From hospitality to manufacturing to oilfields, industries have started adopting sensor-enabled assets since few years now. Companies have started utilizing sensor data for predictive maintenance and other relevant uses. Electrical devices are being designed with energy efficiency in mind to reduce power requirements. Energy efficiency will be a major focus to reduce global greenhouse emissions. Companies are looking for solutions to reduce energy costs and consumption.
Energy data analytics opens new doors to identify areas of inefficiency and implement targeted energy-saving initiatives. Using machine learning and big data analytics for energy efficiency can help address critical challenges in quality, productivity and efficiency.
Get more done from your sensor data
Sensor data tells you the current situation of your machines or assets and accordingly lets you take relevant actions. But what more can be done with that data?
Sensor data helps organizations make predictions, inform users / customers to make adjustments and so on. Companies are implementing smart building solutions that are capable of providing information and insights, but they lack automation and self-optimization. If organizations implement machine learning applications, they can develop strategies to reap greater savings and improve efficiency of their building assets.
Sensor data leads to greater energy savings that subsequently lead to optimizing energy production on the industrial level. These data points carry statistically significant information about how sources of energy are distributed, produced and consumed. And when analyzed, the data can provide companies with information on how efficiently they can distribute and consume those sources.
Business transformation with big data and machine learning
Webinar agenda
- Overview: Big data and machine learning
- Real world uses and benefits of ML
- Business uses of ML
- ML solutions for varied industries
- ML & data visualization: Seeing is believing
- Pathway to success – Onboarding (PoV)
- Demo
- Q&A
Saving energy, saving cost, and saving time
Global energy consumption grows by around one-third of the Outlook 2017 in 2018. – BP
Applying machine learning on the data collected via sensors can increase facilities management efficiency, enabling optimal decision-making without the need to consult numerous dashboards. Organizations can intelligently increase the efficiency of entire machine parks and save energy costs at the same time.
Predictive maintenance helps with machine uptime and utilization while production bottlenecks, quality issues, and energy optimizations are identified by pulling and analyzing available data from across manufacturing facilities.
Machine learning offers a simple and cost-effective solution for improving energy efficiency. It can help plant managers with the following functions:
- Understanding their facility’s energy consumption
- Monitoring usage for excess or untimely consumption
- Prioritizing specific energy efficiency retrofits
- Setting informed and achievable energy savings targets
- Reducing overall energy costs
Automation in smart meters in terms of collecting data in the form of usage patterns and trends across geographies has led to an exponential reduction in the time and effort required to collect data from energy meters. Data collected from smart meters, costs and production figures, asset operations, and even weather data can be used and analyzed over the long term to get outcomes like source of energy leakages, and overly consumed energy sources can be easily determined and acted upon.
Big data and machine learning are changing the energy consumption paradigm
With technologies like big data and machine learning, employees can now monitor the temperature and performance of production machinery in real-time, and react immediately to solve problems. Companies will be able to optimize heating, ventilation and air conditioning (HVAC) usage and analyze patterns to deploy energy-efficient lighting systems. It becomes easier for organizations to monitor smart equipment to identify non-performing ones and fix the issue in an efficient manner.
Our IoT platform, IoTConnect, helps you connect all your critical devices, machines, people and systems to capture the valuable information hidden in the data. And our machine learning platform, SIA, helps users to get prescriptive analysis to enable them to view insights, based on which, they will be able to forecast energy consumption and utilize the data to improve energy efficiency.
If you aim to save energy at your organization but aren’t sure on how to get started, you can consult our data experts and get a desirable solution.
