Neural network based predictive control of personalized heating systems
Autor: | J.C.G. Verhaart, Katarina Katic, W Wim Zeiler, Rongling Li |
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Přispěvatelé: | Building Services |
Jazyk: | angličtina |
Rok vydání: | 2018 |
Předmět: |
Artificial neural network
Automatic control Mean squared error Computer science 020209 energy 02 engineering and technology 010501 environmental sciences Overfitting 01 natural sciences symbols.namesake Machine learning 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering Predictive control 0105 earth and related environmental sciences Civil and Structural Engineering Nonlinear autoregressive exogenous model business.industry Mechanical Engineering Pattern recognition Building and Construction Personalized heating Pearson product-moment correlation coefficient Model predictive control Autoregressive model NARX symbols Artificial intelligence business |
Zdroj: | Katić, K, Li, R, Verhaart, J & Zeiler, W 2018, ' Neural network based predictive control of personalized heating systems ', Energy and Buildings, vol. 174, pp. 199-213 . https://doi.org/10.1016/j.enbuild.2018.06.033 Energy and Buildings, 174, 199-213. Elsevier |
ISSN: | 0378-7788 |
Popis: | The aim of a personalized heating system is to provide a desirable microclimate for each individual when heating is needed. In this paper, we present a method based on machine learning algorithms for generation of predictive models for use in control of personalized heating systems. Data was collected from two individual test subjects in an experiment that consisted of 14 sessions per test subject with each session lasting 4 h. A dynamic recurrent nonlinear autoregressive neural network with exogenous inputs (NARX) was used for developing the models for the prediction of personalized heating settings. The models for subjects A and B were tested with the data that was not used in creating the neural network (unseen data) to evaluate the accuracy of the prediction. Trained NARX showed good performance when tested with the unseen data, with no sign of overfitting. For model A, the optimal network was with 12 hidden neurons with root mean square error equal to 0.043 and Pearson correlation coefficient equal to 0.994. The best result for model B was obtained with a neural network with 16 hidden neurons with root mean square error equal to 0.049 and Pearson correlation coefficient equal to 0.966. In addition to the neural network models, several other machine learning algorithms were tested. Furthermore, the models were on-line tested and the results showed that the test subjects were satisfied with the heating settings that were automatically controlled using the models. Tests with automatic control showed that both test subjects felt comfortable throughout the tests and test subjects expressed their satisfaction with the automatic control. |
Databáze: | OpenAIRE |
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