Modelling the disaggregated demand for electricity at the level of residential buildings with the use of artificial neural networks (deep learning approach)
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 researc...
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2019-01-01
|
| Series: | MATEC Web of Conferences |
| Online Access: | https://www.matec-conferences.org/articles/matecconf/pdf/2019/31/matecconf_cesbp2019_02077.pdf |
| _version_ | 1818920008528429056 |
|---|---|
| author | Jasiński Tomasz |
| author_facet | Jasiński Tomasz |
| author_sort | Jasiński Tomasz |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-20T01:14:55Z |
| format | Article |
| id | doaj.art-6d0d9496dd664de1902aec1996f10523 |
| institution | Directory Open Access Journal |
| issn | 2261-236X |
| language | English |
| last_indexed | 2024-12-20T01:14:55Z |
| publishDate | 2019-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | MATEC Web of Conferences |
| spelling | doaj.art-6d0d9496dd664de1902aec1996f105232022-12-21T19:58:37ZengEDP SciencesMATEC Web of Conferences2261-236X2019-01-012820207710.1051/matecconf/201928202077matecconf_cesbp2019_02077Modelling the disaggregated demand for electricity at the level of residential buildings with the use of artificial neural networks (deep learning approach)Jasiński Tomasz0Łódź University of Technology, Faculty of Management and Production EngineeringThe 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.https://www.matec-conferences.org/articles/matecconf/pdf/2019/31/matecconf_cesbp2019_02077.pdf |
| spellingShingle | Jasiński Tomasz Modelling the disaggregated demand for electricity at the level of residential buildings with the use of artificial neural networks (deep learning approach) MATEC Web of Conferences |
| title | Modelling the disaggregated demand for electricity at the level of residential buildings with the use of artificial neural networks (deep learning approach) |
| title_full | Modelling the disaggregated demand for electricity at the level of residential buildings with the use of artificial neural networks (deep learning approach) |
| title_fullStr | Modelling the disaggregated demand for electricity at the level of residential buildings with the use of artificial neural networks (deep learning approach) |
| title_full_unstemmed | Modelling the disaggregated demand for electricity at the level of residential buildings with the use of artificial neural networks (deep learning approach) |
| title_short | Modelling the disaggregated demand for electricity at the level of residential buildings with the use of artificial neural networks (deep learning approach) |
| title_sort | modelling the disaggregated demand for electricity at the level of residential buildings with the use of artificial neural networks deep learning approach |
| url | https://www.matec-conferences.org/articles/matecconf/pdf/2019/31/matecconf_cesbp2019_02077.pdf |
| work_keys_str_mv | AT jasinskitomasz modellingthedisaggregateddemandforelectricityatthelevelofresidentialbuildingswiththeuseofartificialneuralnetworksdeeplearningapproach |