Publications
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- Brandt, A., Teichgraeber, H., Kang, C., Barnhart, C., Carbajales-Dale, M., & Sgouridis, S. (2021). Blow wind blow: Capital deployment in variable energy systems. Energy, 224, 120198.
- Lyon, D., Hmiel, B., Guatam, R., & all, et. (2021). Concurrent variation in oil and gas methane emissions and oil price during the COVID-19 pandemic. Atmospheric Chemistry and Physics, 21(9), 6605–6626. https://doi.org/10.5194/acp-21-6605-2021
- Kang, M., Brandt, A. ., Zheng, Z., Boutot, J., Yung, C., Peltz, A., & Jackson, R. (2021). Orphaned oil and gas well stimulus—Maximizing economic and environmental benefits. Science of the Anthropocene, 9(1), 00161. https://doi.org/10.1525/elementa.2020.20.00161
- Sherwin, E., Chen, Y., Ravikumar, A., & Brandt, A. (2021). Single-blind test of airplane-based hyperspectral methane detection via controlled releases. Elementa: Science of the Anthropocene, 9(1), 00063. https://doi.org/10.1525/elementa.2021.00063
- Sleep, S., Dadashi, Z., Chen, Y., Brandt, A., MacLean, H., & Bergerson, J. (2021). Improving robustness of LCA results through stakeholder engagement: A case study of emerging oil sands technologies. Journal of Cleaner Production, 281, 125277. https://doi.org/10.1016/j.jclepro.2020.125277
- Teichgraeber, H., & Brandt, A. (2020). Optimal design of an electricity-intensive industrial facility subject to electricity price uncertainty: Stochastic optimization and scenario reduction. Chemical Engineering Research and Design, 163, 204-216. https://doi.org/10.1016/j.cherd.2020.08.022
- Teichgraeber, H., Lindenmeyer, C., Baumgärtner, N., Kotzur, L., Stolten, D., Robinius, M., Bardow, A., & Brandt, A. (2020). Extreme events in time series aggregation: A case study for optimal residential energy supply systems. Applied Energy, 275, 115223. https://doi.org/10.1016/j.apenergy.2020.115223
- 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
- Nie, Y., Zhang, S., Liu, R., Roda-Stuart, D., Ravikumar, A., Bradley, A., Masnadi, M., Brandt, A., Bergerson, J., & Bi, X. (2020). Greenhouse-gas emissions of Canadian liquefied natural gas for use in China: Comparison and synthesis of three independent life cycle assessments. Journal of Cleaner Production, 258, 120701. https://doi.org/10.1016/j.jclepro.2020.120701
- Jing, L., El-Houjeiri, H., Monfort, J.-C., Brandt, A., Masnadi, M., Gordon, D., & Bergerson, J. (2020). Carbon intensity of global crude oil refining and mitigation potential. Nature Climate Change, 10, 526–532. https://doi.org/10.1038/s41558-020-0775-3
- Klise, K., Nicholson, B., Laird, C., Ravikumar, A., & Brandt, A. (2020). Sensor Placement Optimization Software Applied to Site-Scale Methane-Emissions Monitoring. Journal of Environmental Engineering, 146(7). https://doi.org/10.1061/(ASCE)EE.1943-7870.0001737
- Masnadi, M., Perrier, P., Wang, J., Rutherford, J., & Brandt, A. (2020). Statistical proxy modeling for life cycle assessment and energetic analysis. Energy, 194, 116882. https://doi.org/10.1016/j.energy.2019.116882
- Ravikumar, A., Roda-Stuart, D., Liu, R., Bradley, A., Bergerson, J., Nie, Y., Zhang, S., Bi, X., & Brandt, A. (2020). Repeated leak detection and repair surveys reduce methane emissions over scale of years. Environmental Research Letters, 15(3), 034029. https://doi.org/10.1088/1748-9326/ab6ae1
- Wang, J., Tchapmi, L., Ravikumar, A., McGuire, M., Bell, C., Zimmerle, D., Savarese, S., & Brandt, A. (2020). Machine vision for natural gas methane emissions detection using an infrared camera. Applied Energy, 257, 113998. https://doi.org/10.1016/j.apenergy.2019.113998
- Venugopal, V., Sun, Y., & Brandt, A. (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, 066102 . https://doi.org/10.1063/1.5122796
- Levi, P., Kurland, S., Carbajales-Dale, M., Weyant, J., Brandt, A., & Benson, S. (2019). Macro-Energy Systems: Toward a New Discipline. Joule, 3(10), 2282-2286. https://doi.org/10.1016/j.joule.2019.07.017
- Ravikumar, A., Sreedhara, S., Wang, J., Englander, J., Roda-Stuart, D., Bell, C., Zimmerle, D., Lyon, D., Mogstad, I., Ratner, B., & Brandt, A. (2019). Single-blind inter-comparison of methane detectiontechnologies – results from the Stanford/EDF MobileMonitoring Challenge. Elementa: Science of the Anthropocene, 7(37). https://doi.org/10.1525/elementa.373
- Short-term solar power forecast with deep learning: Exploring optimal input and output configuration. (2019). Solar Energy, 188, 730-741. https://doi.org/10.1016/j.solener.2019.06.041
- Yuan, M., Teichgraeber, H., Wilcox, J., & Brandt, A. (2019). Design and operations optimization of membrane-based flexible carbon capture. International Journal of Greenhouse Gas Control, 84, 154-163. https://doi.org/10.1016/j.ijggc.2019.03.018
- Sun, Y., Venugopal, V., & Brandt, A. (2018). Convolutional Neural Network for Short-term Solar Panel Output Prediction. IEEE World Conference on Photovoltaic Energy Conversion (WCPEC), 7, 18288267. https://doi.org/10.1109/PVSC.2018.8547400
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