activity
20162020
most citedSpatio-Temporal Forecasting by Coupled Stochastic Differential Equations: Applications to Solar Power

3 citations · 3 across the 1 of their papers we have counts for

collaborators

6 papers

eess.SY2020

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…

eess.SP2020

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…

math.OC2018

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…

math.OC2018

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…

stat.AP20173 cited

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…

stat.CO2016

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…