Neural network based predictive control of personalized heating systems

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

Personal heating: effectiveness and energy use

Buildings use approximately 40% of primary energy with most energy expended on the provision of a comfortable indoor climate. An extended range of indoor temperatures can significantly reduce the energy load. However, lower temperature set points for heating can cause thermal discomfort. Giving building occupants the option to warm themselves (e.g. a local source at their desk or workstation) can mitigate this discomfort by the provision of a personalized conditioning system. A model is presented to assess the performance of personalized heating and its impact on the whole building energy load. Researchers, designers and facility managers can use this model to compare performance and analyse energy savings. The total energy use of personalized heating is estimated by scaling its settings to the actual level of discomfort resulting from a lowered heating set point. This model is used to assess seven different personalized heating systems. Assessments reveal that personalized heating brings a remarkable energy-saving potential, while maintaining or even improving individually perceived thermal comfort. Assessments are based on an assumed linear relation between the power and level of increased thermal sensation. Future research in personalized conditioning systems should be directed towards the development of the full characteristics and specific settings

Personal heating: effectiveness and energy use

Buildings use approximately 40% of primary energy with most energy expended on the provision of a comfortable indoor climate. An extended range of indoor temperatures can significantly reduce the energy load. However, lower temperature set points for heating can cause thermal discomfort. Giving building occupants the option to warm themselves (e.g. a local source at their desk or workstation) can mitigate this discomfort by the provision of a personalized conditioning system. A model is presented to assess the performance of personalized heating and its impact on the whole building energy load. Researchers, designers and facility managers can use this model to compare performance and analyse energy savings. The total energy use of personalized heating is estimated by scaling its settings to the actual level of discomfort resulting from a lowered heating set point. This model is used to assess seven different personalized heating systems. Assessments reveal that personalized heating brings a remarkable energy-saving potential, while maintaining or even improving individually perceived thermal comfort. Assessments are based on an assumed linear relation between the power and level of increased thermal sensation. Future research in personalized conditioning systems should be directed towards the development of the full characteristics and specific settings

User interaction patterns of a personal cooling system:a measurement study

\u3cp\u3ePersonal cooling systems provide cooling for individual office occupants to maintain thermal comfort at their workplace when cooling is needed. The indoor temperature of the office can be maintained at several degrees higher than is customary in offices today when personal cooling is available, which results in energy saving for office buildings as a whole. To better understand the individual cooling demand of building occupants and develop good control strategies for personal cooling systems, it is necessary to assess the interaction between the user and the personal cooling system. For this purpose, a personal cooling system was tested in a stable, slightly warm environment (27.5°C) in a climate chamber with 11 human subjects. The personal cooling system was controlled by the subject using a simple slider. The interaction of the user with the system was related to comfort level and perceived air quality. The subjects are categorized into groups based on gender, on comfort level, and on whether their comfort improved during the test or not. The results show that comfort level did not directly reflect in a difference in the number of interactions or level of the setting. The largest difference in setting was found between male and female subjects, where females required less cooling.\u3c/p\u3

Neural network based predictive control of personalized heating systems

\u3cp\u3eThe 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.\u3c/p\u3

A measurement setup to test instruments for detecting sweat

The thermal neutral zone is the temperature range in which the body can manipulate the heat balance of the body using only vasomotion to maintain thermal comfort. In a warm environment, vasodilation will allow blood and heat to spread over a larger surface area, increasing the heat loss through passive means. When the heat loss needs to be increased further, people start sweating and the heat is lost by evaporating moisture from the skin. Sweat can be used to \u3cbr/\u3emonitor the thermoregulatory response of users and test subjects. This is similar to skin temperatures. Detection of the onset of sweating is an indicator for the upper boundary of the thermal neutral zone. Up until now, detecting vasomotion, sweat and sweat rate requires highly controlled conditions and complicated instruments. There is a need to develop instruments that can be used in an office environment during field tests for the development of personal \u3cbr/\u3econditioning systems and future climate control systems. In this study, a test set up is build that can mimic the human skin with respect to temperature and sweat rate. Based on the results of the study suggestions are given for the further improvements and measurements on the human body