Minimization Of Natural Gas Consumption Of Domestic Boilers With Convolutional, Long-Short Term Memory Neural Networks And Genetic Algorithm

This paper presents a novel approach that aims to minimize the natural gas consumption of domestic boilers for heating purposes, while not compromising the user's heating needs. The system utilizes gas consumption data, the conditions both inside and outside the house collected via interconnected commercial thermostats, and the heating needs of the users. The architecture of the presented system is divided in two main coordinated processes: (a) the first one consists of two Neural Networks with Convolutional and Long Short-Term Memory layers, used to predict the indoor temperature and the boiler's modulation (load percentage), whereas (b) the second process includes a Genetic Algorithm used to determine the optimal operation conditions of the boiler, by finding the boiler control instructions that meet the user's heating preferences concerning the target indoor temperature, while minimizing the total gas consumption. One main advantage of the solution is its ability to consider boilers as a black box, since it does not need to be aware of the internal mechanics. In this way, the proposed methodology can be applied to a wide range of domestic gas boilers with minimum adjustments. The overall methodology is applied to four domestic boilers in Greece spanning three cities, to capture different climatic conditions and evaluate the system performance in varying outdoor conditions. The attained results indicate that the proposed system can lead to significant gas consumption reduction through autonomously created optimal control instructions provided to the boiler.