[Case study] How to optimize energy investments
eSmart Systems wanted to put big data and IoT technologies to work for its energy customers to optimize their investments and enable next-generation operational performance. With industry changes such as smart meter and renewable energy adoption, utilities companies needed to make data-driven decisions to improve efficiency and cut cost. eSmart Systems use MS Azure and InfluxDB Enterprise to gather vast amounts of data from sensors and analyze it using advanced prediction and optimization models. This results in a completely new way of visualizing data, while helping their customers make decisions faster to save resources and costs.
The Challenge
To solve new energy industry problems, eSmart Systems decided to use deep learning to find problems automatically; use drones as “the eye in the the sky”; develop a tool for field crew that makes their job easier and safer; and attempt to predict problems before they turn into critical errors. Machine learning and analytics help them better understand what is about to happen because they have established a timeline with their very broad definition of time series.
Download this case study to understand better how eSmart Systems uses MS Azure and InfluxDB Enterprise to optimize energy investments.
Contents:
- Case overview
- The business problem
- The technical problem
- The solution
- Results
This case study is sponsored by InfluxData
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