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42. Improving Near Real-Time Predictions of Renewable Electricity Production at Substation Level

Author

Teng, Sin Yong, van Nooten, Charlotte Cambier, van Doorn, Jan Maarten, Ottenbros, Anne, Huijbregts, Mark, and Jansen, Jeroen J.

Subjects
Energy network, Renewable energy, Missing data, Zero-shot learning, Transfer learning
Abstract

With the recent urgency of the transition to a low-carbon economy, the monitoring and prediction of regional renewable energy generation over time have become increasingly important. The difficulties of renewable energy data transfer between multiple stakeholders have also caused elevated the need for more trustworthy data analytics within the energy grid. However, only a few voltage transformation facilities in the grid (i.e. substations) contain complete information about the renewable energy generated within the region. A large number of incomplete-information substations with fully-missing renewable energy data limits the analysis and policy-making related to renewable energy. This work studies the potential to transfer information from perfect-information substations to incomplete-information substations with fully-missing renewable energy data. To preserve the practicality of renewable energy prediction, a domain adaptation for zero-shot learning in sequence (DAZLS) strategy is proposed for fully-missing renewable energy prediction in substations. DAZLS is a model agnostic technique which can utilize any base model within its framework for the prediction of renewable energy generation. Using total measured power and weather information (solar irradiation and wind speed) in information-complete substations in 10 locations within the Netherlands, we developed a model to predict solar and wind power from energy producers associated with information incomplete substations via additional real-time weather data, metadata information (e.g. geospatial position, existence and capacity of renewable facilities). Using DAZLS, the average root-mean-squared error for prediction (RMSEP) is 0.07, while that of a default transfer learning model is 0.70. This meant that renewable energy sources in information-incomplete substations could be reliably monitored using weather data, meta-data and physical data, resulting in lesser investment in power meters. This approach was demonstrated for the highest frequency prediction possible in the grid, with a near-real-time frequency of 15 minutes., S. Y. Teng is grateful for the financial support from the Green Information Technology division of the Interdisciplinary Research Platform, Radboud University Nijmegen under the voucher "RISAI: Renewable Integrated Systems Accelerate by Artificial Intelligence". MA.J. Huijbregts received support from a grant from the Dutch research foundation for the project Global environmental trade-offs of renewable energy technologies (016.Vici.170.190). The photographers Hans Peter van Velthoven, Jack Tillmans and Foto Focus via Alliander for Figure 1 were also appreciated.

Published
2022
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49. Transition Metal Dichalcogenides for the Application of Pollution Reduction: A Review

Author

Zhang, Xixia, Teng, Sin Yong, Loy, Adrian Chun Minh, How, Bing Shen, Leong, Wei Dong, Tao, Xutang, Zhang, Xixia, Teng, Sin Yong, Loy, Adrian Chun Minh, How, Bing Shen, Leong, Wei Dong, and Tao, Xutang

Abstract

The material characteristics and properties of transition metal dichalcogenide (TMDCs) have gained research interest in various fields, such as electronics, catalytic, and energy storage. In particular, many researchers have been focusing on the applications of TMDCs in dealing with environmental pollution. TMDCs provide a unique opportunity to develop higher-value applications related to environmental matters. This work highlights the applications of TMDCs contributing to pollution reduction in (i) gas sensing technology, (ii) gas adsorption and removal, (iii) wastewater treatment, (iv) fuel cleaning, and (v) carbon dioxide valorization and conversion. Overall, the applications of TMDCs have successfully demonstrated the advantages of contributing to environmental conversation due to their special properties. The challenges and bottlenecks of implementing TMDCs in the actual industry are also highlighted. More efforts need to be devoted to overcoming the hurdles to maximize the potential of TMDCs implementation in the industry.

Published
2020

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