Suchergebnisse

[EN] One of the main difficulties encountered when designing automatic tools for water end use identification is the inherent noise present in recorded flow traces. Noise is mainly caused by the inability of the monitoring equipment to accurately register water consumption and data-loggers to register, without distortion, the signal received from the water meter. A universal filtering algorithm has been developed to remove noise and simplify water consumption flow traces with the aim of improving future automatic end use identification algorithms. The performance of the proposed filtering methodology is assessed through the analysis of 21,647 events. Water consumption data were sourced from two different water end use studies, having consumers and monitoring equipment with dissimilar characteristics. The results obtained show that the algorithm is capable of removing an average of 70% of the data points that constitute the flow traces of the complex events examined. The simplified flow traces allow for faster and more accurate disaggregation and classification algorithms, without losing significant information or distorting the original signal. The ability of the proposed filtering algorithm to fit the original flow traces was benchmarked using the Kling-Gupta efficiency coefficient, obtaining an average value above 0.79. ; This study has received funding by the IMPADAPT project/CGL2013-48424-C2-1-R from the Spanish ministry MINECO with European FEDER funds and from the European Union's Seventh Framework Programme (FP7/2007e2013) under grant agreement no. 619172 (SmartH2O: an ICT Platform to Leverage on Social Computing for the Efficient Management of Water Consumption). ; Pastor-Jabaloyes, L.; Arregui De La Cruz, F.; Cobacho Jordán, R. (2018). A filtering algorithm for high-resolution flow traces to improve water end-use analysis. Water Science & Technology: Water Supply. 19(2):451-462. https://doi.org/10.2166/ws.2018.090 ; S ; 451 ; 462 ; 19 ; 2

[EN] Disaggregating residential water end use events through the available commercial tools needs a great investment in time to manually process smart metering data. Therefore, it is extremely difficult to achieve a homogenous and sufficiently large corpus of classified single-use events capable of accurately describe residential water consumption. The main goal of the present paper is to develop an automatic tool that facilitates the disaggregation of the individual water consumptions events from the raw flow trace. The proposed disaggregation methodology is conducted through two actions that are iteratively performed: first, the use of an advanced two-step filter, whose calibration is automatically conducted by the Elitist Non-Dominated Sorting Genetic Algorithm NSGA-II; and second, a cropping algorithm based on the filtered water consumption flow traces. As a secondary goal, yet complementary to the main one, a semiautomatic massive classification process has been developed, so that the resulting single-use events can be easily categorized in the different water end uses in a household. This methodology was tested using water consumption data from two different case studies. The characteristics of the households taken as reference and their occupants were unequivocally dissimilar from each other. In addition, the monitoring equipment used to obtain the consumption flow traces had completely different technical specifications. The results obtained from the processing of the two studies show that the automatic disaggregation is both robust and accurate, and produces significant time saving compared to the standard manual analysis. ; This study has received funding by the IMPADAPT project /CGL2013-48424-C2-1-R from the Spanish ministry MINECO with European FEDER funds and from the European Union's Seventh Framework Programme (FP7/2007-2013) under grant agreement No. 619172 (SmartH2O: an ICT Platform to leverage on Social Computing for the efficient management of Water Consumption). ; Pastor-Jabaloyes, L.; ...

451 462 19 2 ; S ; [EN] One of the main difficulties encountered when designing automatic tools for water end use identification is the inherent noise present in recorded flow traces. Noise is mainly caused by the inability of the monitoring equipment to accurately register water consumption and data-loggers to register, without distortion, the signal received from the water meter. A universal filtering algorithm has been developed to remove noise and simplify water consumption flow traces with the aim of improving future automatic end use identification algorithms. The performance of the proposed filtering methodology is assessed through the analysis of 21,647 events. Water consumption data were sourced from two different water end use studies, having consumers and monitoring equipment with dissimilar characteristics. The results obtained show that the algorithm is capable of removing an average of 70% of the data points that constitute the flow traces of the complex events examined. The simplified flow traces allow for faster and more accurate disaggregation and classification algorithms, without losing significant information or distorting the original signal. The ability of the proposed filtering algorithm to fit the original flow traces was benchmarked using the Kling-Gupta efficiency coefficient, obtaining an average value above 0.79. This study has received funding by the IMPADAPT project/CGL2013-48424-C2-1-R from the Spanish ministry MINECO with European FEDER funds and from the European Union's Seventh Framework Programme (FP7/2007e2013) under grant agreement no. 619172 (SmartH2O: an ICT Platform to Leverage on Social Computing for the Efficient Management of Water Consumption). Pastor-Jabaloyes, L.; Arregui De La Cruz, F.; Cobacho Jordán, R. (2018). A filtering algorithm for high-resolution flow traces to improve water end-use analysis. Water Science & Technology: Water Supply. 19(2):451-462. https://doi.org/10.2166/ws.2018.090