Big Data makes a big difference in how we generate and distribute energy and forecast our energy needs. Let's take a closer look at how the energy sector is levaraging Big Data to ensure cost-effective energy generation.
The world’s demand for energy is insatiable whether it is solar, wind, tidal, oil and gas, thermal, hydro-electric, wood, nuclear energy.
There are two types of energy -- renewable and non-renewable. The non-renewable energy is scarce such as fossil fuels and nuclear material and is found in earth and there is a limited availability. This energy we use in our daily lives, whether in our automobiles or for generating electricity, which powers our home.
Renewable resources regenerate as we use them and include solar, wind, water, magnetic and bio-fuel created from farming. This is the future of energy for the planet and its inhabitants and is generally known as green energy.
As the cost of energy increases and its availability decreases there is an extensive use of collating data in the discovery, extraction, processing and transmission and distribution of energy.
The energy business is increasingly using Big Data and cloud computing to ensure efficiency and cost effective solutions. Let’s take a closer look at the role Big Data and cloud computing is playing in the energy sector.
Exploration:
We will begin with exploration -- a geologist needs to identify the depth at which the non-renewable fuel can be found, the expected quantum that can be extracted and also the quality and yield. The geologist on the field collects data of a number of exploratory sites to create humongous quantum of data. This Big Data has then to be sieved and mapped into economics, local conditions and then a choice has to be made on which locations will be explored. This is an exacting and back breaking activity and on this rides millions of dollars of profit or loss of energy exploration companies. Big Data is able to handle discrete and non-discrete data and provide significant results in identifying potential sites for exploration.
Extraction
Extraction is a challenging task and has environmental issues to it, both in the harshness of the locations where the energy is extracted plus the impact of environmental damage caused as a by-product of extraction. The regulatory laws are getting tougher both for managing the people who are at risk at these extraction sites, which are a high risk environment and also in case environmental damage is caused. Big Data helps the extraction companies manage these risks and also arrive at optimum risk and ensure governance and best practices to contain risk. Many exploration companies use Big Data to manage a number of extraction by automated systems and keep the need for humans in the sites as low as possible.
Processing
The extracted fuel has to be processed to get the energy in a form that can be used, be it petrol, diesel, gas, shale, coal. The processing again needs data management to arrive at optimum output from the given raw material. Big Data is also extensively used in processing.
Generation
When energy is converted from one form into another, mostly into electrical energy, Big Data computation and analytics again plays an important role in generating this energy and optimizing the generation needs to bring about efficiency and ensure cost-effective energy generation.
Distribution
Distribution of energy and the supply chain logistics also has Big Data in the forefront. Whether it is electrical transmission and distribution grid management, oil pipes for pump liquid fuels or managing wind and tidal energy firms, cloud computing and Big Data is used to reduce the distribution losses and deliver at the most cost effective methods.
Metering
Metering of consumption and prediction of usage of energy is important, one for collection of revenue and second for forecasting the energy consumption. Also, green buildings can be monitored and managed by using applications in the cloud, for that matter city energy grids can be managed and optimized by using applications in the cloud coupled with Big Data.
Energy Markets
The energy markets are growing globally and cloud computing and Big Data analytics today plays an instrumental role in helping traders make informed decisions in the market related to risks and making profits from energy trading.
The future of clean, efficient and cost effective energy is being driven by the use of Big Data and cloud computing.
Cloud is by itself a significant contributor to green energy and contributes in reducing the carbon footprint of the world.
With Cloud and Big Data we can take it to the next level by providing clean, renewable and cost effective energy for the planet.
This was published in Information Week - India.
http://www.informationweek.in/software/13-05-03/how_big_data_can_help_in_meeting_big_energy_needs.aspx
The world’s demand for energy is insatiable whether it is solar, wind, tidal, oil and gas, thermal, hydro-electric, wood, nuclear energy.
There are two types of energy -- renewable and non-renewable. The non-renewable energy is scarce such as fossil fuels and nuclear material and is found in earth and there is a limited availability. This energy we use in our daily lives, whether in our automobiles or for generating electricity, which powers our home.
Renewable resources regenerate as we use them and include solar, wind, water, magnetic and bio-fuel created from farming. This is the future of energy for the planet and its inhabitants and is generally known as green energy.
As the cost of energy increases and its availability decreases there is an extensive use of collating data in the discovery, extraction, processing and transmission and distribution of energy.
The energy business is increasingly using Big Data and cloud computing to ensure efficiency and cost effective solutions. Let’s take a closer look at the role Big Data and cloud computing is playing in the energy sector.
Exploration:
We will begin with exploration -- a geologist needs to identify the depth at which the non-renewable fuel can be found, the expected quantum that can be extracted and also the quality and yield. The geologist on the field collects data of a number of exploratory sites to create humongous quantum of data. This Big Data has then to be sieved and mapped into economics, local conditions and then a choice has to be made on which locations will be explored. This is an exacting and back breaking activity and on this rides millions of dollars of profit or loss of energy exploration companies. Big Data is able to handle discrete and non-discrete data and provide significant results in identifying potential sites for exploration.
Extraction
Extraction is a challenging task and has environmental issues to it, both in the harshness of the locations where the energy is extracted plus the impact of environmental damage caused as a by-product of extraction. The regulatory laws are getting tougher both for managing the people who are at risk at these extraction sites, which are a high risk environment and also in case environmental damage is caused. Big Data helps the extraction companies manage these risks and also arrive at optimum risk and ensure governance and best practices to contain risk. Many exploration companies use Big Data to manage a number of extraction by automated systems and keep the need for humans in the sites as low as possible.
Processing
The extracted fuel has to be processed to get the energy in a form that can be used, be it petrol, diesel, gas, shale, coal. The processing again needs data management to arrive at optimum output from the given raw material. Big Data is also extensively used in processing.
Generation
When energy is converted from one form into another, mostly into electrical energy, Big Data computation and analytics again plays an important role in generating this energy and optimizing the generation needs to bring about efficiency and ensure cost-effective energy generation.
Distribution
Distribution of energy and the supply chain logistics also has Big Data in the forefront. Whether it is electrical transmission and distribution grid management, oil pipes for pump liquid fuels or managing wind and tidal energy firms, cloud computing and Big Data is used to reduce the distribution losses and deliver at the most cost effective methods.
Metering
Metering of consumption and prediction of usage of energy is important, one for collection of revenue and second for forecasting the energy consumption. Also, green buildings can be monitored and managed by using applications in the cloud, for that matter city energy grids can be managed and optimized by using applications in the cloud coupled with Big Data.
Energy Markets
The energy markets are growing globally and cloud computing and Big Data analytics today plays an instrumental role in helping traders make informed decisions in the market related to risks and making profits from energy trading.
The future of clean, efficient and cost effective energy is being driven by the use of Big Data and cloud computing.
Cloud is by itself a significant contributor to green energy and contributes in reducing the carbon footprint of the world.
With Cloud and Big Data we can take it to the next level by providing clean, renewable and cost effective energy for the planet.
This was published in Information Week - India.
http://www.informationweek.in/software/13-05-03/how_big_data_can_help_in_meeting_big_energy_needs.aspx
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