Keywords
Energy and water disaggregation; End-use characterisation; Convex Optimization.
Location
Session C5: ICT for Energy and Water Demand Management
Start Date
13-7-2016 9:30 AM
End Date
13-7-2016 9:50 AM
Abstract
Monitoring the household end-use energy and water consumption is essential to design efficient resource management programs and to provide personalized feedback to consumers, so that: (i) users are aware of how much energy/water each appliance is consuming, and personalized hints for reducing their consumption can be given; (ii) household’s occupants can be informed on potential savings in deferring the use of some appliances to off-peak hours; (iii) water utilities can test the impact of different management strategies. In this work, we present a Java Web Service implementation of a recently-developed algorithm for decomposing the aggregate (energy or water) household consumption data collected from a single measurement point into device-level consumption estimations. The proposed algorithm is based on the assumption that the disaggregated signals to be estimated are piecewise constant over the time and it exploits the information on the time-of-day probability in which a specific energy/water use event might occur. The disaggregation problem is formulated as a convex optimization problem, and a numerically efficient interior-point method is implemented to compute the solution of the formulated convex optimization problem. The proposed disaggregation algorithm has been initially tested against household electricity data available in the literature. The obtained results look promising and similar results are expected to be obtained for water data. The developed Java Web Service is a component of an ICT platform currently under development in the SmartH2O project, an European-Union funded project which aims at raising consumers’ awareness about their consumption and pursue water savings in the residential sector, thanks to the integrated use of smart meters, social computation, and dynamic water pricing, based on advanced models of consumer behavior.
Included in
Civil Engineering Commons, Data Storage Systems Commons, Environmental Engineering Commons, Hydraulic Engineering Commons, Other Civil and Environmental Engineering Commons
Online processing of energy and water consumption data to deliver end use characterisation
Session C5: ICT for Energy and Water Demand Management
Monitoring the household end-use energy and water consumption is essential to design efficient resource management programs and to provide personalized feedback to consumers, so that: (i) users are aware of how much energy/water each appliance is consuming, and personalized hints for reducing their consumption can be given; (ii) household’s occupants can be informed on potential savings in deferring the use of some appliances to off-peak hours; (iii) water utilities can test the impact of different management strategies. In this work, we present a Java Web Service implementation of a recently-developed algorithm for decomposing the aggregate (energy or water) household consumption data collected from a single measurement point into device-level consumption estimations. The proposed algorithm is based on the assumption that the disaggregated signals to be estimated are piecewise constant over the time and it exploits the information on the time-of-day probability in which a specific energy/water use event might occur. The disaggregation problem is formulated as a convex optimization problem, and a numerically efficient interior-point method is implemented to compute the solution of the formulated convex optimization problem. The proposed disaggregation algorithm has been initially tested against household electricity data available in the literature. The obtained results look promising and similar results are expected to be obtained for water data. The developed Java Web Service is a component of an ICT platform currently under development in the SmartH2O project, an European-Union funded project which aims at raising consumers’ awareness about their consumption and pursue water savings in the residential sector, thanks to the integrated use of smart meters, social computation, and dynamic water pricing, based on advanced models of consumer behavior.
