A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Similarity measures and comparison methods for residential electricity load profiles
Abstract The simulation of residential electricity load profiles (ELPs) has always played an important role for designing and evaluating energy systems for buildings or entire neighborhoods. Large-scale measurement data, the counterpart to these synthetic data, are often not available or only available at great expense in terms of time and under consideration of data protection. Therefore, sometimes very detailed and elaborate load profile generators are created, which allow the simulation of different scenarios even without measured data. Simulating electricity load profileson a large scale on the one hand, and as detailed as possible on the other, is subject to several challenges. A particular challenge is the assessment of representativeness and the question of which measures are used to evaluate this. Specifically, which measures indicate whether the curve progression of a synthetic load curve becomes more similar to measured curves and when it does not. Electricity load profiles are highly complex structures that depend on numerous conditions. This paper aims to present an approach that addresses the issue of assessing the similarity or representativeness of electricity load profiles. Emphasis is placed on the comparative measures that are expected to indicate the representativeness or similarity between synthetic and measured electricity load profile data. In the first step, comparative measures used in the literature are gathered as well as classified with respect to their statement on the similarity of electricity load profiles. It is of essence, that similarity in this paper corresponds to the likeness and not the sameness of electricity load profile data. Adding to the measures found in the literature, three further similarity measures are introduced. Using measured electricity load profile data from a case study and synthetic electricity load profile data from three different load profile generators, selected similarity measures are calculated and compared. It is found that in addition to measures of position, central tendency and dispersion, the newly introduced complexity measures may substantiate the expressiveness with respect to the similarity of electricity load profiles. In particular, the complexity measure of the fractal dimension seems to be a potential for further similarity studies.
Similarity measures and comparison methods for residential electricity load profiles
Abstract The simulation of residential electricity load profiles (ELPs) has always played an important role for designing and evaluating energy systems for buildings or entire neighborhoods. Large-scale measurement data, the counterpart to these synthetic data, are often not available or only available at great expense in terms of time and under consideration of data protection. Therefore, sometimes very detailed and elaborate load profile generators are created, which allow the simulation of different scenarios even without measured data. Simulating electricity load profileson a large scale on the one hand, and as detailed as possible on the other, is subject to several challenges. A particular challenge is the assessment of representativeness and the question of which measures are used to evaluate this. Specifically, which measures indicate whether the curve progression of a synthetic load curve becomes more similar to measured curves and when it does not. Electricity load profiles are highly complex structures that depend on numerous conditions. This paper aims to present an approach that addresses the issue of assessing the similarity or representativeness of electricity load profiles. Emphasis is placed on the comparative measures that are expected to indicate the representativeness or similarity between synthetic and measured electricity load profile data. In the first step, comparative measures used in the literature are gathered as well as classified with respect to their statement on the similarity of electricity load profiles. It is of essence, that similarity in this paper corresponds to the likeness and not the sameness of electricity load profile data. Adding to the measures found in the literature, three further similarity measures are introduced. Using measured electricity load profile data from a case study and synthetic electricity load profile data from three different load profile generators, selected similarity measures are calculated and compared. It is found that in addition to measures of position, central tendency and dispersion, the newly introduced complexity measures may substantiate the expressiveness with respect to the similarity of electricity load profiles. In particular, the complexity measure of the fractal dimension seems to be a potential for further similarity studies.
Similarity measures and comparison methods for residential electricity load profiles
Köhler, Sally (author) / Rongstock, Ruben (author) / Hein, Martin (author) / Eicker, Ursula (author)
Energy and Buildings ; 271
2022-07-19
Article (Journal)
Electronic Resource
English
Electricity load simulation , Load profile generators , Representativeness of synthetic load profiles , Measures for likeness and sameness , Fractal dimension , ALKIS , Official Real Estate Cadaster Information System (German) , CHP , Combined heat and power , DSM , Demand side management , ECDF , Empirical cumulative distribution function (summer/winter) , EDR , Edit Distance for Real Sequences , ELP , Electricity load profile , ERP , Edit Distance with Real Penalty , EV , Electric vehicles , FD , FDD , Frequency density distribution , GML , Geography Markup Language , IWU , Institut Wohnen und Umwelt , IZES , Institut für Zukunftsenergie und Stoffstromsysteme , KS , Kolmogorov–Smirnov , LCSS , Longest Common Subsequence distance , LDC , Load duration curve , LPG , Load profile generator , LV , Low voltage , MAE , Mean absolute error , MAPE , Mean absolute percentage error , MPE , Mean percentage error , MFH , Multi family house , NRMSE , Normalized Root Mean Square Error , PAR , Peak-to-average ratio , PDF , Probability density function , PL , Programming language , PLPG , Load profile generator by Pflugradt , PV , Photovoltaics , RMSE , Root Mean Square Error , SD , Standard deviation , SFH , Single family house , SLP , Synthetic load profile , SOM , Self Organizing Map , TH , Terraced house , ToU , Time of use
Data-driven load profiles and the dynamics of residential electricity consumption
| BASE | 2022
User-Centered Nonintrusive Electricity Load Monitoring for Residential Buildings
| Online Contents | 2011
User-Centered Nonintrusive Electricity Load Monitoring for Residential Buildings
| British Library Online Contents | 2011