits from the ability to run queries at any given time, whether in its own data center, in the cloud, or at customer locations by using edge analytics. These requirements are not possible with legacy or general-purpose technologies, which are not optimized for IoT.
Integration of Advanced Analytics and Machine Learning
There is so much talk about advanced analytics (AA), artificial intelligence (AI) and machine learning (ML) that most people have a hard time understanding these technologies. The good news is, the majority of people do not have to understand; they should spend their time on their operational processes and products – not on the math. My view on AA (including AI, ML, etc.) is that it delivers hints that normal people might have missed due to volume, speed and complexity of the IoT data available.
The use of advanced analytics and machine learning, for instance, can help a utility check for irregularities in operational performance, potentially leading to the prediction of a potential failure before it happens.
With the world relying more heavily on renewables as a critical source of energy, we need a way to harness IoT data quickly and accurately in order to successfully scale renewable energy across the globe. One could argue that the effective use of IoT will be a key differentiator for the winners in this next phase of growth in the renewable energy sector.
Lead image: Laptop and images of nature. Credit: Shutterstock