Demand response (DR) programs encourage end-use customers to alter their power consumption in response to DR events such as change in real-time electricity prices. Facilitating household participation in DR programs is essential as the residential sector accounts for a sizable portion of the total energy consumed. However, manually tracking energy prices and deciding on how to schedule home appliances can be a challenge for residential consumers who are accustomed to fixed price electricity tariffs. In this work, we present Yupik, a system that helps users respond to realtime electricity prices while being sensitive to their context and lifestyle. Yupik combines sensing, analytics, and optimization to generate appliance usage schedules that may be used by households to minimize their energy bill as well as potential lifestyle disruptions. Yupik uses jPlugs, appliance level energy metering devices, to continuously monitor the power usage by various home appliances. The consumption patterns as well as data from external sources are analyzed using data mining algorithms to infer user's preferred usage profile. Using the preferred profile as a reference, Yupik's optimization engine generates multiple usage plans that attempt to minimize energy and inconvenience costs. Some of Yupik's capabilities are demonstrated with the help of preliminary data collected from a home that was instrumented with jPlugs to monitor the power usage of a few devices.
Document clustering requires a deep understanding of the complex structure of longtext; in particular, the intra-sentential (local) and inter-sentential features (global). Existing representation learning models do not fully capture these features. To address this, we present a novel graph-based representation for document clustering that builds a graph autoencoder (GAE) on a Keyword Correlation Graph. The graph is constructed with topical keywords as nodes and multiple local and global features as edges. A GAE is employed to aggregate the two sets of features by learning a latent representation which can jointly reconstruct them. Clustering is then performed on the learned representations, using vector dimensions as features for inducing document classes. Extensive experiments on two datasets show that the features learned by our approach can achieve better clustering performance than other existing features, including term frequency-inverse document frequency and average embedding.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.