Data-Driven Approach for User Behavior Forecast and Visualization

Aim

This project proposes a data driven method to discover user behavior patterns, predict demand under given conditions and identify its relevant influence factors within the residential energy usage domain. In order to make our models transparent, we apply visual analytics and explainable artificial intelligence to visualize how the models make decision.

Research questions

The aim is to answer the following main research questions for a case study on (smart) buildings and energy demand behavior therein:

  • What user behavior patterns changes after time goes?
  • What are user behavior differences between different countries/regions?
  • What kinds of user behavior groups are?
  • What data driven methods can be used to predict energy demands based on user behavior?
  • What (indirect) impact factors for user energy demands are?
  • What role do technical and social innovations (e.g. demand response systems, PV, EV, innovative tariff strategies) play in influencing behavior patterns towards long-term societal goals, e.g. for energy system transformation?

These research questions will be answered by collecting unstructured data from several sources (e.g. open questions in socio-economic surveys, social media, technology reports, webpages, etc.) together with structured datasets (e.g. smart meter data, structured answers in socio-economic survey, climate data and other public databases).
 

Expected outcomes

  • Database: Energy Consumption Behavior Database in Residential Buildings
  • Workflow/Model: Load Forecast and User Behavior Visualization
  • Working Paper: Explainable Deep Attention Model for Residential Energy Demand Forecast with User Profile Learning
  • Master Thesis: Analysis for User Behaviors of Residential Energy Demand based on Smart Meter Data
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Analysis Framework