Jungsuk Kwac's research while affiliated with Stanford University and other places

Publications (12)

Article
For timely and effective dispatch of demand response (DR) events, utilities require an efficient customer selection process that considers multiple factors, such as customer energy consumption patterns, customer compliance, and DR event time intervals. Moreover, customer targeting strategies may be different depending on the priority of each DR pro...
Article
After deploying a large number of smart meters, utilities are challenged with managing a massive set of interval energy consumption data and decoding the information into meaningful measures that can help them. These new tasks need a more detailed understanding of customers than was previously sought because customers vary widely in their usage, ne...
Article
Customer selection (or targeting) problem has been studied as a very important problem for a long time in the domain of sales and marketing. In the 2000s, as much more resources became available (e.g., various data sources, large databases, and improved computing power), new approaches based on data mining techniques have been tried. In this chapte...
Article
Selecting customers for demand response (DR) programs is challenging, and existing methodologies are hard to scale and poor in performance. The existing methods are limited by lack of temporal consumption information at the individual customer level. We propose a scalable methodology for DR program targeting utilizing novel data available from indi...
Conference Paper
In this paper, we present the Energy Visualization and Insight System for Demand Operations and Management platform (VISDOM), a collection of smart meter data analysis algorithms and visualization tools designed to address the challenge of interpreting patterns in energy data in support of research, utility energy efficiency and demand response pro...
Article
Sparse coding is a regularized least squares solution using the L1 or L0 constraint, based on the Euclidean distance between original and reconstructed signals with respect to a predefined dictionary. The Euclidean distance, however, is not a good metric for many feature descriptors, especially histogram features, e.g. many visual features includin...
Article
Full-text available
Selecting customers for demand response programs is challenging and existing methodologies are hard to scale and poor in performance. The existing methods were limited by lack of temporal consumption information at the individual customer level. We propose a scalable methodology for demand response targeting utilizing novel data available from smar...
Article
A resistive switching (RS) random access memory device with ZrO2-doped HfO2 exhibits better RS performance than that with pure HfO2. In particular, Ires, Vres, and Vset are reduced by approximately 58%, 38%, and 39%, respectively, when HfO2 is doped with ZrO2 (9 at. %). In addition, the ZrO2 doping in HfO2 makes the distribution of most parameters...
Article
Full-text available
This study explores, in the context of semi-autonomous driving, how the content of the verbalized message accompanying the car’s autonomous action affects the driver’s attitude and safety performance. Using a driving simulator with an auto-braking function, we tested different messages that provided advance explanation of the car’s imminent autonom...
Article
Full-text available
The increasing US deployment of residential advanced metering infrastructure (AMI) has made hourly energy consumption data widely available. Using CA smart meter data, we investigate a household electricity segmentation methodology that uses an encoding system with a pre-processed load shape dictionary. Structured approaches using features derived...
Conference Paper
Full-text available
We develop statistical techniques for analyzing the energy information in the 15-min and daily household electricity consumption data. The results provide a good understanding of how usage is affected by environmental, structural and customer features. The analytics yield productive results for a small region, and perform well in other areas and in...
Conference Paper
Full-text available
The drive towards more sustainable power supply systems has enabled significant growth of renewable generation. This in turn has pushed the rollout of demand response (DR) programs to address a larger population of consumers. Utilities are interested in enrolling small and medium sized customers that can provide demand curtailment during periods of...

Citations

... In this schemes, the consumers are awarded incentives for turning off certain loads or even interrupting their energy use in response to utility calls [143]. Curtailable load programs are applied to both medium and large consumers. ...
... This allows us to easily identify different patterns in food activities across clusters using representative food activity time-based profiles. Finally, clustering has also been used in applications for lifestyle segmentation using residential electricity consumption data to identify households for enrollment certain energy programs or interventions [57]. ...
... On the other hand, some works have focused on the analysis of data patterns through various statistical and machine learning techniques to show the consumption behavior of users. For example, in [3], work is shown to extract, visualize, and interpret the consumption data of residential customers; however, the HCI is little considered. In [24], a study of information services based on energy consumption for inhabitants of a smart home is shown. ...
... Thus, in these experiments, MED and MCD, respectively, correspond to Euclidean distance and cosine distance. Furthermore, due to vector normalisation applied for the resolution of sparse coding (Section 5.3), and because squared difference between two normalised vectors is proportional to the cosine distance [39], RSCR and MCD will produce the same ranking. Therefore, RSCR is not included in these experiments. ...
... Assessing DR potential is an essential prerequisite for estimating possible energy or power reduction so that DR proponents can target potential participants economically [7], design optimal incentive schemes [8], and implement optimal strategies [2,9]. In this context, estimating household energy use is a key requirement for DR program management and control techniques. ...
... [9]). While engaged in an NDRT, even if not necessary from a technical perspective, transparency about the system's driving behavior leads to a better user experience (UX) during an autonomous ride, increasing trust and acceptance [10,31,49]. Examples of this include highlighting detected objects in the scenery [6,7] or warning about potentially critical objects [57]. ...
... The success of such events is measured as the ratio between required target met versus achieved target level. Current event yields are low, in the range of 10% to 30% [12]. The success of the DR events is determined by the identification of consumers who are both likely to agree to reduce their consumption and use deferrable appliances during peak hours. ...
... However, the stochastic nature of CFs formation during device operation can hinder the large-scale commercial application of the RRAM device. So several process optimizations such as doped switching layers, metal electrode selection, bilayer switching layer structures, and metal nanocrystal incorporation are needed to control the CFs formation to improve the device-to-device and cycle-to-cycle variability [6][7][8][9][10][11][12][13]. Among different techniques, nanoparticles embedded oxide-based switching layers are examined extensively, influencing CFs formation and improving the resistive switching process. ...
... However, the increase of the number of vehicles connected to the grid has led to a challenging study with effect on quality and stability of the overall system. In Kwac and Rajagopal (2013), a methodology was developed for large-scale consumer targeting by combining BDA and scalable selection procedures via stochastic knapsack problem and demand response modeling. Accordingly, the fast heuristic algorithm has been considered to cope with computational issues resulting from the big volume of dataset. ...
... On the other hand, lower entropy users' with less variable consumption data is easy to predict and more suitable for direct load control and other incentive-based DSM programs. Using quarter-hourly electricity consumption data, Kwac et al. [163] developed statistical techniques through the measure of variability to identify small and large customer segments that can yield measurable results and high returns for energy programmes. It was discovered that an individual-level energy consumption forecast would be easier for stable customers having less variable load profiles as compared to unstable customers exhibiting highly variable load patterns. ...