PV Magazine Publishes QOS Energy’s Insights about Data Science Applied to Solar PV Industry
20 September 2018
Decision-Making Through Data
According to a study conducted by Massachusetts Institute of Technology, companies that use data-driven decision-making are 5% more productive and profitable than their competitors.
Jean-Yves Bellet, cofounder and VP-CTO of QOS Energy asks, “Can we apply the results of this study to solar PV operations and maintenance?” Today, solar plant owners expect to generate value from their data using real-time decision-making tools, analytics, and machine learning models.
Operations teams make decisions based on data that they receive from monitoring providers, SCADA systems, dataloggers, inverters, satellite irradiation data, weather forecast services, and other sources. Analysts use a variety of benchmarking tools to compare current performance with historical data, or that of other plants, in order to identify trends and maximize the performance of these assets.
Data quality is critical for reliable renewable asset operations and effective decision-making. […]
“Evaluating the quality and integrity of all of these data is a crucial step in building a strong foundation for reliable asset performance analytics. ”
For more than a decade, QOS Energy has been helping renewable asset owners and operators to address problems arising from the complexity of asset performance data acquisition, by offering a hardware agnostic, cloud-based asset performance management solution. […]
But data acquisition is only the first step of the process to sort, qualify, and transform these data into operational insights. […]
Solar asset operators and owners now demand more from analytical toolsets than simply evaluating the operational status of their renewable portfolios via real-time and historical benchmarks. […]
Data science technologies, such as machine learning, can facilitate the management of such complexity by delivering accurate, reliable, and actionable insights. Today, combining data analytics and machine learning enables operators to reduce downtime, increase benchmark reliability, and better forecast energy productions.
Case studies and applications are numerous. Data science models such as the Digital Twin, a virtual replica of a plant working at top performance, enable the creation of accurate benchmarks and thereby assess precisely if a plant is operating as planned or is underperforming. […]
Our goal is to deliver turnkey analytical tools which can be used by our client without having to be an expert in data science.
About the Author
Jean Yves Bellet
Jean-Yves Bellet is co-founder and VP-CTO of QOS Energy. He has extensive experience in monitoring systems and software design and is the functional designer of the Qantum® platform which monitors 8 GW of renewable power.