Smart Building Cooling Load Prediction Based on GWO-CNN-LSTM
TL;DR
It can be concluded that this GWO-CNN-LSTM forecasting approach is relatively reliable and can provide data support for optimising AC system operation and promoting building energy conservation.
Abstract
To improve the energy-saving effect of buildings, we need to know how much electricity will be used in advance; that is, we should be able to accurately predict the electricity demand of different branches. However, due to the irregular and complex changes in the time of electricity demand, it is very difficult to accurately forecast the cooling load. Therefore, a combination of forecasting methods was used in this study to solve this problem, and the results are as follows: First, a detailed 3D model of the building is constructed to collect all kinds of data on cooling load, and then a new method integrating Grey Wolf Optimizer (GWO), Convolutional Neural Networks (CNN) and Long Short-Term Memory networks (LSTM) is trained and tested; this method has been named the GWO-CNN-LSTM model, and it can solve problems such as irregular timing and prediction difficulty. After training, a simple optimization algorithm is employed to automatically tune the hyper-parameters of the combined network and connects the feature extraction part with the memory function of the sequence processor; through this combination, the model can better seize not only the long-term trend but also a short-term fluctuations of load data, and it has performed reasonably well compared to previous reference models and generated slightly more accurate calculations; thus, it can be concluded that this GWO-CNN-LSTM forecasting approach is relatively reliable and can provide data support for optimising AC system operation and promoting building energy conservation.