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Filters: Author is Ling Jin [Clear All Filters]
"Winners are not keepers: Characterizing household engagement, gains, and energy patterns in demand response using machine learning in the United States." Energy Research & Social Science 70 (2020)..
"Spillover as a cause of bias in baseline evaluation methods for demand response programs." Applied Energy 250 (2019) 344 - 357..
"Evaluating the Effects of Missing Values and Mixed Data Types on Social Sequence Clustering Using t-SNE Visualization." Journal of Data and Information Quality 11.2 (2019) 1 - 22..
"Describing the users: Understanding adoption of and interest in shared, electrified, and automated transportation in the San Francisco Bay Area." Transportation Research Part D: Transport and Environment 71 (2019) 283-301..
"Accelerating the Deployment of Anaerobic Digestion to Meet Zero Waste Goals." Environmental Science & Technology 52.23 (2018) 13663–13669..
"Time Will Tell: Using Smart Meter Time Series Data to Derive Household Features and Explain Heter ogeneity in Pricing Programs." 2016 ACEEE Summer Study on Energy Efficiency in Buildings 2016..
"Go for the Silver? Evidence from field studies quantifying the difference in evaluation results between “gold standard” randomized controlled trial methods versus quasi-experimental methods." 2016 ACEEE Summer Study on Energy Efficiency in Buildings 2016..
"Data quality challenges with missing values and mixed types in joint sequence analysis." 2017 IEEE International Conference on Big Data (Big Data)2017 IEEE International Conference on Big Data (Big Data). Boston, MA, USA: IEEE, 2017..