In this paper we introduce Distributed Acoustic Sensing (DAS) and discuss its potential application in Intelligent Transportation Systems (ITS). We present the fundamentals of the technology and data processing methods required for converting the raw data into information that can be used to track the position of vehicles along a road in real time. We present and discuss the results of experiments that explore the dependency of defined DAS signal quality metrics as a function of vehicle speed, size and distance from the fiber, as well as how the cables are installed. The experiments indicate that decisions concerning cable installation could influence data signal to noise by up to $\sim$ 10 dB. Finally, we demonstrate that DAS systems that operate by measuring phase shifts of laser light backscattered along the fiber, are capable of tracking smaller and slower moving traffic than DAS systems that rely only on measuring the intensity of the backscatter.

Interdisciplinary and transdisciplinary collaboration has become a common practice in technology development projects. Rarely, however, the integration (and translation) of knowledge from different disciplines and different societal contexts is reported in detail. In this article, we address this gap and present the inter- and transdisciplinary technology development in the international research project “DigiMon—Digital Monitoring of CO₂ Storage Projects” that aims to develop a human-centered monitoring system. Based on interviews, surveys and stakeholder workshops in Norway, Greece, Germany and The Netherlands, we identify characteristics of CO₂ storage monitoring systems that reflect the concerns and expectations of publics and stakeholders. We document the translation of social scientific findings into technical expertise for the design of a monitoring system. We discuss how the interdisciplinary and transdisciplinary process has affected the technology development. In outlining how this process was set up, carried out and validated, we are able to show a viable route for the meaningful incorporation of heterogeneous knowledge in complex energy infrastructures. Furthermore, we discuss the features of the project organization that made this comprehensive process possible. Thus, our results contribute to inter- and transdisciplinary research organization in general and to the development of methods for monitoring CO₂ storage in particular.

In this paper, we address the problem of high greenhouse gas emissions from oil and gas platforms in Norway. We look at the potential of integrating an energy system composed of wind turbines and battery systems to unload the electrical power generated by gas turbines being the main source of emissions today. We propose a simulation model of the energy system, the power demand, the available wind speed, and different control strategies. By putting the models together, we evaluate the performance of various compositions of the system and determine their impact on emissions and battery lifetime. The numerical results show that changing today’s practices has great potential to reduce greenhouse gases, with amounts varying between 30% and 80% compared with today’s level.

Short-term wind field forecasting, particularly wind speed prediction, is a critical component in wind farm control. Traditional statistical techniques, such as autoregressive integrated moving average, and machine learning approaches, such as recurrent neural networks, have been widely used for short-term forecasting, but these methods are often difficult to interpret. In this study, we propose a transformer model that can capture the internal dependencies within previous wind measurements to predict wind speeds in the upcoming hours. Measurement data from the FINO1 research platform collected in 2007 and 2008, including wind speeds, direction, and air temperature at three altitudes (40m, 60m, and 80m), have been used to train and evaluate the model. With the help of the attention mechanism in the transformer model, we can interpret the impact of the previous wind profile on future predictions, thereby enabling the explanation of deep learning models for wind forecasting. Our experimental results on the FINO1 measurement data demonstrate exceptional performance of the proposed model in short-term wind forecasting.

The technical objective of a monitoring system is to provide the means to detect potential irregularities related to the project plan, to provide assurance that the migration of the CO₂ plume stays within the storage unit, and to show that CO₂ behaves in conformance with the model predictions. From an operational point of view, monitoring will also provide data that can be used to optimize the injection schedule relative to the storage capacity and availability of CO₂ to minimize risks and long-term costs. Finally, monitoring is a crucial factor for the public perception of risks related to CO₂ storage, as surveys indicate that adequately designed monitoring can mitigate concerns. The Analytical Hierarchy Process (AHP) is a holistic, transdisciplinary, multi-criteria decision-making framework. The objective of this work is to apply the AHP framework to monitoring-solutions for a synthetic geological storage site of CO₂ to secure the technical, operational, and societal embeddedness of the solutions and gain experience in how this can be applied to a real project. Through this first application of AHP within the field of geological carbon storage, the AHP was found to be a structured and transparent framework for holistic, multi-criteria decision-making (MCDM), where the wisdom and expertise of different domain experts were considered. A further novelty in this study is introducing a measure of spread in assessing the various solution alternatives’ capacity to meet monitoring criteria. This approach was utilized to underscore disparities among respondents’ experiences and to identify potential informational deficiencies in evaluating alternatives and devising the optimal monitoring solution.