Using predictive analytics on their data, IBM writes, utility companies can make a wide range of forecasts, such as:
-- How much excess energy will be available, when to sell it and whether the grid can transmit it.
-- When and where equipment downtime and power failures are most likely to occur.
-- Which customers are most likely to feed energy back to the grid, and under what circumstances.
-- Which customers are most likely to respond to energy conservation and demand reduction incentives.
-- How to manage the commitment of larger, traditional plants in a scenario where peaks from distributed generation are becoming relevant.
Modeling, forecasting and simulation know-how is where much of energy-optimization company Stem's intellectual property resides, company founder and CEO Salim Kahn told InformationWeek recently. Stem's turnkey system, which analyzes large amounts of data, including historical weather information and industry usage patterns, uses predictive algorithms to help companies determine the best times to pull power from the grid.
Big data sets are also a fact of life at 3Tier, which provides renewable energy risk analysis, assessment and forecasting for alternative energy projects in wind, solar and hydro. Among other things, the company looks at 40 years of global climate data in its model.
"The data really piles up," IT director Paul English says in a video for EMC Corp. The company's supercomputer cluster outputs about a terabyte of data per day, which are held in EMC storage systems.
Bringing "Victorian-esque" electrical systems into the computer age is a mission of Glynne Townsend, CEO at Spider9, which manufacturers advanced control systems for energy storage and solar fields.
A field with thousands of solar panels, batteries and inverters -- all constantly changing in response to environmental conditions -- is dramatically more complicated and produces one thousand times more data than a traditional electrical network, Townsend said in a phone interview.
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