Explainability and Interpretability in Electric Load Forecasting Using Machine Learning Techniques – A Review
Electric Load Forecasting (ELF) is the central instrument for planning and controlling demand response programs, electricity trading, and consumption optimization. Due to the increasing automation of these processes, meaningful and transparent forecasts become more and more important. Still, at the...
Main Authors: | Lukas Baur, Konstantin Ditschuneit, Maximilian Schambach, Can Kaymakci, Thomas Wollmann, Alexander Sauer |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier
2024-05-01
|
Series: | Energy and AI |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S2666546824000247 |
Similar Items
-
Explainability-based Trust Algorithm for electricity price forecasting models
by: Leena Heistrene, et al.
Published: (2023-10-01) -
Justifying Short-Term Load Forecasts Obtained with the Use of Neural Models
by: Tadeusz A. Grzeszczyk, et al.
Published: (2022-03-01) -
Application of explainable artificial intelligence in medical health: A systematic review of interpretability methods
by: Shahab S Band, et al.
Published: (2023-01-01) -
Explainable multi-step heating load forecasting: Using SHAP values and temporal attention mechanisms for enhanced interpretability
by: Alexander Neubauer, et al.
Published: (2025-05-01) -
A systematic review on the integration of explainable artificial intelligence in intrusion detection systems to enhancing transparency and interpretability in cybersecurity
by: Vincent Zibi Mohale, et al.
Published: (2025-01-01)