Big data analytics is clearly on a penetrative path across all arenas that rely on technology. Data analytics has also made its advent through IBM into the renewable energy, particularly wind power arena.
The unreliable world of renewable energy
Renewable energy is a largely unreliable source, this is due to its dependence on weather conditions that cannot be controlled or manipulated. IBM, however has announced a new solution that will increase the reliability of renewable energy sources.
The advanced power and weather modeling technology combines analytics with weather prediction to increase the accuracy of the prediction of availability of renewable energy sources such as solar energy and wind power.
The model helps further the efforts towards a sustainable future. The prediction system will allow an integration of the renewable energy into the regular power grid. The integration will in turn lead to reduced carbon emissions while ensuring a fairly regulated supply of clean energy.
The product according to IBM has been named “Hybrid Renewable Energy Forecasting” or the acronym HyRef. The product uses a sum total of various technologies that are used to predict weather. Advanced cloud imaging technology, weather modeling capabilities work in tandem with cameras that are facing the sky. The cameras track the movement of clouds as they happen.
Further, the wind turbines that generate wind power also have sensors which monitor the speed of the wind, the direction of the wind and the temperature. The data that is collected through all the machines is assimilated through the analytics technology.
The solution that uses data assimilation by IBM can provide accurate weather forecast reports as much as a month in advance. The technology not only finds long term application but also in short term application where the wind farms can receive local weather forecasts in fifteen minute increments.
The IBM word
IBM’s General Manager of the Global Energy and Utilities Industry Brad Gammons spoke about the technology when he said, “Applying analytics and harnessing big data will let utilities tackle the intermittent nature of renewable energy and forecast power production from solar and wind, in a way that has never been done before. We have developed an intelligent system that combines weather and power forecasting to increase system availability and optimize power grid performance.”
The utilization of the technology
Apart from increasing the reliability of the renewable energy source, HyRef has also made possible the analysis of the performance of individual wind turbines. Every wind turbine can be monitored for the amount of energy generated.
This accuracy of prediction will revolutionize how renewable energy is perceived. The integration of conventional energy sources such as coal and natural gas can be better integrated with renewable energy sources. The amount of renewable energy that can be directed to power grids can be predicted leading to a better management of resources.
Vice Admiral Dennis McGinn, the CEO and President of ACORE (American Council On Renewable Energy) predicted the use of the technology when he said, “Utilities around the world are employing a host of strategies to integrate new renewable energy resources into their operating systems to reach a baseline goal of a 25% renewable energy mix globally by 2025. The weather modeling and forecasting data generated from HyRef will significantly improve this process and in turn, put us one step closer to maximizing full potential of renewable resources.”
The fact that the renewable energy can be integrated into power grids was underscored with a ringing clarity when State Grid Jibei Electricity Power Company (SG-JBEPC) integrated renewable energy into its power grid using HyRef. The Power Company is a subsidiary company of the State Grid Corporation of China (SGCC).
China is engaged in a five year plan to utilize clean energy and reduce its reliance on traditional sources of energy like fossil fuels. The integration of HyRef was a part of the initiative called the Zhangbei 670 MW demonstration project. The combination of wind power, solar energy, transmission and energy storage is demonstrated in the initiative.
The use of HyRef, IBM’s technology has allowed the Zhangbei project to increase the integration of renewable energy sources by 10%. The prediction has increased the utilization of wind and solar energy while the analytics provides the information that is required to turn up the efficiency of the grid.
A winning combination was created when IBM’s analytics teamed up with Denmark’s leading wind turbine manufacturer Vestas Wind Systems. The supercomputing technology and the big data analytics technology allows Vestas to strategically place their wind turbines in areas where the maximum output of energy can be expected through the year. The data that the placement of wind turbines relies on is accumulated through tidal phases, weather reports, sensors, deforestation maps, satellite images and weather modeling research. The bulk of data will afford the wind turbine company lower running costs through a reduced operational and maintenance cost and a vast improvement in the energy generation.
The technological advancements that the Hybrid Renewable Energy Forecaster stands for stems from technological advancements in weather modeling technology such as Deep Thunder. The technology, another brainchild of the same IBM, provides micro forecasts in high resolution for weather in a particular area.
The technology can be used to monitor and predict weather in an area as small as a metropolitan area to an area as large as an entire state. Every square kilometer will receive its own micro weather forecast.
The scope of the technological advancement is unlimited. Nearly every arena of life can use the accurate weather prediction. The data analytics combined with weather prediction can help governments prevent large scale disasters. It can help businesses create tailor made services. Public transport routes can be changed to accommodate bad weather. Governments and businesses can deploy the appropriate equipment ahead of time to minimize the ravages of weather. Lives and costs can be saved. The efficiency of services across the board can be increased through the appropriate use of technology.