The aim of this course is to provide the PhD students with a background on the main aspects of data analysis to support their activities on energy systems. The course will describe the main techniques and concepts, with a methodological framework and case studies and examples of applications to different energy systems. The course will also cover how to communicate the results, with particular attention to data visualization. The students will learn how to apply the concepts to real cases by using open-source tools and resources, such as R and python.

A basic knowledge of energy systems is preferred

Introduction to data analysis: objectives, strategies, techniques, limitations. Data acquisition and manipulation, application to datasets related to energy monitoring and weather conditions. Descriptive analysis of a dataset, main statistical summaries, techniques to manage missing data and outliers, large datasets. Time series: discretization, gap filling, comparison of different time resolutions. Data visualization: types of charts, main rules, how to choose the right chart to convey the message of the analysis. Quick introduction to geospatial data and maps. Open data and FAIR data principles: data should be findable, accessible, interoperable and reusable. Case studies on energy systems of different types and at different scales. Dedicated session with application of data analysis to district heating systems to demonstrate the concepts and tools presented in the course.

In presenza

Presentazione orale - Presentazione report scritto

P.D.1-1 - Gennaio