29 citations · 104 across the 11 of their papers we have counts for
13 papers
onlineforecast: An R package for adaptive and recursive forecasting
Peder Bacher, Hjörleifur G. Bergsteinsson, Linde Frölke +6
Systems that rely on forecasts to make decisions, e.g. control or energy trading systems, require frequent updates of the forecasts. Usually, the forecasts are updated whenever new…
Control of heat pumps with CO2 emission intensity forecasts
Kenneth Leerbeck, Peder Bacher, Rune Grønborg +3
An optimized heat pump control for building heating was developed for minimizing CO2 emissions from related electrical power generation. The control is using weather and CO2 emissi…
Short-Term Forecasting of CO2 Emission Intensity in Power Grids by Machine Learning
Kenneth Leerbeck, Peder Bacher, Rune Junker +5
A machine learning algorithm is developed to forecast the CO2 emission intensities in electrical power grids in the Danish bidding zone DK2, distinguishing between average and marg…
Stochastic Modelling of the Flow-Front Evolution in a Vacuum Assisted Resin Transfer Moulding Process with Missing Data
Michael Nauheimer, Rishi Relan, Uffe Høgsbro Thygesen +2
The real-time fault monitoring and control of the Vacuum Assisted Resin Transfer Moulding (VARTM) production process requires a knowledge of the position of the epoxy flow-front in…
Operational planning and bidding for district heating systems with uncertain renewable energy production
Ignacio Blanco, Daniela Guericke, Anders N. Andersen +1
In countries with an extended use of district heating (DH), the integrated operation of DH and power systems can increase the flexibility of the power system achieving a higher int…
A two-phase stochastic programming approach to biomass supply planning for combined heat and power plants
Ignacio Blanco, Daniela Guericke, Juan M. Morales +1
Due to the new carbon neutral policies, many district heating operators start operating their combined heat and power (CHP) plants using different types of biomass instead of fossi…
A novel bidding method for combined heat and power units in district heating systems
Ignacio Blanco, Anders N. Andersen, Daniela Guericke +1
We propose a bidding method for the participation of combined heat and power (CHP) units in the day-ahead electricity market. More specifically, we consider a district heating syst…
Spatio-Temporal Forecasting by Coupled Stochastic Differential Equations: Applications to Solar Power
Emil B. Iversen, Rune Juhl, Jan K. Møller +3
Spatio-temporal problems exist in many areas of knowledge and disciplines ranging from biology to engineering and physics. However, solution strategies based on classical statistic…
ctsmr - Continuous Time Stochastic Modeling in R
Rune Juhl, Jan Kloppenborg Møller, Henrik Madsen
ctsmr is an R package providing a general framework for identifying and estimating partially observed continuous-discrete time gray-box models. The estimation is based on maximum l…
Commitment and Dispatch of Heat and Power Units via Affinely Adjustable Robust Optimization
Marco Zugno, Juan M. Morales, Henrik Madsen
The joint management of heat and power systems is believed to be key to the integration of renewables into energy systems with a large penetration of district heating. Determining…
A Data-driven Bidding Model for a Cluster of Price-responsive Consumers of Electricity
Javier Saez-Gallego, Juan M. Morales, Marco Zugno +1
This paper deals with the market-bidding problem of a cluster of price-responsive consumers of electricity. We develop an inverse optimization scheme that, recast as a bilevel prog…
Optimal Charging of an Electric Vehicle using a Markov Decision Process
Emil B. Iversen, Juan M. Morales, Henrik Madsen
The combination of electric vehicles (EVs) and renewable energy is taking shape as a potential driver for a future free of fossil fuels. However, the efficient management of the EV…
Probabilistic Forecasts of Solar Irradiance by Stochastic Differential Equations
Emil B. Iversen, Juan M. Morales, Jan K. Møller +1
Probabilistic forecasts of renewable energy production provide users with valuable information about the uncertainty associated with the expected generation. Current state-of-the-a…