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A Pyramid Approach to AWS Data Analytics Overview Nowadays we are producing data every second. According to Domo, “ Data never sleeps .” There are  2.5 quintillion bytes of data  created each day; however, these values are changing rapidly day by day. I would like to emphasise it is more exponential growth than linear that brings a new challenge and new business strategies in the tech world, where the largest organizations will be engaged in finding an analytics data solution. In my  previous blog  I explained how to collect and transform data located in different AWS accounts and regions. Often, however, the enterprise company has data everywhere, not only in the cloud. One of the most common questions that I get from customers is: Which AWS services fit well with my requirements to improve my experience with data analytics?  So here I would like to show you the data model represented in a Pyramid that may be useful and provide the results you’re seeking. I would like to call it the A

Advanced PV Performance Modelling Based on Different Levels of Irradiance Data Accuracy by Julián Ascencio-Vásquez, Jakob Bevc, Kristjan Reba, Kristijan Brecl, Marko Jankovec  and Marko Topič Faculty of Electrical Engineering, University of Ljubljana, Tržaška cesta 25, 1000 Ljubljana, Slovenia Faculty of Computer and Information Science, University of Ljubljana, Večna pot 113, 1000 Ljubljana, Slovenia Published: 1 May 2020 (This article belongs to the Special Issue Applications of Artificial Intelligence in Renewable Energy) Abstract In photovoltaic (PV) systems, energy yield is one of the essential pieces of information to the stakeholders (grid operators, maintenance operators, financial units, etc.). The amount of energy produced by a photovoltaic system in a specific time period depends on the weather conditions, including snow and dust, the actual PV modules’ and inverters’ efficiency and balance-of-system losses. The energy yield can be estimated by using empirical models with ac

Advanced PV Performance Modelling Based on Different Levels of Irradiance Data Accuracy by Julián Ascencio-Vásquez 1,*OrcID,Jakob Bevc 2,Kristjan Reba 2,Kristijan Brecl 1OrcID,Marko Jankovec 1 andMarko Topič  Faculty of Electrical Engineering, University of Ljubljana, Tržaška cesta 25, 1000 Ljubljana, Slovenia Faculty of Computer and Information Science, University of Ljubljana, Večna pot 113, 1000 Ljubljana, Slovenia Published: 1 May 2020 Abstract In photovoltaic (PV) systems, energy yield is one of the essential pieces of information to the stakeholders (grid operators, maintenance operators, financial units, etc.). The amount of energy produced by a photovoltaic system in a specific time period depends on the weather conditions, including snow and dust, the actual PV modules’ and inverters’ efficiency and balance-of-system losses. The energy yield can be estimated by using empirical models with accurate input data. However, most of the PV systems do not include on-site high-class me