What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Ecology
Henrik Madsen focuses on Wind power, Mathematical optimization, Meteorology, Econometrics and Estimation theory.
He has researched Wind power in several fields, including Probabilistic logic, Probabilistic forecasting and Electricity.
Many of his studies on Mathematical optimization apply to Measure as well.
His work carried out in the field of Meteorology brings together such families of science as Climatology, Statistical model and Identification.
Henrik Madsen has included themes like Sensitivity analysis, Uncertainty analysis and Bayes estimator, Estimator in his Econometrics study.
His Estimation theory research includes elements of Stochastic differential equation, Statistical hypothesis testing, Calibration and Hydrological modelling.
His most cited work include:
- Automatic calibration of a conceptual rainfall-runoff model using multiple objectives. (579 citations)
- Online short-term solar power forecasting (530 citations)
- A review on the young history of the wind power short-term prediction (484 citations)
What are the main themes of his work throughout his whole career to date?
Henrik Madsen mostly deals with Mathematical optimization, Meteorology, Wind power, Econometrics and Stochastic differential equation.
Mathematical optimization is closely attributed to Estimation theory in his study.
His study on Meteorology is mostly dedicated to connecting different topics, such as Climatology.
His Climatology research is multidisciplinary, relying on both Climate model and Precipitation.
His Wind power study focuses on Offshore wind power in particular.
Stochastic differential equation is a subfield of Applied mathematics that Henrik Madsen investigates.
He most often published in these fields:
- Mathematical optimization (12.36%)
- Meteorology (11.25%)
- Wind power (10.44%)
What were the highlights of his more recent work (between 2017-2021)?
- Model predictive control (7.19%)
- Electricity (5.78%)
- Flexibility (2.94%)
In recent papers he was focusing on the following fields of study:
Henrik Madsen mainly focuses on Model predictive control, Electricity, Flexibility, Mathematical optimization and Renewable energy.
Henrik Madsen focuses mostly in the field of Model predictive control, narrowing it down to matters related to Control theory and, in some cases, Stochastic differential equation and Ventilation.
His Stochastic differential equation study incorporates themes from Estimation theory and Extended Kalman filter.
In his study, which falls under the umbrella issue of Electricity, Stochastic programming is strongly linked to Environmental economics.
His Flexibility research includes themes of Electric power system, Grid, Smart grid, Heat pump and Demand response.
His Mathematical optimization research incorporates themes from Production and Control.
Between 2017 and 2021, his most popular works were:
- Characterizing the energy flexibility of buildings and districts (123 citations)
- Wind Power Forecasting Based on Echo State Networks and Long Short-Term Memory (50 citations)
- Model predictive control of urban drainage systems: A review and perspective towards smart real-time water management (31 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Ecology
His scientific interests lie mostly in Model predictive control, Electricity, Flexibility, Renewable energy and Bidding.
His studies in Model predictive control integrate themes in fields like Aeration, Activated sludge model and Control theory, Control theory.
The study incorporates disciplines such as Stochastic differential equation, Artificial pancreas, Ventilation and Estimation theory in addition to Control theory.
The concepts of his Flexibility study are interwoven with issues in Electric power system, Control, Smart grid, Electricity generation and Demand response.
His Electricity market study combines topics in areas such as Economic model, Mathematical optimization, Mathematical model, Nonlinear modelling and Operational planning.
His Mathematical optimization research is multidisciplinary, incorporating elements of Variety, Drainage and Implementation.
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