Author name cluster

Liangda Li

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

4 papers

1 author row

TIST Journal 2018 Journal Article

Energy Usage Behavior Modeling in Energy Disaggregation via Hawkes Processes

Liangda Li
Hongyuan Zha

Energy disaggregation, the task of taking a whole home electricity signal and decomposing it into its component appliances, has been proved to be essential in energy conservation research. One powerful cue for breaking down the entire household’s energy consumption is user’s daily energy usage behavior, which has so far received little attention: existing works on energy disaggregation mostly ignored the relationship between the energy usages of various appliances by householders across different time slots. The major challenge in modeling such a relationship in that, with ambiguous appliance usage membership of householders, we find it difficult to appropriately model the influence between appliances, since such influence is determined by human behaviors in energy usage. To address this problem, we propose to model the influence between householders’ energy usage behaviors directly through a novel probabilistic model, which combines topic models with the Hawkes processes. The proposed model simultaneously disaggregates the whole home electricity signal into each component appliance and infers the appliance usage membership of household members and enables those two tasks to mutually benefit each other. Experimental results on both synthetic data and four real-world data sets demonstrate the effectiveness of our model, which outperforms state-of-the-art approaches in not only decomposing the entire consumed energy to each appliance in houses but also the inference of household structures. We further analyze the inferred appliance-householder assignment and the corresponding influence within the appliance usage of each householder and across different householders, which provides insight into appealing human behavior patterns in appliance usage.

Details DOI

IJCAI Conference 2016 Conference Paper

Household Structure Analysis via Hawkes Processes for Enhancing Energy Disaggregation

Liangda Li
Hongyuan Zha

In energy conservation research, energy disaggregation becomes an increasingly critical task, which takes a whole home electricity signal and decomposes it into its component appliances. While householder's daily energy usage behavior acts as one powerful cue for breaking down the entire household's energy consumption, existing works rarely modeled it straightforwardly. Instead, they either ignored the influence between users' energy usage behaviors, or modeled the influence between the energy usages of appliances. With ambiguous appliance usage membership of householders, we find it difficult to appropriately model the influence between appliances, since such influence is determined by human behaviors in energy usage. To address this problem, we propose to model the influence between householders' energy usage behaviors directly through a novel probabilistic model, which combines topic models with the Hawkes processes. The proposed model simultaneously disaggregates the whole home electricity signal into each component appliance and infers the appliance usage membership of household members, and enables those two tasks mutually benefit each other. Experimental results on both synthetic data and four real world data sets demonstrate the effectiveness of our model, which outperforms state-of-the-art approaches in not only decomposing the entire consumed energy to each appliance in houses, but also the inference of household structures. We further analyze the inferred appliance-householder assignment and the corresponding influence within the appliance usage of each householder and across different householders, which provides insight into appealing human behavior patterns in appliance usage.

PDF Details

AAAI Conference 2015 Conference Paper

Energy Usage Behavior Modeling in Energy Disaggregation via Marked Hawkes Process

Liangda Li
Hongyuan Zha

Energy disaggregation, the task of taking a whole home electricity signal and decomposing it into its component appliances, has been proved to be essential in energy conservation research. One powerful cue for breaking down the entire household’s energy consumption is user’s daily energy usage behavior, which has so far received little attention: existing works on energy disaggregation mostly ignored the relationship between the energy usages of various appliances across different time slots. To model such relationship, we combine topic models with Hawkes processes, and propose a novel probabilistic model based on marked Hawkes process that enables the modeling of marked event data. The proposed model seeks to capture the influence from the occurrence and the marks of one usage event to the occurrence and the marks of subsequent usage events in the future. We also develop an inference algorithm based on variational inference for model parameter estimation. Experimental results on both synthetic data and three real world data sets demonstrate the effectiveness of our model, which outperforms state-of-the-art approaches in decomposing the entire consumed energy to each appliance. Analyzing the influence captured by the proposed model provides further insights into numerous interesting energy usage behavior patterns.

PDF Details

AAAI Conference 2014 Conference Paper

Learning Parametric Models for Social Infectivity in Multi-Dimensional Hawkes Processes

Liangda Li
Hongyuan Zha

Efficient and effective learning of social infectivity presents a critical challenge in modeling diffusion phenomena in social networks and other applications. Existing methods require substantial amount of event cascades to guarantee the learning accuracy and they only consider time-invariant infectivity. Our paper overcomes those two drawbacks by constructing a more compact model and parameterizing the infectivity using time-varying features, thus dramatically reduces the data requirement, and enables the learning of timevarying infectivity which also takes into account the underlying network topology. We replace the pairwise infectivity in the multidimensional Hawkes processes with linear combinations of those time-varying features, and optimize the associated coefficients with lasso-type of regularization. To efficiently solve the resulting optimization problem, we employ the technique of alternating direction method of multipliers which allows independent updating of the individual coefficients by optimizing a surrogate function upper-bounding the original objective function. On both synthetic and real world data, the proposed method performs better than alternatives in terms of both recovering the hidden diffusion network and predicting the occurrence time of social events.

PDF Details