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362 results on '"Wallom, David"'

1. Optimal sizing of solar photovoltaic and lithium battery storage to reduce grid electricity reliance in buildings

Author

Ren, Han Kun, McCulloch, Malcolm, and Wallom, David

Subjects
Electrical Engineering and Systems Science - Systems and Control
Abstract

In alignment with the Paris Agreement, the city of Oxford in the UK aims to become carbon neutral by 2040. Renewable energy help achieve this target by reducing the reliance on carbon-intensive grid electricity. This research seeks to optimally size solar photovoltaic and lithium battery storage systems, reducing Oxford's grid electricity reliance in buildings. The analysis starts with modeling the electricity demand. The model uses Elexon electricity settlement profiles, and assembles them into the demand profile according to the quantity and types of buildings in Oxford. Then, solar generation is modeled using Pfenninger and Staffell's method. Solar photovoltaic and lithium storage systems are sized using a hybridized analytical and iterative method. First, the method calculates the solar system size search range, then iterates through the range. At each solar size, the method calculates and iterates through the storage system size search range. Within each iteration, the renewable system is simulated using demand and generation data with a simplified system set-up and the conventional operation strategy. The method outputs combinations of solar system capacity, storage system capacity, and grid electricity import. Each combination's levelized cost of electricity is calculated, and the lowest cost combination is the optimal sizing. Solar and storage system costs are projected from 2019 to 2100, and the optimal sizing is calculated for each year. The result shows that solar photovoltaic is economically competitive, but lithium storage cost is still too high. As solar and storage prices continue to drop, they will take up greater portions of the energy system. However, there will always be a need for the grid, as it provides flexibility and can meet demands that are too costly for solar and storage, Comment: 10 pages, 8 figures, published in the conference of ECEEE 2022 Summer Study on energy efficiency: agents of change

Published
2023

3. A generalised multi-factor deep learning electricity load forecasting model for wildfire-prone areas

Author

Yang, Weijia, Sparrow, Sarah N., and Wallom, David C. H.

Subjects
Electrical Engineering and Systems Science - Systems and Control, Computer Science - Machine Learning
Abstract

This paper proposes a generalised and robust multi-factor Gated Recurrent Unit (GRU) based Deep Learning (DL) model to forecast electricity load in distribution networks during wildfire seasons. The flexible modelling methods consider data input structure, calendar effects and correlation-based leading temperature conditions. Compared to the regular use of instantaneous temperature, the Mean Absolute Percentage Error (MAPE) is decreased by 30.73% by using the proposed input feature selection and leading temperature relationships. Our model is generalised and applied to eight real distribution networks in Victoria, Australia, during the wildfire seasons of 2015-2020. We demonstrate that the GRU-based model consistently outperforms another DL model, Long Short-Term Memory (LSTM), at every step, giving average improvements in Mean Squared Error (MSE) and MAPE of 10.06% and 12.86%, respectively. The sensitivity to large-scale climate variability in training data sets, e.g. El Ni\~no or La Ni\~na years, is considered to understand the possible consequences for load forecasting performance stability, showing minimal impact. Other factors such as regional poverty rate and large-scale off-peak electricity use are potential factors to further improve forecast performance. The proposed method achieves an average forecast MAPE of around 3%, giving a potential annual energy saving of AU\$80.46 million for the state of Victoria.

Published
2023

4. Incentive-weighted Anomaly Detection for False Data Injection Attacks Against Smart Meter Load Profiles

Author

Higgins, Martin, Stephen, Bruce, and Wallom, David

Subjects
Electrical Engineering and Systems Science - Systems and Control
Abstract

Spot pricing is often suggested as a method of increasing demand-side flexibility in electrical power load. However, few works have considered the vulnerability of spot pricing to financial fraud via false data injection (FDI) style attacks. In this paper, we consider attacks which aim to alter the consumer load profile to exploit intraday price dips. We examine an anomaly detection protocol for cyber-attacks that seek to leverage spot prices for financial gain. In this way we outline a methodology for detecting attacks on industrial load smart meters. We first create a feature clustering model of the underlying business, segregated by business type. We then use these clusters to create an incentive-weighted anomaly detection protocol for false data attacks against load profiles. This clustering-based methodology incorporates both the load profile and spot pricing considerations for the detection of injected load profiles. To reduce false positives, we model incentive-based detection, which includes knowledge of spot prices, into the anomaly tracking, enabling the methodology to account for changes in the load profile which are unlikely to be attacks.

