Forecasting methods for facilitating the integration of solar power in buildings and energy systems are presented in a new doctoral thesis
2021-01-27
Dennis van der Meer at the Division of Civil Engineering and Built Environment defended his doctoral thesis on solar forecasting on 22 January. The thesis presents forecasting methods for solar power generation and how they can be applied to control energy systems in buildings and local electricity networks, which is important for managing future large amounts of solar power.
In his PhD project, Dennis has developed and analyzed probabilistic forecasting models for solar power generation and electricity use in buildings. Probabilistic forecasts provide a probability distribution for the forecasted variable, as opposed to deterministic forecasts that provide a point value. Among other things, the studies have shown that forecasts for even a small number of buildings are more reliable than forecasts for individual buildings, which can have practical significance for how forecasts are designed and applied for buildings and urban areas. The dissertation also presents a methodology for multivariate forecasts in both time and space, which is important for controlling several geographically dispersed systems.
The opponent of the thesis was Professor Pierre Pinson from the Technical University of Denmark (DTU), a leading international expert in forecasting and editor-in-chief of the peer-reviewed International Journal of Forecasting. The grading committee consisted of Associate Professor Jan Kloppenborg Møller, DTU, Dr Bengt Stridh, Mälardalen University, and professor Anna Rutgersson, Uppsala University.
The thesis, titled Spatio-temporal forecasting and optimization for integration of solar energy in urban energy systems, can be viewed here.
News
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Forecasting methods for facilitating the integration of solar power in buildings and energy systems are presented in a new doctoral thesis
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Dennis van der Meer at the Division of Civil Engineering and Built Environment defended his doctoral thesis on solar forecasting on 22 January. The thesis presents forecasting methods for solar power generation and how they can be applied to control energy systems in buildings and local electricity networks, which is important for managing future large amounts of solar power.
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