PV
P.E.M. Van Santvliet
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1
Control and Software
Distribution of the electricity grid of a tiny house community
This thesis shows the detailed design of the control and software of a DC microgrid of a tiny house community on the roof of a high-rise building in the city of Rotterdam in the Netherlands, consisting of twelve tiny houses powered by solar and wind energy. This thesis is part of a project with two other subgroups, focusing on the microgrid design and the powerline communication.
First, an introduction to the problem is given together with a description of the tiny house community. After that, the general program of requirements is presented, as well as the requirements of this subgroup. Next, an artificial neural network design is presented, which is used to forecast solar and wind generation and energy demand. The designed dense neural network resulted in predictions with mean errors of 10.11%, 12.56%, and 6.95% as a fraction of the maximum value for solar generation, wind generation, and energy demand, respectively. The predictions functioned as an input for the model predictive controller, which used them to place restrictions on appliances in the community when necessary, to reduce dependency on the main power grid of Rotterdam. Using a mathematical optimization algorithm, a simulation of one year showed that the controller could reduce the grid dependency up to 25%, compared to simulating without the controller. The conclusion summarises the achieved results, discusses whether the requirements are met, and considers possible future works. ...
First, an introduction to the problem is given together with a description of the tiny house community. After that, the general program of requirements is presented, as well as the requirements of this subgroup. Next, an artificial neural network design is presented, which is used to forecast solar and wind generation and energy demand. The designed dense neural network resulted in predictions with mean errors of 10.11%, 12.56%, and 6.95% as a fraction of the maximum value for solar generation, wind generation, and energy demand, respectively. The predictions functioned as an input for the model predictive controller, which used them to place restrictions on appliances in the community when necessary, to reduce dependency on the main power grid of Rotterdam. Using a mathematical optimization algorithm, a simulation of one year showed that the controller could reduce the grid dependency up to 25%, compared to simulating without the controller. The conclusion summarises the achieved results, discusses whether the requirements are met, and considers possible future works. ...
This thesis shows the detailed design of the control and software of a DC microgrid of a tiny house community on the roof of a high-rise building in the city of Rotterdam in the Netherlands, consisting of twelve tiny houses powered by solar and wind energy. This thesis is part of a project with two other subgroups, focusing on the microgrid design and the powerline communication.
First, an introduction to the problem is given together with a description of the tiny house community. After that, the general program of requirements is presented, as well as the requirements of this subgroup. Next, an artificial neural network design is presented, which is used to forecast solar and wind generation and energy demand. The designed dense neural network resulted in predictions with mean errors of 10.11%, 12.56%, and 6.95% as a fraction of the maximum value for solar generation, wind generation, and energy demand, respectively. The predictions functioned as an input for the model predictive controller, which used them to place restrictions on appliances in the community when necessary, to reduce dependency on the main power grid of Rotterdam. Using a mathematical optimization algorithm, a simulation of one year showed that the controller could reduce the grid dependency up to 25%, compared to simulating without the controller. The conclusion summarises the achieved results, discusses whether the requirements are met, and considers possible future works.
First, an introduction to the problem is given together with a description of the tiny house community. After that, the general program of requirements is presented, as well as the requirements of this subgroup. Next, an artificial neural network design is presented, which is used to forecast solar and wind generation and energy demand. The designed dense neural network resulted in predictions with mean errors of 10.11%, 12.56%, and 6.95% as a fraction of the maximum value for solar generation, wind generation, and energy demand, respectively. The predictions functioned as an input for the model predictive controller, which used them to place restrictions on appliances in the community when necessary, to reduce dependency on the main power grid of Rotterdam. Using a mathematical optimization algorithm, a simulation of one year showed that the controller could reduce the grid dependency up to 25%, compared to simulating without the controller. The conclusion summarises the achieved results, discusses whether the requirements are met, and considers possible future works.
tunus - tiny house project
An interdisciplinary approach to architecture
Student report
(2021)
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Wim Landuyt, Korneel Somers, Onno Van de Sype, Pieter Van Santvliet, S. Zijlstra, J. Hellendoorn
A tiny house is a building for permanent human habitation that is specifically designed to have a limited ground surface. The tiny house design discussed in this report has a strong focus on circularity, sustainable material usage, smart systems, and affordability. To achieve an overall self-regulating and ecological concept, the aim is to combine and optimise the different flows that go through the tiny house - i.e. electricity, waste, and water. These flows are also smartly integrated and made more efficient on a network scale. Several tunus tiny houses are combined in a village because sustainable living environments can be created more effectively when collaborating in communities. Eventually, the goal is to obtain a network with such flexibility that its principles can be implemented on any collection of tiny houses or even terraced houses and flats.
...
A tiny house is a building for permanent human habitation that is specifically designed to have a limited ground surface. The tiny house design discussed in this report has a strong focus on circularity, sustainable material usage, smart systems, and affordability. To achieve an overall self-regulating and ecological concept, the aim is to combine and optimise the different flows that go through the tiny house - i.e. electricity, waste, and water. These flows are also smartly integrated and made more efficient on a network scale. Several tunus tiny houses are combined in a village because sustainable living environments can be created more effectively when collaborating in communities. Eventually, the goal is to obtain a network with such flexibility that its principles can be implemented on any collection of tiny houses or even terraced houses and flats.