Huge volumes of data are collected in the energy sector continuously. Today, we have sensors, network communication, wireless networks, cloud computing and so much more, this generates tremendous volume of data on the supply and demand side.
With the SMART Grid coming into foray, the amount of data is only going to increase at a staggering rate.
To give you an idea of the amount of data that will be generated: 1 million households and companies using SMART meters collect 2920 Terabytes of data in 1 year. Integrating other intelligent devices like thermostats and sensors will allow for outwards expansion.
The problem in the energy and utilities industry was that the data wasn’t put to use till now. Companies are now making efficient use of Big Data technologies and solutions.
Big data comes from 4 major sources – SMART meters, asset management data, third party data and grid equipment.
Let’s dive in to how the energy and utilities industry is making use of big data and big data analytics to improve operational efficiency, reduce emissions and drive down costs.
Power generation optimization and planning can be done via big data analytics. When it comes to power generation, economic load dispatch and planning are the most critical decision-making processes.
Economic load dispatch is a term that means matching power supply with the demand for energy for a short-term period.
This is done bearing in mind the distribution and transmission constraints at the lowest possible cost.
Big data analytics plays a huge role in matching supply and demand effectively.
Leveraging big data and big data analytics techniques, efficiency in energy production can be improved and a reduction in production costs can be achieved.
Big data analytics can prove to be really advantageous to another grid component which is renewable energy.
On the SMART Grid, the two main sources of renewable energy are wind and solar energy.
Weather conditions and other external factors sure do affect their output with respect to power generation.
Weather data analysis allows for accurate and efficient forecasting of renewable energy power generation.
Forecasting comprises of multiple factors like consumption data, weather data, GIS data and energy production.
Analysis of all these factors dictates the geographical placement of these devices as well.
GIS data helps with spatial planning by making use of satellite imagery of the geography.
This is done by topography analysis, calculation of distance from water bodies, radiation zones etc.
Being asset intensive defines the energy and utilities industry. Asset management challenges are often faced by this industry.
These may include, inventory management, procurement monitoring, asset retirement monitoring and maintenance. Big data analytics can help improve efficiency of asset management.
“You can’t manage, what you can’t measure”
The task of managing energy consumption is automated nowadays, thanks to big data analytics services that makes the lives of energy managers easier.
If you look at the advances made on the sustainable energy front, it has all been on the demand side.
Energy efficient electrical devices are now used which reduce the power requirements.
Curbing global greenhouse emissions can be done by being energy efficient. Top priority for companies today is reduction in energy costs and sustainable energy usage.
Big data analytics allows them to do this. Data obtained from asset operations, business policies, weather data, SMART meters, production costs etc, once integrated can be analysed over a long period of time, thereby discovering hidden patterns in usage, demand, supply and costs.
Apart from this, issues like efficiency of equipment, operational lags and other issues can be solved. Energy consumption and savings can also be forecasted.
To achieve this objective, many energy management software solutions are there in the market today.
These solution systems encompass data feeds that are automated to collect data from SMART meters and other data sources.
The software then integrates all the data, analyses it and shows energy consumption patterns. Big data analytics also helps identifying projects which save maximum energy and money.
The results obtained from these systems is real-time. They help provide energy managers a tool to identify and rectify energy issues.
Cheaper energy solutions combined with big data and analytics is the path to free energy.
Matching energy supply and demand is extremely crucial and is the way to sustainable energy being a permanent solution.
Giving back unused power to the grid is what we should all be looking to do, and virtual power stations may just be the answer to that.
It is a relatively new technology which operates energy storage devices in a centralized location.
Free energy will still take quite some time to achieve. However, big data and analytics are rapidly reducing the cost of power generation and consumption.
By Uma Raj
By Uma Raj
By Abishek Balakumar
Abhimanyu is a sportsman, an avid reader with a massive interest in sports. He is passionate about digital marketing and loves discussions about Big Data.