Enterprise IT vendor IBM today announced its 10-year initiative to support China in transforming its energy systems.
Green Horizon project will focus on air quality management, renewable energy forecasting and energy optimization for industry.
Air pollution challenges
IBM and the Beijing Municipal Government, one of the first partners, will develop solutions which can tackle the city’s air pollution challenges, leveraging IBM technologies such as cognitive computing, optical sensors and the internet of things — on Big Data and analytics platform.
DC Chien, chairman and CEO, IBM Greater China Group, said: “While other nations waited until their economies were fully developed before taking comprehensive action to address environmental issues, China can leverage IBM’s information technologies to help transform its energy infrastructures in parallel with its growth.”
Beijing will invest over $160 billion to improve air quality and deliver on its target of reducing harmful fine Particulate Matter (PM 2.5) particles by 25 percent by 2017. IBM is partnering with the Beijing Municipal Government on a system to enable authorities to pinpoint the type, source and level of emissions and predict air quality in the city.
IBM’s cognitive computing systems will analyze and learn from streams of real-time data generated by air quality monitoring stations, meteorological satellites and IBM’s optical sensors – all connected by the internet of things.
By applying supercomputing processing power, scientists from IBM and the Beijing Government aim to create visual maps showing the source and dispersion of pollutants across Beijing 72 hours in advance with street-scale resolution.
Renewable energy forecasting
The Chinese government recently announced increased investment in solar, wind, hydro and biomass energy in a bid to decrease its dependency on fossil fuels. IBM recently developed a renewable energy forecasting system to help energy grids harness and manage alternative energy sources.
The solution combines weather prediction and Big Data analytics to accurately forecast the availability of renewable energy which is renowned for its variability. It enables utility companies to forecast the amount of energy which will be available to be redirected into the grid or stored.
It increases the viability of renewable energy, helping the Chinese government to realize its objective of getting 13 percent of consumed energy from non-fossil fuels by 2017 and enabling the construction of the world’s biggest renewable grids.
Based on IBM’s Hybrid Renewable Energy Forecasting (HyRef) technology, the solution uses weather modeling capabilities, cloud imaging technology and sky-facing cameras to track cloud movements, while sensors monitor wind speed, temperature and direction. It can predict the performance of renewable energy farms and estimate the amount of energy several days ahead.
IBM said the system has already been rolled out to 30 wind, solar and hydro power sources. The biggest deployment is at China’s largest renewable energy initiative – the Zhangbei Demonstration Project managed by State Grid Jibei Electricity Power Company Limited (SG-JBEPC) in the Northern province of Hebei.
Using the system, SG-JBEPC is able to integrate 10 percent more alternative energy (enough for 14,000 homes) into the national grid. With a prediction accuracy of 90 percent proven on Zhangbei’s wind turbines, it is one of the most accurate energy forecasting systems in the world.
Energy Optimization for Industry
China government has committed to reducing the country’s carbon intensity by 40-45 percent by the year 2020, compared with 2005 levels (equivalent to 130 million tons of coal per year).
To support these goals, IBM is developing a new system to help monitor, manage and optimize the energy consumption of industrial enterprises – representing over 70 percent of China’s total energy consumption.
Using a Big Data and analytics platform deployed over the cloud, it will analyze vast amounts of data generated by energy monitoring devices and identify opportunities for conservation. It could be used to analyze data from industrial enterprises in different cities and identify which sites and equipment waste the most energy.