| Title |
Energy consumption prediction in urban areas for the smart city ecosystem |
| ID_Doc |
38074 |
| Authors |
Belyaev, AO; Boldyrev, AS; Gorbunova, EB; Sinutin, ES; Sinutin, SA |
| Title |
Energy consumption prediction in urban areas for the smart city ecosystem |
| Year |
2018 |
| Published |
|
| DOI |
10.1117/12.2517153 |
| 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). |
| Author Keywords |
energy consumption forecasting; recurrent neural network; data analysis; regression; smart city |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:000453076800027 |
| WoS Category |
Environmental Sciences; Remote Sensing; Optics |
| Research Area |
Environmental Sciences & Ecology; Remote Sensing; Optics |
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