An Interpretable Probabilistic Model for Short-Term Solar Power Forecasting Using Natural Gradient Boosting

5 August 2021

Papers citing "An Interpretable Probabilistic Model for Short-Term Solar Power Forecasting Using Natural Gradient Boosting"

6 / 6 papers shown

Title
Why Reinforcement Learning in Energy Systems Needs Explanations Hallah Shahid Butt Benjamin Schäfer 28 1 0 29 May 2024
Improving the Accuracy and Interpretability of Neural Networks for Wind Power Forecasting Wenlong Liao F. Porté-Agel Jiannong Fang Birgitte Bak-Jensen Zhe Yang Gonghao Zhang AI4CE 11 1 0 25 Dec 2023
An Explainable Framework for Machine learning-Based Reactive Power Optimization of Distribution Network Wenlong Liao Benjamin Schäfer Dalin Qin Gonghao Zhang Zhixian Wang Zhe Yang FAtt 11 0 0 07 Nov 2023
Quantifying and Explaining Machine Learning Uncertainty in Predictive Process Monitoring: An Operations Research Perspective Nijat Mehdiyev Maxim Majlatow Peter Fettke 38 12 0 13 Apr 2023
Understanding electricity prices beyond the merit order principle using explainable AI Julius Trebbien L. R. Gorjão Aaron Praktiknjo B. Schäfer D. Witthaut 21 25 0 09 Dec 2022
Boosted Ensemble Learning based on Randomized NNs for Time Series Forecasting Grzegorz Dudek AI4TS 14 1 0 02 Mar 2022