Development and Validation of Building Control Algorithm Energy Management

In this paper, a building control algorithm is proposed to reduce the electricity consumption of a building with a variable refrigerant flow (VRF) system. The algorithm uses sequence-to-sequence long short_term memory (seq2seq LSTM) to set target electricity consumption, and uses a VRF air conditioner system to reduce electricity consumption. After setting target electricity consumption, the algorithm is applied as a method of updating target electricity consumption. In addition, we propose two methods to increase the performance of the seq2seq LSTM model. First, among the feature selection methods, random forest is used to select, among the numerous features of the data, only those features that are most relevant to the predicted value. Second, we use Bayesian optimization, which selects the optimal hyperparameter that shows the best model performance. In order to control the air conditioners, the priority of air conditioners is designated, the method of prioritization being the analytical hierarchy process (AHP). In this study, comparison of the performance of seq2seq LSTM model with and without Bayesian optimization proved that the use of Bayesian optimization achieved good performance. Simulation and demonstration experiments using the algorithm were also conducted, and showed that building electricity consumption decreased in a similar manner to the reduction rate by means of the algorithm.

This creative work has been published under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license which allows copying, and redistribution as well as adaptation of the original work provided appropriate credit is given to the original author and the conditions of the license are met.