High-resolution electricity load profiles are often used for analysis-based decision makings, energy modelling, or other modelling tasks such as household electricity consumption. Smart meters have made it possible to store realtime data, where the data are being optimised by the demand response programs. This paper presents the household load profile method by using smart meter data in Indonesia, which is the case study of this work. An efficient load profile framework is proposed by defining the data selection, applying the interpolation technique and performing clustering of multiresolution time-series data. The clustering through Multiresolution time-series data is a detailed approach to identify the load profile comprehensively at all levels and aspects, include minute, hourly, daily and weekly resolutions in the weekdays and weekend. The mean, maximum, minimum and random patterns are presented in the results. The presented approach of multi-grained load profile analysis may identify the potential of energy savings. The potential of energy savings may support consumers' energy efficiency by adjusting the timing and amount of electricity use, and the utilities may improve better energy management by shifting the energy consumption from peak to non-peak hours.
|Title of host publication||Proceedings of 2018 2nd Borneo International Conference on Applied Mathematics and Engineering (BICAME)|
|Publication status||Published - 2018|