• Energy Research
• 2021-2025close
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This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 723649 Indoor environmental quality (IEQ) measurement and survey data of demonstration buildings in the hybridGEOTABS project

Data Availability: The data that support the findings of this study are available from the corresponding author, C. Wen, upon reasonable request. ; Deep-learning-based fault diagnosis of wind turbine has played a significant role in advancing the renewable energy industry. However, the imbalanced data sampled by the supervisory control and data acquisition systems has led to low diagnosis accuracy. Additionally, deep neural networks can encounter issues like gradient vanishing and insufficient feature learning during backpropagation when the model is too deep. This article introduces a novel approach that is based on dynamic weight loss functions to modulate unbalanced data and improve diagnostic accuracy by focusing on misclassification of a small sample number. The proposed approach employs a 1D-CNN model and an ensemble-learning-based convolution neural network (EL-CNN) to enhance diversity of models and complementarity of feature learning. The EL-CNN model addresses the problem of local features being overlooked and provides more accurate results. The effectiveness of this proposed approach is well demonstrated through experimental cases on real wind turbine pitch system fault data. Two different networks using three different loss functions and three state-of-the-art fault diagnosis models are tested, demonstrating the EL-CNN model’s superiority. ; This work was supported in part by the European Union’s Horizon 2020 Research and Innovation Programme under Grant 820776 (INTEGRADDE), the Engineering and Physical Sciences Research Council (EPSRC) of the UK, the Royal Society of the UK, the Alexander von Humboldt Foundation of Germany, the BRIEF Award of Brunel University London, the National Natural Science Foundation of China under Grant 61973209, the Natural Science Foundation of Shanghai of China under Grant 20ZR1421200, and the Capacity Building Project of Shanghai Local Colleges and Universities of China under Grant 22010501100.

Technical and economic developments in battery and fast-charging technologies could soon make fuel cell electric vehicles, which run on hydrogen, superfluous in road transport

Sensing strategies adopting minimal electronic systems help in realizing REASSURED diagnostic tests. However, the challenge in developing such strategies escalates with demand in power and electronics during pursuit of reliable and accurate sensing. Herein, we present an electronic design strategy using a smart strip, operating with power generated from 3.5 μL of serum sample, to reveal glucose concentration through a response preserved in a capacitor. Further, by integrating an NFC tag alongside the strip, we devised a self-powered glucose measuring card, mobile-glucocard (or mGlucocard) for retrieving this stored digital response using smartphone, enabling 'connected mobile-health diagnostics'. The response from our device relates linearly to glucose concentration offering a sensitivity of 11.3 mV/mM and good correlation (R = 0.974) with colorimetric reference method. Interestingly, the design strategy uses only four components - two resistors, diode, and capacitor - of simple architecture likely transferable to printed technologies to deliver advanced self-powered sustainable devices.

Since 2015, the intended climate actions of the Paris Agreement signatories have been reported as nationally determined contributions (NDC). These climate actions are fully aligned with the 13th Sustainable Development Goal (SDG) which calls for urgent action to combat climate change. The same, however, cannot be said for their relation to the other 16 SDGs of the 2030 Agenda for Sustainable Development, since climate action can either enhance or compromise the prospects for SDG implementation. In light of this challenge, this paper proposes a simple method for quantifying the synergies and trade-offs between national climate actions and the SDGs. The method, referred to as Q-SCAN, makes use of a seven-step scale and the SDG Climate Action Nexus tool. The effectiveness of the method has been demonstrated on a case study of North Macedonia, a non-Annex I, Western Balkan country with a coal-intensive energy system. Based on the experience in the preparation of the country’s enhanced NDC, the paper elaborates how the method can be used to contribute to the alignment of the national climate actions with the SDGs and how it can be used to improve stakeholder engagement.