Published
2023

5. Spatial-Temporal Anomaly Detection for Sensor Attacks in Autonomous Vehicles

Author

Higgins, Martin, Jha, Devki, and Wallom, David

Subjects
Electrical Engineering and Systems Science - Systems and Control, Computer Science - Cryptography and Security, Computer Science - Machine Learning
Abstract

Time-of-flight (ToF) distance measurement devices such as ultrasonics, LiDAR and radar are widely used in autonomous vehicles for environmental perception, navigation and assisted braking control. Despite their relative importance in making safer driving decisions, these devices are vulnerable to multiple attack types including spoofing, triggering and false data injection. When these attacks are successful they can compromise the security of autonomous vehicles leading to severe consequences for the driver, nearby vehicles and pedestrians. To handle these attacks and protect the measurement devices, we propose a spatial-temporal anomaly detection model \textit{STAnDS} which incorporates a residual error spatial detector, with a time-based expected change detection. This approach is evaluated using a simulated quantitative environment and the results show that \textit{STAnDS} is effective at detecting multiple attack types.

Published
2022

18. Anomaly Detection for Industrial Big Data

Author

Caithness, Neil and Wallom, David

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

As the Industrial Internet of Things (IIoT) grows, systems are increasingly being monitored by arrays of sensors returning time-series data at ever-increasing 'volume, velocity and variety' (i.e. Industrial Big Data). An obvious use for these data is real-time systems condition monitoring and prognostic time to failure analysis (remaining useful life, RUL). (e.g. See white papers by Senseye.io, and output of the NASA Prognostics Center of Excellence (PCoE).) However, as noted by Agrawal and Choudhary 'Our ability to collect "big data" has greatly surpassed our capability to analyze it, underscoring the emergence of the fourth paradigm of science, which is data-driven discovery.' In order to fully utilize the potential of Industrial Big Data we need data-driven techniques that operate at scales that process models cannot. Here we present a prototype technique for data-driven anomaly detection to operate at industrial scale. The method generalizes to application with almost any multivariate dataset based on independent ordinations of repeated (bootstrapped) partitions of the dataset and inspection of the joint distribution of ordinal distances., Comment: 9 pages; 11 figures

Published
2018
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22. EXPLAINING EXTREME EVENTS OF 2017 : From A Climate Perspective

Author

Herring, Stephanie C., Christidis, Nikolaos, Hoell, Andrew, Hoerling, Marty, Stott, Peter A., Owen, Rebecca, Vano, Julie A., Dettinger, Michael D., Cifelli, Rob, Curtis, David, Dufour, Alexis, Miller, Kathleen, Olsen, J. Rolf, Wilson, Anna M., Nerem, R. S., Fasullo, J., Hoell, Andrew, Perlwitz, Judith, Dewes, Candida, Wolter, Klaus, Rangwala, Imtiaz, Quan, Xiao-Wei, Eischeid, Jon, Wang, Hailan, Schubert, Siegfried D., Koster, Randal D., Chang, Yehui, Christidis, Nikolaos, Betts, Richard A., Stott, Peter A., de Abreu, Rafael C., Cunningham, Christopher, Rudorff, Conrado M., Rudorff, Natalia, Abatan, Abayomi A., Dong, Buwen, Lott, Fraser C., Tett, Simon F. B., Sparrow, Sarah N., Navarro, Juan C. Acosta, Ortega, Pablo, García-Serrano, Javier, Guemas, Virginie, Tourigny, Etienne, Cruz-García, Rubén, Massonnet, François, Doblas-Reyes, Francisco J., Kew, Sarah F., Philip, Sjoukje Y., van Oldenborgh, Geert Jan, Otto, Friederike E. L., Vautard, Robert, van der Schrier, Gerard, Funk, Chris, Hoell, Andrew, Nicholson, Sharon, Korecha, Diriba, Galu, Gideon, Artan, Guleid, Teshome, Fetene, Hailermariam, Kinfe, Segele, Zewdu, Harrison, Laura, Tadege, Abebe, Atheru, Zachary, Pomposi, Catherine, Pedreros, Diego, Rimi, Ruksana H., Haustein, Karsten, Barbour, Emily J., Allen, Myles R., Takahashi, Chiharu, Shiogama, Hideo, Imada, Yukiko, Kosaka, Yu, Mori, Masato, Arai, Miki, Kamae, Youichi, Watanabe, Masahiro, Min, Seung-Ki, Kim, Yeon-Hee, Park, In-Hong, Lee, Donghyun, Sparrow, Sarah, Wallom, David, Stone, Dáithí, Sun, Ying, Dong, Siyan, Zhang, Xuebin, Stott, Peter, Hu, Ting, Wang, Shanshan, Yuan, Xing, Wu, Renguang, Chen, Yang, Chen, Wei, Su, Qin, Luo, Feifei, Sparrow, Sarah, Tian, Fangxing, Dong, Buwen, Tett, Simon F. B., Lott, Fraser C., Wallom, David, Zhou, Chunlüe, Wang, Kaicun, Qi, Dan, Tan, Jianguo, Perkins-Kirkpatrick, S. E., King, A. D., Cougnon, E. A., Grose, M. R., Oliver, E. C. J., Holbrook, N. J., Lewis, S. C., Pourasghar, F., Hope, Pandora, Black, Mitchell T., Lim, Eun-Pa, Dowdy, Andrew, Wang, Guomin, Pepler, Acacia S., and Fawcett, Robert J. B.

