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IBM develops pollution, energy-waste forecaster

Posted: 10 Dec 2015     Print Version  Bookmark and Share

Keywords:Watson  Green Horizons  renewable energy 

Hamann expects the technology will combine energy generation and energy usage peak forecasts to help traditional power plants cut back on generation to accommodate abundant renewable energy peaks. Similarly, those plants would have information on when to increase energy output to accommodate renewable energy shortages, such as when the sun is not shining or the wind is not blowing.

If all power needs can be met by merely balancing the outputs of renewable and non-renewable sources in near-real-time, IBM's Green Horizons can mitigate or perhaps even eliminate the need to invest in costly energy grid batteries. In fact, usage of grid-sized batteries would require the same kind of balancing algorithms as Green Horizons provides without the need for costly grid batteries, so it may turn out that the world does not need grid-sized batteries after all (except in remote locations with no grid connection).

Xiaowei Shen

IBM Research-China Director, Xiaowei Shen is supporting China's ambitious energy and environmental commitments through its 10-year Green Horizon initiative. (Source: IBM, used with permission)

For instance, is it better for a metropolitan area to invest in more elaborate scrubbers to eliminate 2.5µ particulate (2.5PM) and harmful chemicals from the air, or would the money be better spent converting the city's fleet of petrol-burning busses to natural gas? These kind of what-if questions can be quickly and accurately accessed by Green Horizon's forecasting capabilities.

Jin Dong

Leader of IBM's Green Horizon initiative, Jin Dong, Distinguished Engineer & Member of IBM Industry Academy, Associate Director at IBM Research—China. (Source: IBM, used with permission)

Green Horizon's evaluate energy scenarios can also be quite useful, Hamann said, in weighing whether additional wind farms would be more useful than additional solar panels—which would give "the best bang for the buck" as Hamann put it. Or whether phasing out the most polluting traditional power plant would put undo stress on the grid and if it would what options might be the best way to alleviate that stress.

"Our forecasting models combined with our what-if technology helps decision makers make better decisions the first time by eliminating the need for costly and time-consuming trial-and-error," Hamann said.

Baoguo Xie

Baoguo Xie, IBM Research-China, working on Green Horizons Project. (Source: IBM, used with permission)

The 20 per cent lowering of pollution levels in Beijing was made with a combination of temporary—such as cut power plant production by 10 per cent tomorrow—and permanent solutions, such as converting busses to natural gas or installing more scrubbers in smoke stacks. IBM plans to install wind speed IoT sensors on every turbine and IR IoT sensors next to each solar panel, which use machine learning algorithms to create an accurate model, then enable better decisions on how to integrate renewable energy generation resources with traditional power plants.

Hamann expects that customers will be a mix of city governments, environmental protection agencies, utility companies and factories. In Bejing, IBM is creating a Joint Environmental Innovation Centre with the government. In Baoding and Zhangjiakou it is targeting air pollution, in Dehli targeting automobile pollution and in Johannesburg targeting air pollution, with many more cities coming on board in 2016.

IBM Research scientists

IBM Research-China scientists (Left to Right): IBM Research-China's Green Horizons environmental expert Meng Zhang, IBM Research-China Associate Director Jin Dong, and IBM Research-China Green Horizons researcher Weida Xu in the IBM ThinkLab, Beijing. (Source: IBM, used with permission)

Likewise, IBM expects Green Horizon to grow from its existing work in Japan (Toyo Engineering Corp. and renewable energy company Setouchi Future Creations LLC on the Setouchi solar project, to China (Xingjiang Goldwind Science and Technology Co., the third largest wind turbine manufacturer globally and the Zhangbei Demonstration Project, managed by China's State Grid Jibei Electricity Power Company), to the U.K. (SSE plc formerly Scottish and Southern Energy plc), to the U.S. (Department of Energy's SunShot initiative with renewable energy forecasting for government agencies, utilities and grid operators nationwide) to many other countries in line with Barclay's predicted $30 trillion to be invested in green energy generation by 2040 "as a windfall" of COP21 agreements, according to IBM.

- R. Colin Johnson
  EE Times U.S.


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