Search results for "550"
showing 10 items of 1192 documents
Intelligent estimation of the wake losses in wind farms : Artificial neural network estimation of the power of a wind farm considering the effect of …
2018
Master's thesis Renewable Energy ENE500 - University of Agder 2018 The transition from non-renewable to renewable energy production requires a detailed optimization and quantification of the generated power. The loss of power due to wake effect is a common problem for wind farms. The wake effect is the reduction of velocity and increase of turbulence in the wind flow downstream from a wind turbine. The wake effect is a complex multivariable phenomenon and its understanding iscapital forappropriate estimations of the power of a wind field and its turbines.This thesis builds an artificial neural network based on machine learning to model the performance of a single wind farm owned by WEICAN (…
System modeling and dispatch schedule optimization of combined PV battery system using linear optimization
2021
Master's thesis in Renewable energy (ENE500) Energy storage plays a vital role in paving the way for more renewable penetration. The technology is costly, but intelligent solutions regarding dispatch strategies and system design can help reduce the total cost over the projected lifetime of a system. For this thesis, a customizable linear programming algorithm is created within Python to optimize the battery energy scheduling based on generated PV power, electricity cost and load demand. The commercial system optimization tool HOMER is used to verify the code by running simulations based on historic data collected from Nord Pool and UiAs own photovoltaic system. One benefit of the custom mad…
Optimization of Power Supply for Hydrology-and Meteorology-stations : Optimization of power supply for off-grid hydrology- and meteorology-stationsus…
2019
Master's thesis Renewable Energy ENE500 - University of Agder 2019 This thesis consider a case study of a PV/wind/battery hybrid energy system installed at Scanmatic ASheadquarters in Arendal, Norway. The energy system is a stand-alone and off-grid hybrid system. It consistof four PV panels of 20 W with different tilt and orientation, a wind turbine of 300 W and a battery of1.4 kWh. This work consider machine learning and artificial intelligence in Python 3 for prediction andoptimization. Machine learning is used to predict the power production from the system components basedon the weather data at site. Artificial intelligence is used to optimize the system size based on cost and theabilit…
A novel learning automata game with local feedback for parallel optimization of hydropower production
2017
Master's thesis Information- and communication technology IKT590 - University of Agder 2017 Hydropower optimization for multi-reservoir systems is classi ed as a combinatorial optimization problem with large state-space that is particularly di cult to solve. There exist no golden standard when solving such problems, and many proposed algorithms are domain speci c. The literature describes several di erent techniques where linear programming approaches are extensively discussed, but tends to succumb to the curse of dimensionality problem when the state vector dimensions increase. This thesis introduces LA LCS, a novel learning automata algorithm that utilizes a parallel form of local feedbac…
Virtual reality applications for higher education: Design elements, lessons learned, and research agenda
2019
Advances in Deep Learning Towards Fire Emergency Application : Novel Architectures, Techniques and Applications of Neural Networks
2020
Paper IV is not published yet. With respect to copyright paper IV and paper VI was excluded from the dissertation. Deep Learning has been successfully used in various applications, and recently, there has been an increasing interest in applying deep learning in emergency management. However, there are still many significant challenges that limit the use of deep learning in the latter application domain. In this thesis, we address some of these challenges and propose novel deep learning methods and architectures. The challenges we address fall in these three areas of emergency management: Detection of the emergency (fire), Analysis of the situation without human intervention and finally Evac…
Supporting Effective Online Learning Groups for eLearning Systems
2020
Learning in groups has been advocated to increase learning based on the social constructivist learning theory. ICT has been preferred to bridge the gap between distance learning students for possibilities to enhance the benefits of learning groups. However, although learning groups can bring about meaningful learning, learning groups in online environments are often not working. To solve this problem, this study uses design science approaches to establish methods and factors that support effective online learning groups. Within design science three case studies were used. These case studies were used under three research areas: context of online learning groups, processes to support effecti…
IT infrastruktur for tilstandsbasert vedlikehold
2012
Masteroppgave i mekatronikk MAS500 - Universitetet i Agder, 2012 Konfidensiell til / confidential until 01.07.2017
MapAI: Precision in BuildingSegmentation
2022
MapAI: Precision in Building Segmentation is a competition arranged with the Norwegian Artificial Intelligence Research Consortium (NORA) in collaboration with Centre for Artificial Intelligence Research at the University of Agder (CAIR), the Norwegian Mapping Authority, AI:Hub, Norkart, and the Danish Agency for Data Supply and Infrastructure. The competition will be held in the fall of 2022. It will be concluded at the Northern Lights Deep Learning conference focusing on the segmentation of buildings using aerial images and laser data. We propose two different tasks to segment buildings, where the first task can only utilize aerial images, while the second must use laser data (LiDAR) with…