Neural network based predictive control of personalized heating systems

Katarina Katić,Rongling Li,Jacob Verhaart,Wim Zeiler

doi:10.1016/j.enbuild.2018.06.033

Katarina Katić, Rongling Li + Show 2 more

Open Access

https://doi.org/10.1016/j.enbuild.2018.06.033

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Abstract

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.

Highlights

Individualized conditioning systems in commercial buildings are able to provide an improvement in the thermal comfort of occupants while reducing energy consumption [1,2,3,4,5]
Data collection is the first step in developing the prediction model and demonstration of the feasibility of the machine learning model to predict the settings of the personalized heating system
We demonstrated how to use machine learning can be successfully used for the control of personalized heating systems

Summary

Introduction

Individualized conditioning systems in commercial buildings are able to provide an improvement in the thermal comfort of occupants while reducing energy consumption [1,2,3,4,5]. Building occupants have a different perception of the thermal environment and what they perceive as a comfortable environment differs due to individual differences (e.g. gender, age, body composition) [6,7]. Previous studies showed that individuals with different body composition react differently to the same thermal environment [8,9,10,11]. Personalized local conditioning systems provide the option that every user can create their own environment based on their individual comfort requirements and preferences. Personal conditioning systems are mostly user controlled where users determine the heating or cooling setting of the system at any given time [13]

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