Portfolio item number 1
Short description of portfolio item number 1
Short description of portfolio item number 1
Short description of portfolio item number 2
Published in Journal 1, 2009
This paper is about the number 1. The number 2 is left for future work.
Recommended citation: Your Name, You. (2009). "Paper Title Number 1." Journal 1. 1(1). http://academicpages.github.io/files/paper1.pdf
Published in Journal 1, 2010
This paper is about the number 2. The number 3 is left for future work.
Recommended citation: Your Name, You. (2010). "Paper Title Number 2." Journal 1. 1(2). http://academicpages.github.io/files/paper2.pdf
Published in Journal 1, 2015
This paper is about the number 3. The number 4 is left for future work.
Recommended citation: Your Name, You. (2015). "Paper Title Number 3." Journal 1. 1(3). http://academicpages.github.io/files/paper3.pdf
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This was a panel discussion on the challenges and gaps which may occur or arise with energy disruptions. Energy disruptions may include climate change or new technologies and or regulations.
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This talk demonstrated a two-stage electricity planning approach. Building locations and merging algorithms were used to apporximate residential locations, used as demand nodes for network planning. Given residential nodes, electricity access metrics (low voltage wire length, medium voltage wire length, transformer density and the cost of electrification) are estimated for wards in Kenya. These metrics highlight opportunities for varying electricity technologies (solar, minigrid, full-scale grid).
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Won Best Poster Award
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This session highlighted impactful work at the intersection of climate change, electricity, and machine learning in a variety of African contexts. Topics addressed will include energy access, load forecasting, and data mining for electricity system data. Watch session HERE
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Presented our accepted paper at ACM COMPASS 2020.
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The webinar can be watched here
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Abstract: Recent work has demonstrated how one can use daytime satellite imagery to assist the prediction of individual residential building consumption levels upon connection. Using six years of longitudinal data of electricity consumption for a large cross-section of grid customers from Kenya, we apply convolutional neural networks (CNNs) to daytime satellite images to predict expected levels of residential electricity consumption for individual customers throughout the country.
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.