dc.creator | Kouziokas G.N. | en |
dc.date.accessioned | 2023-01-31T08:46:42Z | |
dc.date.available | 2023-01-31T08:46:42Z | |
dc.date.issued | 2019 | |
dc.identifier | 10.1109/PACET48583.2019.8956252 | |
dc.identifier.isbn | 9781728143606 | |
dc.identifier.uri | http://hdl.handle.net/11615/75465 | |
dc.description.abstract | The application of Long Short-Term Memory (LSTM) Deep Neural Networks has been increased the last years. This paper proposes a novel methodology based on a hybrid model using the Long Short-Term Memory (LSTM) Networks and the Particle Swarm Optimization (PSO) in energy appliances prediction in a low-energy house. The Particle Swarm Optimization was implemented in order to evaluate the feature importance of the energy related factors in the input vector and the LSTM Networks to perform the time series forecasting. The results have illustrated an improved accuracy compared to other machine learning techniques such as Support Vector Machines and Feedforward Neural Networks. © 2019 IEEE. | en |
dc.language.iso | en | en |
dc.source | 5th Panhellenic Conference on Electronics and Telecommunications, PACET 2019 | en |
dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078933159&doi=10.1109%2fPACET48583.2019.8956252&partnerID=40&md5=ee6f119c4dae2c3a6d4c591ac9db6366 | |
dc.subject | Brain | en |
dc.subject | Deep neural networks | en |
dc.subject | Feedforward neural networks | en |
dc.subject | Forecasting | en |
dc.subject | Low power electronics | en |
dc.subject | Particle swarm optimization (PSO) | en |
dc.subject | Support vector machines | en |
dc.subject | Energy prediction | en |
dc.subject | Hybrid model | en |
dc.subject | Low-energy house | en |
dc.subject | Machine learning techniques | en |
dc.subject | Novel methodology | en |
dc.subject | Related factors | en |
dc.subject | Short term memory | en |
dc.subject | Time series forecasting | en |
dc.subject | Long short-term memory | en |
dc.subject | Institute of Electrical and Electronics Engineers Inc. | en |
dc.title | Long Short-Term Memory (LSTM) Deep Neural Networks in Energy Appliances Prediction | en |
dc.type | conferenceItem | en |