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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 

IBM Research-China unveiled a Watson-life forecaster that estimates pollution to mitigate its effects. Dubbed Green Horizons, it also forecasts energy usage in energy-stressed cities, which allows for better integration of renewable energy sources without the need for grid-scale batteries.

IBM announced its forecaster on the last day of the Conference of the Parties (COP21, Nov. 30—Dec. 11 in Paris) during the Intergovernmental Panel on Climate Change (IPCC 2015) forecast.

The Green Horizons Pollution Forecaster is based on Watson-like cognitive computing, which draws on vast amounts of environmental big data from environmental monitoring stations, weather stations and traffic cameras, as well as meteorological and environmental satellites to create a predictive model for more informed decisions on how to improve air quality.

"We perfected our forecaster in Beijing, in cooperation with the Beijing Environmental Protection Bureau [BEPB]—cutting pollution by 20 per cent," Hendrik Hamann, physical analytics manager at IBM Watson Research Lab told EE Times in an exclusive interview. "Our software enables us to forecast both future pollution levels as well as future energy output of renewable energy sources like solar cells and wind turbines—which mathematically use very similar machine learning algorithms."

IBM aims to create a new generation of renewable energy forecasting systems which enable utility companies to accurately predict levels of available wind and solar energy several days in advance with 90 per cent accuracy. The result is savings of thousands of megawatts of energy, according to IBM.

For example, solar farms use sky-facing cameras to monitor cloud movement and calculate their potential blocking impact on solar radiation. Wind turbines are fitted with sensors 80m above the ground to monitor wind speed, moisture and air pressure, creating optimal balance between supply and demand in energy markets and better usage of renewable resources thus cutting down on greenhouse gases from conventional power plants.

In a nutshell, IBM placed its own Internet of Things (IoT) sensors (see first photo) all over Beijing—especially near known pollution producers—then hooked them up to a central monitoring station at IBM Research where their cognitive computer algorithms could make intelligent choices about how to mitigate pollution. Two types of recommendations were made: the first to permanently "clean-up" repeated offenders so as to minimise the constant pollution emitted every day; and secondly to give 15 minute to 72-hour notice to pollution producers to cut down on emissions during forecasted peaks, thus levelling out the pollution level to 20 per cent less that just a year ago.

Jin Yan Shao

IBM Green Horizons Project Leader Jin Yan Shao carries a prototype of a mobile and compact environmental monitoring unit. (Source: IBM, used with permission)

"We also plan to continue this process, hoping to hit 25 per cent pollution reduction by 2017 and then continue reducing it to 35 per cent less by 2018 and then by 10 per cent more per year thereafter," Hamann told EE Times.

That kind of optimism has attracted arguably the most polluted city in China, Baoding, to sign up with IBM, as well as Zhangjiakou, China—site of the 2022 Winter Olympics—as well as other polluted cities around the world, including Delhi, India and Johannesburg, South Africa.

Renewable energy backers in Japan, Europe, the United Kingdom and the United States are also signing up for Green Horizons energy forecaster software.

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