Additive models and robust aggregation for GEFCom2014 probabilistic electric load and electricity price forecasting
We summarize the methodology of the team Tololo, which ranked first in the load forecasting
and price forecasting tracks of the Global Energy Forecasting Competition 2014. During the …
and price forecasting tracks of the Global Energy Forecasting Competition 2014. During the …
Adaptive methods for short-term electricity load forecasting during COVID-19 lockdown in France
The coronavirus disease 2019 (COVID-19) pandemic has urged many governments in the
world to enforce a strict lockdown where all nonessential businesses are closed and citizens …
world to enforce a strict lockdown where all nonessential businesses are closed and citizens …
Bayesian inference for structured additive regression models for large-scale problems with applications to medical imaging
P Schmidt - 2017 - edoc.ub.uni-muenchen.de
In der angewandten Statistik können Regressionsmodelle mit hochdimensionalen
Koeffizienten auftreten, die sich nicht mit gewöhnlichen Computersystemen schätzen …
Koeffizienten auftreten, die sich nicht mit gewöhnlichen Computersystemen schätzen …
Forecasting hourly global horizontal solar irradiance in South Africa using machine learning models
Solar irradiance forecasting is essential in renewable energy grids amongst others for back-
up programming, operational planning, and short-term power purchases. This study focuses …
up programming, operational planning, and short-term power purchases. This study focuses …
[HTML][HTML] Operational thermal load forecasting in district heating networks using machine learning and expert advice
D Geysen, O De Somer, C Johansson, J Brage… - Energy and …, 2018 - Elsevier
Forecasting thermal load is a key component for the majority of optimization solutions for
controlling district heating and cooling systems. Recent studies have analysed the results of …
controlling district heating and cooling systems. Recent studies have analysed the results of …
Potential of three variant machine-learning models for forecasting district level medium-term and long-term energy demand in smart grid environment
T Ahmad, H Chen - Energy, 2018 - Elsevier
Medium-term and long-term energy prediction is essential for the planning and operations of
the smart grid eco-system. The prediction of next year and next month energy demand of …
the smart grid eco-system. The prediction of next year and next month energy demand of …
Improving short term load forecast accuracy via combining sister forecasts
Although combining forecasts is well-known to be an effective approach to improving
forecast accuracy, the literature and case studies on combining electric load forecasts are …
forecast accuracy, the literature and case studies on combining electric load forecasts are …
Effective sparse adaboost method with ESN and FOA for industrial electricity consumption forecasting in China
Accurate electricity consumption forecasting is a challenging task for its unstable behavior
and influence mechanism based on multiple factors. In this study, a neural network …
and influence mechanism based on multiple factors. In this study, a neural network …
Aggregation of multi-scale experts for bottom-up load forecasting
B Goehry, Y Goude, P Massart… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
The development of smart grid and new advanced metering infrastructures induces new
opportunities and challenges for utilities. Exploiting smart meters information for forecasting …
opportunities and challenges for utilities. Exploiting smart meters information for forecasting …
[HTML][HTML] Multivariate probabilistic crps learning with an application to day-ahead electricity prices
J Berrisch, F Ziel - International Journal of Forecasting, 2024 - Elsevier
This paper presents a new method for combining (or aggregating or ensembling)
multivariate probabilistic forecasts, considering dependencies between quantiles and …
multivariate probabilistic forecasts, considering dependencies between quantiles and …