Renewable energy companies have made a significant amount of progress in terms of global growth throughout the past several years. Even so, they're under constant pressure to boost both profitability and productivity. We've come to a point, however, where these improvements can no longer reply upon mechanical engineering and physics. Instead, the next wave of industry innovation will be ushered in by sensors and data, or, the Internet of Things (IoT).
IoT analytics make it possible for energy companies to keep up with increasing demands without sacrificing profits. IoT data can be analyzed in real-time via complex even processing systems, thus enabling businesses to better manage large capacities and physically assets - even when they are located in very widely distributed and/or remote regions. It's the simplest solution to a very complex problem, and IoT will certainly act as the key differentiator between the winners and the losers in this stage of growth within the renewable energy industry. Here's how:
The demand for IoT analytics is sure to increase along with a rise in competitive pressure for renewable energy companies to keep up with market demands. With near real-time big data analytics making it possible for companies to react to potential problems quickly, they will improve overall efficiency and productivity while avoiding disaster. As an example, a wind turbine farm might utilize sensors which collect real-time data from the turbines. That data is analyzed and turned into actionable insights about acceleration, temperature, vibration, etc. This will reveal trends for performance optimization and allow for predictive maintenance as well.
Horizontal Integration Paired With Vertical Application
Energy companies are seeing a big change in the speed at which they must respond to market changes and customer demands. This creates the need for flexible solutions that can be adapted at a moment's notice. We're sure to notice more horizontal components being optimized for infrastructural tasks like device management, data collection, analytics, storage, and app management. At the same time, vertical applications will increase in prevalence as it's the smartest way to put data into context and convey insights that pertain to specific user groups.
There's been a lot of growth in the volume of data that is coming from disparate devices and demands real-time and post-mortem analytics. Unfortunately, the costs associated with transferring all sensory log data (including low-value data) to a central analytics hub is not efficient. In addition to this, network latencies and interruptions would create problems for centralized solutions. With IoT, we will likely see a decentralization of the data storage and analytics processes as they pertain to geographically distributed sensors, switches, and machines. This will make it easier to run queries directly to the data at any given time. With legacy technologies that aren't optimized for IoT, this is not a possibility.
Machine Learning Meets Advanced Analytics
The usage of advanced analytics (AA) and machine learning (ML) will be used more and more in the renewable energy sector. For example, these technologies might help with utility checks for any irregularities and system performance. Through predictive analysis, potential failures could be detected and prevented before they occur. With the help of both AA and ML, we can detect even trace hints that people would have otherwised missed due to the complexity, volume, and speed of available IoT data.
If you weren't interested in the impact of the Internet of Things on today's energy industry, you should be now. After all, advancements could mean increased energy efficiency and lower consumer costs. See for yourself how EnerTrac can keep you aligned with your energy costs, consumption, savings, and more.