During 2022 we have seen a huge increase in electricity prices compared with previous years, given both the crisis in Europa and extreme weather during 2022. The record high electricity prices have been accompanied with an increasing difference between high and low prices.
Watts is a company developing digital solutions for the green energy system. By combining data from the usage of the with the users' electricity consumption and the electricity prices, we can investigate correlations between electricity price and electricity consumption, both on load shifting and reduction, and try to explain them by app usage data. The usage data can for example be time of app session and which information has been viewed. The main objective of the project is to describe the dependencies between price and consumption using data, and in that process be able to explain which behavioural factors have significant impacts.
Furthermore, we hypothesise that the individual consumers have different price elasticity, i.e., might also depend on the information obtained from the app usage. Can this be quantified?
Combine data on app usage of the users with their electricity consumption and price.
Investigate the hypothesis of correlation between app usage, electricity consumption and price.
Develop quantification metrics for price elasticity/responsiveness for individual users.
Analyse how increasing tariffs will affect the price elasticity in the coming years.
Statistics courses, preferably time series analysis (02417) and “advanced statistics” (either (02409) or (02418)).
You can use either R or Python.
Peder Bacher, DTU Compute, Dynamical Systems, email@example.com
Jon Liisberg, Watts A/S firstname.lastname@example.org
15 - 35
Bachelor Project or Master Project