Published
2019

27. Half a degree additional warming, prognosis and projected impacts (HAPPI): background and experimental design

Author

Mitchell, Daniel, AchutaRao, Krishna, Allen, Myles, Bethke, Ingo, Beyerle, Urs, Ciavarella, Andrew, Forster, Piers M, Fuglestvedt, Jan, Gillett, Nathan, Haustein, Karsten, Ingram, William, Iversen, Trond, Kharin, Viatcheslav, Klingaman, Nicholas, Massey, Neil, Fischer, Erich, Schleussner, Carl-Friedrich, Scinocca, John, Seland, Øyvind, Shiogama, Hideo, Shuckburgh, Emily, Sparrow, Sarah, Stone, Dáithí, Uhe, Peter, Wallom, David, Wehner, Michael, and Zaaboul, Rashyd

Subjects
Earth Sciences, Atmospheric Sciences, Climate Action, Earth sciences
Abstract

The Intergovernmental Panel on Climate Change (IPCC) has accepted the invitation from the UNFCCC to provide a special report on the impacts of global warming of 1.5 °C above pre-industrial levels and on related global greenhouse-gas emission pathways. Many current experiments in, for example, the Coupled Model Inter-comparison Project (CMIP), are not specifically designed for informing this report. Here, we document the design of the half a degree additional warming, projections, prognosis and impacts (HAPPI) experiment. HAPPI provides a framework for the generation of climate data describing how the climate, and in particular extreme weather, might differ from the present day in worlds that are 1.5 and 2.0 °C warmer than pre-industrial conditions. Output from participating climate models includes variables frequently used by a range of impact models. The key challenge is to separate the impact of an additional approximately half degree of warming from uncertainty in climate model responses and internal climate variability that dominate CMIP-style experiments under low-emission scenarios. Large ensembles of simulations (> 50 members) of atmosphere-only models for three time slices are proposed, each a decade in length: the first being the most recent observed 10-year period (2006-2015), the second two being estimates of a similar decade but under 1.5 and 2 °C conditions a century in the future. We use the representative concentration pathway 2.6 (RCP2.6) to provide the model boundary conditions for the 1.5 °C scenario, and a weighted combination of RCP2.6 and RCP4.5 for the 2 °C scenario.

