Publications
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- Sherwin, E., Rutherford, J., Chen, Y., Aminfard, S., Kort, E., Jackson, R., & Brandt, A. (2023). Single-blind validation of space-based point-source detection and quantification of onshore methane emissions. Scientific Reports, 13, 3836. https://doi.org/10.1038/s41598-023-30761-2
- Jing, L., El-Houjeiri, H., Monfort, J.-C., Littlefield, J., Al-Qahtani, A., Dixit, Y., Speth, R., Brandt, A., Masnadi, M., MacLean, H., Peltier, W., Gordon, D., & Bergerson, J. (2022). Understanding variability in petroleum jet fuel life cycle greenhouse gas emissions to inform aviation decarbonization. Nature Communications, 13(1), 7853. https://doi.org/10.1038/s41467-022-35392-1
- Zhang, Z., Sherwin, E., Varon, D., & Brandt, A. (2022). Detecting and quantifying methane emissions from oil and gas production: algorithm development with ground-truth calibration based on Sentinel-2 satellite imagery. Atmospheric Measurement Techniques, 15(23), 7155-7169. https://doi.org/10.5194/amt-15-7155-2022
- Sherwin, E., Lever, E., & Brandt, A. (2022). Low-Cost Representative Sampling for a Natural Gas Distribution System in Transition. ACS Omega, 7(48), 43973–43980. https://doi.org/10.1021/acsomega.2c05314
- Yu, J., Hmiel, B., Lyon, D., Warren, J., Cusworth, D., Duren, R., Chen, Y., Murphy, E., & Brandt, A. (2022). Methane Emissions from Natural Gas Gathering Pipelines in the Permian Basin. Environmental Science & Technology Letters, 9(11), 969–974. https://doi.org/10.1021/acs.estlett.2c00380
- Kuepper, L., Teichgraeber, H., Baumgärtner, N., Bardow, A., & Brandt, A. (2022). Wind data introduce error in time-series reduction for capacity expansion modelling. Energy, 256, 124467. https://doi.org/10.1016/j.energy.2022.124467
- Plant, G., Kort, E., Brandt, A., Chen, Y., Fordice, G., Gorchov Negron, A., Schwietzke, S., Smith, M., & Zavala-Araiza, D. (2022). Inefficient and unlit natural gas flares both emit large quantities of methane. Science, Report: Methane Emissions, 377(6614), 1566-1571. https://doi.org/10.1126/science.abq0385
- Von Wald, G., Sundbar, K., Sherwin, E., Zlotnik, A., & Brandt, A. (2022). Optimal Gas-Electric Energy System Decarbonization Planning. Advances in Applied Energy, 6, 100086. https://doi.org/10.1016/j.adapen.2022.100086
- Teichgraeber, H., & Brandt, A. (2022). Time-series aggregation for the optimization of energy systems: Goals, challenges, approaches, and opportunities. Renewable and Sustainable Energy Reviews, 157, 111984. https://doi.org/10.1016/j.rser.2021.111984
- Chen, Y., Sherwin, E., Berman, E., Jones, B., Gordo, M., Wetherly, E., Kort, E., & Brandt, A. (2022). Quantifying Regional Methane Emissions in the New Mexico Permian Basin with a Comprehensive Aerial Survey. Environmental Science & Technology, 56(7), 4317–4323. https://doi.org/10.1021/acs.est.1c06458
- Shi, L., Mach, K., Suh, S., & Brandt, A. (2022). Functionality-based life cycle assessment framework: An information and communication technologies (ICT) product case study. Journal of Industrial Ecology, 26(3), 782-800. https://doi.org/10.1111/jiec.13240 Find it @ Stanford
- Bell, C., Rutherford, J., Brandt, A., Sherwin, E., Vaughn, T., & Zimmerle, D. (2022). Single-blind determination of methane detection limits and quantification accuracy using aircraft-based LiDAR. Elementa: Science of the Anthropocene, 10(1), 00080. https://doi.org/10.1525/elementa.2022.00080
- Wang, J., Ji, J., Ravikumara, A., Savarese, S., & Brandt, A. (2022). VideoGasNet: Deep learning for natural gas methane leak classification using an infrared camera. 238, 121516. https://doi.org/10.1016/j.energy.2021.121516
- Teichgraeber, H., Küpper, L., & Brandt, A. (2021). Designing reliable future energy systems by iteratively including extreme periods in time-series aggregation. Applied Energy, 304, 117696. https://doi.org/10.1016/j.apenergy.2021.117696
- Zhang, Z., Sherwin, E., & Brandt, A. (2021). Estimating global oilfield-specific flaring with uncertainty using a detailed geographic database of oil and gas fields. Environmental Research Letters, 16(12), 124039. https://doi.org/10.1088/1748-9326/ac3956
