Fant 10469 publikasjoner. Viser side 199 av 419:
Machine Learning-Based Digital Twin for Predictive Modeling in Wind Turbines
Wind turbines are one of the primary sources of renewable energy, which leads to a sustainable and efficient energy solution. It does not release any carbon emissions to pollute our planet. The wind farms monitoring and power generation prediction is a complex problem due to the unpredictability of wind speed. Consequently, it limits the decision power of the management team to plan the energy consumption in an effective way. Our proposed model solves this challenge by utilizing a 5G-Next Generation-Radio Access Network (5G-NG-RAN) assisted cloud-based digital twins’ framework to virtually monitor wind turbines and form a predictive model to forecast wind speed and predict the generated power. The developed model is based on Microsoft Azure digital twins infrastructure as a 5-dimensional digital twins platform. The predictive modeling is based on a deep learning approach, temporal convolution network (TCN) followed by a non-parametric k-nearest neighbor (kNN) regression. Predictive modeling has two components. First, it processes the univariate time series data of wind to predict its speed. Secondly, it estimates the power generation for each quarter of the year ranges from one week to a whole month (i.e., medium-term prediction) To evaluate the framework the experiments are performed on onshore wind turbines publicly available datasets. The obtained results confirm the applicability of the proposed framework. Furthermore, the comparative analysis with the existing classical prediction models shows that our designed approach obtained better results. The model can assist the management team to monitor the wind farms remotely as well as estimate the power generation in advance.
2022
Ensuring a healthy and comfortable indoor environment in schools is essential for student well-being and academic performance. The purpose of this study is to investigate the factors influencing students’ satisfaction with indoor air quality (IAQ) and thermal comfort in classrooms. To address this, one year-long measurements were conducted across multiple classrooms in a Norwegian secondary school, collecting data on indoor climate (CO₂, VOC levels, temperature, relative humidity, and air pressure) along with outdoor climate variables (temperature, humidity, and solar radiation). Additional room-specific data, including orientation, floor level, and ventilation system specifications, were also considered. An online feedback system was used to gather 1,473 real-time student responses on satisfaction levels. Supervised machine learning (ML) models were developed to assess the importance of these parameters in predicting perceived indoor comfort: IAQ perceptions and thermal environmental perceptions. Results showed ML models effectively predicted student dissatisfaction, achieving accuracy greater than 80% when environmental and building parameters were considered simultaneously. The findings emphasized that dissatisfaction with indoor conditions is driven by multiple interacting factors of measured variables and building parameters single independent variables. SHAP analysis provided valuable interpretability, revealing how variations in environmental conditions collectively impact students' perceived comfort. This comprehensive approach demonstrates the practical potential of ML-based IEQ monitoring systems, suggesting that schools can proactively improve indoor conditions through targeted interventions informed by real-time predictions.
2025
2026
Machine learning for mapping glacier surface facies in Svalbard
Glaciers are dynamic and highly sensitive indicators of climate change, necessitating frequent and precise monitoring. As Earth observation technology evolves with advanced sensors and mapping methods, the need for accurate and efficient approaches to monitor glacier changes becomes increasingly important. Glacier Surface Facies (GSF), formed through snow accumulation and ablation, serve as valuable indicators of glacial health. Mapping GSF provides insights into a glacier's annual adaptations. However, satellite-based GSF mapping presents significant challenges in terms of data preprocessing and algorithm selection for accurate feature extraction. This study presents an experiment using very high-resolution (VHR) WorldView-3 satellite data to map GSF on the Midtre Lovénbreen glacier in Svalbard. We applied three machine learning (ML) algorithms—Random Forest (RF), Artificial Neural Network (ANN), and Support Vector Machine (SVM)—to explore the impact of different image preprocessing techniques, including atmospheric corrections, pan sharpening methods, and spectral band combinations. Our results demonstrate that RF outperformed both ANN and SVM, achieving an overall accuracy of 85.02 %. However, nuanced variations were found for specific processing conditions and can be explored for specific applications. This study represents the first clear delineation of ML algorithm performance for GSF mapping under varying preprocessing conditions. The data and findings from this experiment will inform future ML-based studies aimed at understanding glaciological adaptations in a rapidly changing cryosphere, with potential applications in long-term spatiotemporal monitoring of glacier health.
2025
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Luftkvalitetsmålinger, Norske Skog Saugbrugsforeningen AS, Halden. NILU OR
Norsk institutt for luftforskning (NILU) har på oppdrag fra Norske Skog, Saugbrugsforeningen AS i Halden gjennomført et måleprogram for målinger av luftkvalitet i bebygd område i nærheten av fabrikkanlegget. Bakgrunnen for forespørselen var klager fra naboer på stikkende lukt og sjenerende røyk fra fabrikken.Det var vind med moderat styrke fra fabrikken mot boligene i alle periodene med luktplager.Måleresultatene viser forhøyede VOC-konsentrasjoner i uteluft under luktepisodene i forhold til den gjennomsnittlige VOC-konsentrasjonen i måleperioden. De identifiserte enkeltkomponentene som hadde høyest konsentrasjon, var først og fremst terpener som også ble målt i utslippene i 1998. Utslipp fra Saugbrugsforeningen gir sannsynligvis det største bidraget til VOC i uteluft ved de undersøkte boligene. Målte konsentrasjoner av enkeltkomponenter av VOC ligger langt under lukttersklene for de enkelte forbindelsene. Målte gjennomsnittskonsentrasjoner av eddiksyre og maursyre var i samme størrelsesorden som konsentrasjoner det er vanlig å finne i urbane strøk. Resultatene viser forhøyede konsentrasjoner i uteluft under luktepisodene i forhold til gjennomsnittskonsentrasjonen i måleperioden. Hovedkilden til eddiksyre og maursyre er sannsynligvis utslipp fra fabrikken. Målte konsentrasjoner av maursyre ligger langt under lukttersklen mens eddiksyrekonsentrasjonene ligger i samme størrelseorden som de laveste lukterskelene som er rapportert.De høyeste timesmiddelverdiene av partikulært sulfat (SO4) var i samme størrelsesorden som maksimale døgnmiddelskonsentrasjoner NILU måler i lite forurenset område på bakgrunnsstasjonen Prestebakke. Målte timesmiddelkonsentrasjoner av svoveldioksid (SO2) var høyere enn SO2 -nivået på bakgrunnstasjonen Prestebakke. Dette tyder på at lokalt utslipp fra fabrikken gir et bidrag til SO2-forurensning i området men konsentrasjonene er lave.
2000
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Luftkvalitetsmålinger ved Kværnerbygget i Lodalen i Oslo i perioden september 2002-april 2003. NILU OR
2003
Luftkvalitetsmålinger ved Konowsgt. 67B 'postgarasjen' i Lodalen i Oslo. Desember 2002 - mai 2003. NILU OR
2003
2004