Published
2017

29. The ocean sampling day consortium

Author

Kopf, Anna, Bicak, Mesude, Kottmann, Renzo, Schnetzer, Julia, Kostadinov, Ivaylo, Lehmann, Katja, Fernandez-Guerra, Antonio, Jeanthon, Christian, Rahav, Eyal, Ullrich, Matthias, Wichels, Antje, Gerdts, Gunnar, Polymenakou, Paraskevi, Kotoulas, Giorgos, Siam, Rania, Abdallah, Rehab Z, Sonnenschein, Eva C, Cariou, Thierry, O’Gara, Fergal, Jackson, Stephen, Orlic, Sandi, Steinke, Michael, Busch, Julia, Duarte, Bernardo, Caçador, Isabel, Canning-Clode, João, Bobrova, Oleksandra, Marteinsson, Viggo, Reynisson, Eyjolfur, Loureiro, Clara Magalhães, Luna, Gian Marco, Quero, Grazia Marina, Löscher, Carolin R, Kremp, Anke, DeLorenzo, Marie E, Øvreås, Lise, Tolman, Jennifer, LaRoche, Julie, Penna, Antonella, Frischer, Marc, Davis, Timothy, Katherine, Barker, Meyer, Christopher P, Ramos, Sandra, Magalhães, Catarina, Jude-Lemeilleur, Florence, Aguirre-Macedo, Ma Leopoldina, Wang, Shiao, Poulton, Nicole, Jones, Scott, Collin, Rachel, Fuhrman, Jed A, Conan, Pascal, Alonso, Cecilia, Stambler, Noga, Goodwin, Kelly, Yakimov, Michael M, Baltar, Federico, Bodrossy, Levente, Van De Kamp, Jodie, Frampton, Dion MF, Ostrowski, Martin, Van Ruth, Paul, Malthouse, Paul, Claus, Simon, Deneudt, Klaas, Mortelmans, Jonas, Pitois, Sophie, Wallom, David, Salter, Ian, Costa, Rodrigo, Schroeder, Declan C, Kandil, Mahrous M, Amaral, Valentina, Biancalana, Florencia, Santana, Rafael, Pedrotti, Maria Luiza, Yoshida, Takashi, Ogata, Hiroyuki, Ingleton, Tim, Munnik, Kate, Rodriguez-Ezpeleta, Naiara, Berteaux-Lecellier, Veronique, Wecker, Patricia, Cancio, Ibon, Vaulot, Daniel, Bienhold, Christina, Ghazal, Hassan, Chaouni, Bouchra, Essayeh, Soumya, Ettamimi, Sara, Zaid, El Houcine, Boukhatem, Noureddine, Bouali, Abderrahim, Chahboune, Rajaa, Barrijal, Said, Timinouni, Mohammed, El Otmani, Fatima, Bennani, Mohamed, and Mea, Marianna

Subjects
Microbiology, Oceanography, Biological Sciences, Earth Sciences, Life Below Water, Biodiversity, Database Management Systems, Marine Biology, Metagenomics, Oceans and Seas, Ocean sampling day, OSD, Genomics, Health Index, Bacteria, Microorganism, Marine, Micro B3, Standards
Abstract

Ocean Sampling Day was initiated by the EU-funded Micro B3 (Marine Microbial Biodiversity, Bioinformatics, Biotechnology) project to obtain a snapshot of the marine microbial biodiversity and function of the world's oceans. It is a simultaneous global mega-sequencing campaign aiming to generate the largest standardized microbial data set in a single day. This will be achievable only through the coordinated efforts of an Ocean Sampling Day Consortium, supportive partnerships and networks between sites. This commentary outlines the establishment, function and aims of the Consortium and describes our vision for a sustainable study of marine microbial communities and their embedded functional traits.

Published
2015

30. chapter 21: A 1-DAY EXTREME RAINFALL EVENT IN TASMANIA: PROCESS EVALUATION AND LONG TAIL ATTRIBUTION

Author

Tozer, Carly R., Risbey, James S., Grose, Michael R., Monselesan, Didier P., Squire, Dougal T., Black, Amanda S., Richardson, Doug, Sparrow, Sarah N., Li, Sihan, and Wallom, David

Subjects
Tasmania -- Environmental aspects, Rain -- Environmental aspects, Business, Earth sciences
Abstract

Attribution of an extreme magnitude I-day rainfall event in Hobart is inhibited by small sample size. For moderate magnitude Hobart daily rainfall extremes, models suggest that the associated extratropical lows [...]

