Analytics to Drive Efficiency in Solar

Over the years, data analytics has been adopted in the solar industry for its significant benefits to asset owners in categorizing data and generating useful insights. While solar panels let us tap into almost inexhaustible power, data helps us do it efficiently and cost-effectively. Though solar power generation is on the rise, the unpredictable nature of sunlight often hampers the overall renewable energy production. Consequently, it becomes difficult for solar plants to operate at their maximum potential. Advancements in big data, predictive analytics, and machine learning is rapidly changing this scenario by helping the solar industry to predict weather conditions well in advance, allowing renewable plants to increase their production significantly. In these ways and more, big data and solar power make an ideal match that will help drive the renewable sector forward. 

 

One of the biggest applications of data analytics in the solar industry is the streamlining of operation and maintenance processes, further optimizing the energy production and its distribution. Solar panels are geographically dispersed and often installed by thousands at remote sites. In essence, ground-level operation and maintenance of this physical infrastructure spread across a vast terrain has become a tough balancing act, affecting the daily energy output of the plant. Big data analytics provides the means to streamline their O&M processes to a great extent. Many analytics tools have been proven beneficial for running day-to-day- operations of various solar plants. To mention one, Virtual Irradiance (VI), a solar management program based on big data, which collects ground-level irradiance or sunlight intensity data accurately, eliminates the need for expensive on-site sensors.

 

With the ability to send a signal whenever solar panels underperform or over perform, this software can help verify how well solar panels perform under varying weather conditions compared to their ratings. With this condition monitoring enabled, the ground crew can quickly locate the problem and take appropriate action while considerably reducing operations and maintenance costs. Proper O&M gives rise to better-connected smart energy grids, which contribute to cost savings in their own way by providing real-time analysis during outages and making restorative service calls more accurate and quicker to carry out.

 

While cost is another most frequently cited driver of solar power adoption, data analytics is a savior as well. Big data and predictive analytics technology is allowing solar companies to produce more energy without adding additional infrastructure costs. The ability to extract useful information from big data is one of the reasons behind the gradual decline in energy prices. Instead of increasing the number of solar panels, the value is in increasing the efficiency of existing plant infrastructure. Another visible obstacle in this sector’s growth rate is quality data for financial organizations, banks, and insurance companies reconsidering renewable energy projects as credible investment options. The lack of reliable data supporting the long-term viability of a project is usually why most investors are hesitant to back an energy venture. Data analytics has made way for the companies to forecast energy generation based on past performance, weather, and other parameters accurately. It can also help determine the precise quantity of solar panels required to produce the desired output, making the project more bankable than it has been.

 

- Madhu Sudhan, VP Marketing, SenseHawk, Inc.