Energy consumption prediction in urban areas for the smart city ecosystem

A. O. Belyaev; A. S. Boldyrev; E. B. Gorbunova; Evgeniy S. Sinutin; S. A. Sinutin

doi:10.1117/12.2517153

25 October 2018 Energy consumption prediction in urban areas for the smart city ecosystem

A. O. Belyaev, A. S. Boldyrev, E. B. Gorbunova , Evgeniy S. Sinutin , S. A. Sinutin

Proceedings Volume 10793, Remote Sensing Technologies and Applications in Urban Environments III; 107931D (2018) https://doi.org/10.1117/12.2517153
Event: SPIE Remote Sensing, 2018, Berlin, Germany

Abstract

It is nowadays a worldwide trend to implement the so-called digital power industry, which implies optimization of the energy resources dissemination and use, based on modern digital technologies and telecommunication systems. Control systems of this kind must take into account clearly inevident trends in changing energy consumption depending on the time of year, time of day, day of the week, etc. Since the end user is a specific person with his own individual preferences, the task of modeling his behavior in terms of energy consumption is an estimate of some realization of a random process. This process features a quasi-deterministic component and a pronounced random component. The goal of almost all digital power systems is to predict the trends in the quasi-deterministic component, the random component in this case is noise interference. The most appropriate solution when constructing a predictive system is to conduct a digital experiment on an array of data taken during the actual operation of a particular energy consumption accounting system.

The research was conducted on a real array of data from energy consumption by 61 households over a period of 730 days. Classical regression analysis methods were compared with neural network analysis (trained with teacher).

Citation Download Citation

A. O. Belyaev, A. S. Boldyrev, E. B. Gorbunova , Evgeniy S. Sinutin , and S. A. Sinutin "Energy consumption prediction in urban areas for the smart city ecosystem", Proc. SPIE 10793, Remote Sensing Technologies and Applications in Urban Environments III, 107931D (25 October 2018); https://doi.org/10.1117/12.2517153

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