Abstract
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.
Highlights
Worldwide energy consumption in the building sector accounts for 20–30% of major energy consumption
Under a 5% reduction ratio, since the building electricity consumption is lower than the target, the difference between the initial target and the adjustment target applied to the actual algorithm gradually increases over time, and the last algorithm time, 22:00–23:00, shows that the difference between targets is about 10 kWh
Under a 15% reduction ratio, since the building electricity consumption is lower than the target, the difference between the initial target and the adjustment target is applied, the actual algorithm gradually increases over time, and the last algorithm time, 22:00–23:00, shows that the difference between targets gradually increases over time; but after 18:00, the building electricity consumption is a higher target, so the difference between the targets narrows, and it can be seen that the last algorithm time, 22:00–23:00, has a smaller adjustment target than the initial target
Summary
Worldwide energy consumption in the building sector accounts for 20–30% of major energy consumption. Heating, ventilation, and air conditioning (HVAC) systems for comfort and lighting systems to maintain a bright interior account for about 50% of the total electricity consumption of a building [1]. More than 80% of the GHG (Greenhouse gas) emissions take place mainly from electricity consumption for HVAC, water heating, lighting, and entertainment during the building’s operation phase [2]. This means that reducing buildings’ electricity consumption is important. In order to systematically proceed with energy optimization control, it is necessary to predict building energy demand, analyze the condition of the building, and judge the situation
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