Mika Rantonen

Global warming is a significant threat to the future of humankind. It is caused by greenhouse gases that accumulate in the atmosphere. CO₂ emissions are one of the main drivers of global warming, and the energy sector is one of the main contributors to CO₂ emissions. Recent technological advances in artificial intelligence (AI) have accelerated the adoption of AI in numerous applications to solve many problems. This study carries out a scoping review to understand the use of AI solutions to reduce CO₂ emissions in the energy sector. This paper follows the PRISMA-ScR guidelines in reporting the findings. The academic search engine Google Scholar was utilized to find papers that met the review criteria. Our research question was “How is artificial intelligence used in the energy sector to reduce CO₂ emissions?” Search phrases and inclusion criteria were decided based on this research question. In total, 186 papers from the search results were screened, and 16 papers fitting our criteria were summarized in this study. The findings indicate that AI is already used in the energy sector to reduce CO₂ emissions. Three main areas of application for AI techniques were identified. Firstly, AI models are employed to directly optimize energy generation processes by modeling these processes and determining their optimal parameters. Secondly, AI techniques are utilized for forecasting, which aids in optimizing decision-making, energy transmission, and production planning. Lastly, AI is applied to enhance energy efficiency, particularly in optimizing building performance. The use of AI shows significant promise of reducing CO₂ emissions in the energy sector.

Satellite-based Synthetic Aperture Radar (SAR) images can be used as a source of remote sensed imagery regardless of cloud cover and day-night cycle. However, the speckle noise and varying image acquisition conditions pose a challenge for change detection classifiers. This paper proposes a new method of improving SAR image processing to produce higher quality difference images for the classification algorithms. The method is built on a neural network-based mapping transformation function that produces artificial SAR images from a location in the requested acquisition conditions. The inputs for the model are: previous SAR images from the location, imaging angle information from the SAR images, digital elevation model, and weather conditions. The method was tested with data from a location in North-East Finland by using Sentinel-1 SAR images from European Space Agency, weather data from Finnish Meteorological Institute, and a digital elevation model from National Land Survey of Finland. In order to verify the method, changes to the SAR images were simulated, and the performance of the proposed method was measured using experimentation where it gave substantial improvements to performance when compared to a more conventional method of creating difference images.

This work suggests and tests a solution for detecting loss of integrity with means of prediction in distributed, low‐power computing environment or when collecting relational data disconnected from original data source to data ecosystems. With graph convolution network and variational auto‐encoder, the proposed solution is able to detect data integrity attacks based on link prediction for datasets from various data sources without common data model. The solution has in‐built mechanism to prevent poisoning the detection capability, and it is resource efficient since it does not require extensive storage capability. Due to its programmatic nature and high ability to integrate, unsupervised learning‐based solution fits well in embedded system ecosystems' control functions where automated decisions are taken.

Information about the paper titled "CONVERSION OF EMERGING ICT-TECHNOLOGY INTO CURRICULUM COURSES" at IATED Digital Library