7 citations · 28 across the 6 of their papers we have counts for
7 papers
Electricity Demand Forecasting in Future Grid States: A Digital Twin-Based Simulation Study
Daniel R. Bayer, Felix Haag, Marco Pruckner +1
Short-term forecasting of residential electricity demand is an important task for utilities. Yet, many small and medium-sized utilities still use simple forecasting approaches such…
Overcoming Anchoring Bias: The Potential of AI and XAI-based Decision Support
Felix Haag, Carlo Stingl, Katrin Zerfass +2
Information systems (IS) are frequently designed to leverage the negative effect of anchoring bias to influence individuals' decision-making (e.g., by manipulating purchase decisio…
Meta-Regression Analysis of Errors in Short-Term Electricity Load Forecasting
Konstantin Hopf, Hannah Hartstang, Thorsten Staake
Forecasting electricity demand plays a critical role in ensuring reliable and cost-efficient operation of the electricity supply. With the global transition to distributed renewabl…
Short-Term Electricity Load Forecasting Using the Temporal Fusion Transformer: Effect of Grid Hierarchies and Data Sources
Elena Giacomazzi, Felix Haag, Konstantin Hopf
Recent developments related to the energy transition pose particular challenges for distribution grids. Hence, precise load forecasts become more and more important for effective g…
Explainable AI for tailored electricity consumption feedback -- an experimental evaluation of visualizations
Jacqueline Wastensteiner, Tobias M. Weiss, Felix Haag +1
Machine learning (ML) methods can effectively analyse data, recognize patterns in them, and make high-quality predictions. Good predictions usually come along with "black-box" mode…
Augmented cross-selling through explainable AI -- a case from energy retailing
Felix Haag, Konstantin Hopf, Pedro Menelau Vasconcelos +1
The advance of Machine Learning (ML) has led to a strong interest in this technology to support decision making. While complex ML models provide predictions that are often more acc…
Filter Methods for Feature Selection in Supervised Machine Learning Applications -- Review and Benchmark
Konstantin Hopf, Sascha Reifenrath
The amount of data for machine learning (ML) applications is constantly growing. Not only the number of observations, especially the number of measured variables (features) increas…