Ling Jin

TL;DR: In this paper, a survey of San Francisco Bay Area residents was conducted to analyze adoption patterns for shared mobility, electrified vehicle technologies, and vehicle automation, finding that ride-hailing and adaptive cruise control have penetrated the market more extensively than have electrified vehicles or car-sharing services.

TL;DR: This paper applies 11 clustering methods to daily residential meter data and evaluates their parameter settings and suitability based on 6 generic performance metrics and post-checking of resulting clusters to discover diverse daily load patterns among residential customers.

TL;DR: The results show that the c-tree approach differentiates households by their energy-use characteristics in a way that increases the spread in enrollment rates and critical peak reduction among household groups, compared with the spreads achieved via several conventional segmentation methods.

TL;DR: In this paper, the authors extended the existing empirical literature on the experiences of low-income customers exposed to critical peak pricing, and provided the first glimpses into the experiences for the elderly and those who reported being chronically ill.

TL;DR: It is found that the ability to overcome missing data problems is more difficult in the nominal domain than in the binary domain, and the usage of t-distributed stochastic neighborhood embedding is demonstrated to visually guide mitigation of such biases.