- El Abbadi, S., Sherwin, E., Brandt, A., Luby, S., & Criddle, C. (2021). Displacing fishmeal with protein derived from stranded methane. Nature Sustainability, 5, 47–56. https://doi.org/10.1038/s41893-021-00796-2
- Masnadi, M., Benini, G., El-Houjeiri, H., Milivinti, A., Anderson, J., Wallington, T., De Kleine, R., Dotti, V., Jochem, P., & Brandt, A. (2021). Carbon implications of marginal oils from market-derived demand shocks. Nature Research, 599(7883), 80–84. https://doi.org/10.1038/s41586-021-03932-2
- Orsini, R., Brodrick, P., Brandt, A., & Durlofsky, L. (2021). Computational optimization of solar thermal generation with energy storage. Sustainable Energy Technologies and Assessments, 47(3), 101342. https://doi.org/10.1016/j.seta.2021.101342
- Rutherford, J., Sherwin, E., Ravikumar, A., Heath, G., Englander, J., Cooley, D., Omara, M., Lanfitt, Q., & Brandt, A. (2021). Closing the methane gap in US oil and natural gas production emissions inventories. Nature Communications, 12(1), 4715. https://doi.org/10.1038/s41467-021-25017-4
- Nie, Y., Zamzam, A., & Brandt, A. (2021). Resampling and data augmentation for short-term PV output prediction based on an imbalanced sky images dataset using convolutional neural networks. Solar Energy, 224, 341-354. https://doi.org/10.1016/j.solener.2021.05.095
2020
Nie, Y., Sun, Y., Chen, Y., Orsini, R., & Brandt, A. (2020). PV power output prediction from sky images using convolutional neural network: The comparison of sky-condition-specific sub-models and an end-to-end model. Journal of Renewable and Sustainable Energy, 12(4), 046101. https://doi.org/10.1063/5.0014016
2019
Ravikumar, A.P., Sreedhara, S., Wang, J., Englander, J., Roda-Stuart, D., Bell, C., Zimmerle, D., Lyon, D., Mogstad, I., Ratner, B. and Brandt, A.R., 2019. Single-blind inter-comparison of methane detection technologies – results from the Stanford/EDF Mobile Monitoring Challenge. Elem Sci Anth, 7(1), p.37. DOI: http://doi.org/10.1525/elementa.373
Sun, Y., Venugopal, V., & Brandt, A. R. (2019). Short-term solar power forecast with deep learning: Exploring optimal input and output configuration. Solar Energy, 188, 730–741. https://doi.org/10.1016/j.solener.2019.06.041
Venugopal, V., Sun, Y., & Brandt, A. R. (2019). Short-term solar PV forecasting using computer vision: The search for optimal CNN architectures for incorporating sky images and PV generation history. Journal of Renewable and Sustainable Energy, 11(6), 066102. https://doi.org/10.1063/1.5122796
Wang, J., Tchapmi, L. P., Ravikumar, A. P., McGuire, M., Bell, C. S., Zimmerle, D., Savarese, S., Brandt, A. R. (2020). Machine vision for natural gas methane emissions detection using an infrared camera. Applied Energy, 257, 113998. DOI: https://doi.org/10.1016/j.apenergy.2019.113998
2018
R.A. Alvarez, D. Zavala-Araiza, D.R. Lyon, D.T. Allen, Z.R. Barkley, A.R. Brandt, K.J. Davis, S.C. Herndon, D.J. Jacob, A. Karion, E.A. Kort, B.K. Lamb, T. Lauvaux, J.D. Maasakkers, A.J. Marchese, M. Omara, S.W. Pacala, J. Peischl, A.L. Robinson, P.B. Shepson, C. Sweeney, A. Townsend-Small, S.C. Wofsy, S.P. Hamburg. Assessment of methane emissions from the U.S. oil and gas supply chain. Science. DOI: 10.1126/science.aar7204
*Brandt, A.R., M.S. Masnadi, J.G. Englander, J.G. Koomey, D. Gordon. Climate-wise oil choices in a world of oil abundance. Environmental Research Letters DOI: 10.1088/1748- 9326/aaae76
*P.G. Brodrick, A.R. Brandt., L.J. Durlofsky. Optimal design and operation of integrated solar combined cycles under emissions intensity constraints. Applied Energy
DOI: 10.1016/j.apenergy.2018.06.052
Englander, J.G.; Brandt, A.R.; Conley, S.; Lyon, D.; Jackson, R.B. (2018). Aerial inter-year comparison and quantification of methane emissions persistence in the Bakken formation of North Dakota, USA. Environmental Science & Technology. DOI: 10.1021/acs.est.8b01665
*Masnadi, M.S., D. Schunack, Y. Li, S.O. Roberts, A.R. Brandt, H.M. El-Houjeiri, S. Przesmitzki, M.Q. Wang. Well-to-refinery emissions and net-energy analysis of China?s crude-oil supply. Nature Energy. DOI: 10.1038/s41560-018-0090-7