Renewable energy technologies are quickly gaining acceptance globally as a reliable source of electricity. Total global renewable energy installations have increased from 160 GW in 2004 to more than 1,560 GW in 2013. With a growing installed capacity of renewable energy plants comes a growing number of remote monitoring solutions to track the performance of these plants. Enormous amounts of data are being generated by these renewable energy plants and it is becoming ever important to create valuable insights from this data. Big data analytics performed on the data collected from these plants, enables owners and O&M crews to operate the renewable plants at the plants maximum potential. Among all the types of big data analytics that could be performed on the plant data, predictive analytics holds the most promising of providing insights by leveraging performance data to create correlations and outcomes. Let us understand how it could impact on Renewable Energy Industry.
“Data is not Information, Information is not Knowledge and Knowledge is not Wisdom”.
There are multiple steps required to reach from data collection to generating actionable insights. Predictive analytics is the link in this chain that takes us from the ‘Information’ stage to ‘knowledge’ stage. It models the cause and effect relationship among various parameters using various data mining techniques, statistical models and machine learning techniques thus allowing us a window to see the contextual future events.
According to a study, 20% – 40% of renewable energy cannot be used because it is unstable.
Predictive analytics when used deftly on renewable energy power plants can provide accurate energy production forecasts. It also predicts the machine breakdowns or glitches thereby optimizing overall operational efficiencies. The analytics checks for the correlation of various parameters like irradiation, wind speed, temperature, humidity, cloud cover, transformer status etc. and learns their cause and effect relationship. One study estimates that a good predictive model can increase the power generating capacity of a wind farm by about 10%, which practically revitalizes the entire business. It is also important to note that Predictive Analytics doesn’t only improve operational efficiencies but also improves the life span of the valuable renewable energy technology assets.
Banks are yet to consider renewable energy projects as a sound investment compared to oil and gas power projects.
The current growth of renewable energy technologies could be amplified if there is enough data to prove that they are credible investment options. Numerous renewable energy power projects still lack appropriate funding because of the lack of historic data that raises suspicions on the long term viability of the projects. Predictive analytics can addresses this problem by accurately forecasting energy generation based on historic performance, weather and other parameters. These quantifiable results associated with revenues generated from the future performance can improve the bankability of renewable energy projects.
Ultimately, predictive analytics is set to provide immense value to the renewable energy industry. It is now up to the plants owners to capitalize on this statistical tool to achieve the most out of their renewable energy power plant.
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