Tri Kurniawan Wijaya

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With an ever growing population, global energy demand is predicted to keep increasing. Furthermore, the integration of renewable energy sources into the electricity grid (to reduce carbon emission and humanity's dependency on fossil fuels), complicates eff ...

Generalized Additive Models (GAM) are a widely popular class of regression models to forecast electricity demand, due to their high accuracy, flexibility and interpretability. However, the residuals of the fitted GAM are typically heteroscedastic and lepto ...

Schema and ontology matching is a process of establishing correspondences between schema attributes and ontology concepts, for the purpose of data integration. Various commercial and academic tools have been developed to support this task. These tools prov ...

Smart sensors, such as smart meters or smart phones, are nowadays ubiquitous. To be "smart", however, they need to process their input data with limited storage and computational resources. In this paper, we convert the stream of sensor data into a stream ...

Demand Response (DR) is a mechanism in which electricity consumers alter their demand in response to power grid’s supply and economic conditions. DR programs have the potential to improve resource efficiency, sustainability, grid reliability and economic v ...

Demand response (DR) is a Smart Grid application that aims at managing consumption of electricity in response to supply-side signals. The exchange of sensitive information (i.e., electricity consumption data) between the consumers and the DR provider is ne ...

Complex activities are activities that are a combination of many simple ones. Typically, activities of daily living (ADLs) fall in this category. Complex activity recognition is an active area of interest amongst sensing and knowledge mining community toda ...

Periods of peak consumer demand in today’s electricity sector are expensive to satisfy and can be the source of power failures. One possible solution is the use of demand-side management (DSM) applying dynamic pricing mechanisms. However, instead of reduci ...

Recent work has suggested reducing electricity generation cost by cutting the peak to average ratio (PAR) without reducing the total amount of the loads. However, most of these proposals rely on consumer's willingness to act. In this paper, we propose an a ...

Currently smart meter data analytics has received enormous attention because it allows utility companies to analyze customer consumption behavior in real time. However, the amount of data generated by these sensors is very large. As a result, analytics per ...

The recent development of smart meters has allowed the analysis of household electricity consumption in real time. Predicting electricity consumption at such very low scales should help to increase the efficiency of distribution networks and energy pricing ...

Demand response (DR) emerges as one of the cheapest and greenest solutions to match supply and demand in the electricity sector. While DR has been focused on large and industrial consumers, pervasive implementation (by including residential consumers) is n ...

Demand response (DR) has been known to play an important role in the electricity sector to balance supply and demand. To this end, the DR baseline is a key factor in a successful DR program since it influences the incentive allocation mechanism and custome ...

While electricity demand forecasting literature has focused on large, industrial, and national demand, this paper focuses on short-term (1 and 24 hour ahead) electricity demand forecasting for residential customers at the individual and aggregate level. Si ...

The ability of smart meters to communicate energy consumption data in (near) real-time enables data analytics for novel applications, such as pervasive demand response, personalized energy feedback, outage management, and theft detection. Smart meter data ...

Schema matching supports data integration by establishing correspondences between the attributes of independently designed database schemas. In recent years, various tools for automatic pair-wise matching of schemas have been developed. Since the matching ...

Tri Kurniawan Wijaya

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Pervasive Data Analytics for Sustainable Energy Systems

Forecasting Uncertainty in Electricity Demand

Minimizing Human Effort in Reconciling Match Networks

Online Unsupervised State Recognition in Sensor Data

DRSim: A Cyber Physical Simulator for Demand Response Systems

Privacy Enhanced Demand Response with Reputation-based Incentive Distribution

Mining Complex Activities in the Wild via a Single Smartphone Accelerometer

Effective Consumption Scheduling for Demand-Side Management in the Smart Grid using Non-Uniform Participation Rate

Matching Demand with Supply in the Smart Grid using Agent-Based Multiunit Auction

Symbolic Representation of Smart Meter Data

Electricity Load Forecasting for Residential Customers: Exploiting Aggregation and Correlation between Households

An Economic Analysis of Pervasive, Incentive-Based Demand Response

When Bias Matters: An Economic Assessment of Demand Response Baselines for Residential Customers

Cluster-based Aggregate Forecasting for Residential Electricity Demand using Smart Meter Data

SmartD: Smart Meter Data Analytics Dashboard

SMART: A tool for analyzing and reconciling schema matching networks