by Paul Rudo on 15/10/12 at 10:44 am
At the same time as we’re seeing increasing worldwide demand for energy, we’re also reaching an ecological tipping point. This has prompted governments all over the world to provide incentives for companies and individuals to reduce their power consumption.
Of course, this blog has devoted lots of times to discussing the power management and sustainability issues surrounding green computing.
And we’re also seeing a shift in the consumer market, as homes are employing more energy efficient devices such as LED lighting. We’re also seeing a surge in the use of internet-enabled power outlets which monitor energy use in order to find efficiencies.
But all of these efforts put the energy-saving burden on the shoulders of the end user. What about the use of analytics and IT by the power utilities themselves?
Utilities companies are amongst the most important early-adoption candidates for large-scale big data initiatives, since they have access to massive pools of real-time streaming data from GIS, weather stations, electronic power meters and other sources.
Especially in developing countries like India and Brazil, power theft has always been a major issue for power utilities. New, more secure electronic power meters make it harder to circumvent the mechanical security mechanisms of the previous generation. Less theft means less waste, and better use of a precious resource.
Data collected from electronic power meters can also help with predicting loads and reacting to unexpected spikes in energy usage which could lead to brown-outs.
Big Data analytics are absolutely critical when it comes to selecting appropriate sites for new solar and wind projects. Some of the more powerful high-performance computing projects currently underway are devoted to simulating wind patterns and other environmental phenomena in order to determine the most ideal sites for new renewable energy projects.
Of course, renewable and distributed energy sources also bring along their own problems. When energy is being generated across multiple sites, by systems ranging from small wind farms to home solar installations, this has the potential to create dips and spikes in energy use which could be damaging to the grid infrastructure. Utilities are using Big Data analytics to get around these problems.
For example, power usage data can be analyzed in order to minimize undue strain on transformers. This extends the life of the transformers, resulting in fewer power outages, less maintenance and lower costs.
As our energy needs grow, we’ll need to maximize our use of environmentally-friendly energy sources by diversifying the renewable sources that we rely upon. This will bring up a number of new problems which can best be solved using sophisticated data modeling and predictive analytics. For more information on this topic, I’d recommend reading this interview with mechanical engineer Brad Van Orden.
Image Source: http://www.flickr.com/photos/traftery/2996254784