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Neural-Network-Based Software Stabilizes Power Grids


New self-learning software system from Siemens uses neural-network technology to help grid operators more accurately forecast the electrical output from renewable energy sources. The researchers say their forecast over a 72-hour period was more than 90 percent accurate. Grid operators can use this type of information to calculate network power demand, which can help them more accurately place advance orders with suppliers for additional electricity. The Siemens algorithm calculates projected transfer losses directly from electricity-consumption forecasts. Switzerland’s Swissgrid national electricity-transmission company estimates using the software will save it about 200,000 euros per year. (PhysOrg)(Siemens)

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