Published
2020
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31. chapter 9: ON HIGH PRECIPITATION IN MOZAMBIQUE, ZIMBABWE, AND ZAMBIA IN FEBRUARY 2018: This multi-method study of high precipitation over parts of Mozambique, Zimbabwe, and parts of Zambia in February 2018 indicates decreased likelihood of such events due to climate change, but with substantial uncertainty based on the used observations and models

Author

Fuckar, Neven S., Otto, Friederike E.L., Lehner, Flavio, Pinto, Izidine, Howard, Emma, Sparrow, Sarah, Li, Sihan, and Wallom, David

Subjects
Precipitation (Meteorology) -- Environmental aspects, Business, Earth sciences
Abstract

Precipitation in southern Africa displays a notable zonal gradient (south of about 15[degrees]S there is a dominant contrast between dry west and wet east) and is characterized by a pronounced [...]

Published
2020
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35. ANTHROPOGENIC WARMING HAS SUBSTANTIALLY INCREASED THE LIKELIHOOD OF JULY 2017-LIKE HEAT WAVES OVER CENTRAL EASTERN CHINA: Heat waves in central eastern China like the record-breaking July 2017 event were rare in natural worlds but have now become approximately 1-in-5-yr events due to anthropogenic forcings

Author

Chen, Yang, Chen, Wei, Su, Qin, Luo, Feifei, Sparrow, Sarah, Tian, Fangxing, Dong, Buwen, Tett, Simon F.B., Lott, Fraser C., and Wallom, David

Subjects
China -- Environmental aspects, Meteorological research, Human-environment interactions -- Research, Hot weather -- Research, Business, Earth sciences
Abstract

INTRODUCTION. During July 2017, an unprecedentedly intense heat wave struck central eastern China, resulting in drastically increased human morbidity/mortality, steeply reduced agriculture productivity, and serious shortage of electricity and water [...]

Published
2019
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36. ANTHROPOGENIC CONTRIBUTION TO THE 2017 EARLIEST SUMMER ONSET IN SOUTH KOREA: Large-ensemble RCM and GCM simulations suggest a significant anthropogenic contribution to the observed warmest May and earliest summer onset in South Korea, increasing the risk of its occurrence by 2-3 times

Author

Min, Seung-Ki, Kim, Yeon-Hee, Park, In-Hong, Lee, Donghyun, Sparrow, Sarah, Wallom, David, and Stone, Daithi

Subjects
South Korea -- Environmental aspects, Meteorological research, Human-environment interactions -- Research, Hot weather -- Research, Business, Earth sciences
Abstract

INTRODUCTION. During May 2017, South Korea experienced the hottest recorded temperature since 1973 (the beginning of the observations from 45 stations; Fig. 1a). This was the culmination of four consecutive [...]

Published
2019
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42. SWForum.eu The Way Forward: Workshop on Future Challenges in Software Engineering

Author

Alonso, Juncal, Favaro, John, Miller, Mark, Di Nitto, Elisabetta, Wallom, David, Ciavotta, MIchele, Di Nucci, Dario, Higgins, Martin, Giordanino, Marina, Lattari, Francesco, Lavazza, Luigi, Quintano Fernández, Nuria, and Abergas-Arteza, Julie

Subjects
machine learning, cybersecurity, AI, computing continuum, future challenges, sustainability, artificial intelligence, cyber-physical systems, security-by-design, quantum computing, service level agreement (SLA), software engineering
Abstract

SWForum.eu Way Forward Workshop: Future Challenges in Software Engineering, held on 27 June 2023 at the Politecnico di Milano (POLIMI), in the Dipartamento di Elettronica, Informazione e Bioingegneria (DEIB), featured a dynamic agenda that encompassed a comprehensive range of topics and discussions. With a focus on emerging trends, technological advancements, and industry insights, the event provided a platform for attendees to gain a deeper understanding of the challenges and opportunities awaiting the software engineering community. Unlock the power of knowledge sharing! Embrace the opportunity to showcase your expertise and connect with peers on Online SW-Forum, an accessible and free platform. With our thriving community of nearly 3,000 SWForum members, your contributions will have a far-reaching impact. Let's amplify your voice by spreading this valuable information through your networks and channels. Together, we can create a ripple effect of knowledge and collaboration. Follow us on our pages: 🎓LinkedIn company/SWForumEU 💬Twitter: @SWforumEU ₪ Website: www.SWForum.eu

Published
2023
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