MATEC Web Conf.
Volume 282, 20194th Central European Symposium on Building Physics (CESBP 2019)
|Number of page(s)||6|
|Published online||06 September 2019|
Modelling the disaggregated demand for electricity at the level of residential buildings with the use of artificial neural networks (deep learning approach)
Łódź University of Technology, Faculty of Management and Production Engineering, 90924 Łódź, Piotrkowska 266, Poland
* Corresponding author: email@example.com
The paper addresses the issue of modelling the demand for electricity at the level of residential buildings with the use of artificial intelligence tools, namely artificial neural networks (ANN). The real data for six buildings acquired by measurement meters installed in them was used in the research. Their original frequency of 1 Hz has been resampled to a frequency of 1/600 Hz which corresponds to a period of 10 minutes. Out-of-sample forecasts verified the ability of ANN to disaggregate electricity usage for its specific applications. Four categories were distinguished, which were electricity consumption by: (i) fridge, (ii) washing machine, (iii) personal computer and (iv) freezer. Both standard ANNs with multilayer perceptron architecture and newer types of networks based on deep learning approach were used. The simulations included a total of over 10,000 ANNs differing, e.g. architecture, input variables, activation functions, their parameters, and training algorithms. The research confirmed the possibility of using ANN in modelling the disaggregation of electricity consumption and indicated the way of building a highly optimized model.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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