| Issue |
E3S Web Conf.
Volume 49, 2018
SOLINA 2018 - VII Conference SOLINA Sustainable Development: Architecture - Building Construction - Environmental Engineering and Protection Innovative Energy-Efficient Technologies - Utilization of Renewable Energy Sources
|
|
|---|---|---|
| Article Number | 00048 | |
| Number of page(s) | 8 | |
| DOI | https://doi.org/10.1051/e3sconf/20184900048 | |
| Published online | 13 August 2018 | |
Modelling of electricity demand in residential buildings using artificial neural networks
Lodz University of Technology, Faculty of Management and Production Engineering, 90924 Lodz, Poland
* Corresponding author: tomasz.jasinski@p.lodz.pl
Electricity is the basis for the functioning of modern society. It is used for many purposes, including HVAC systems. Information on future electricity demand is an important element from the point of view of both the real estate user and other entities on the energy market. The study forecasts the demand for electricity on the basis of data from over 12,000 buildings. The model was created using one of the tools from the area of artificial intelligence - neural networks. Over 15,000 networks differing in architecture, number of nerve cells, activation functions, sets of explanatory variables and learning algorithms have been tested. The paper presents those from the tested models, which were characterized by the highest precision of operation.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.