The Prediction of Building Energy Consumption Using Feature Selection and Ensemble Deep Learning with Improved Rafflesia Optimization Algorithm

Buildings emit a great deal of carbon dioxide and use a lot of energy. The study for predicting building energy consumption is useful for the sustainable development of multi-energy planning and energy-saving strategies. Therefore, improving the prediction of building energy consumption using feature selection and ensemble deep learning with the Rafflesia Optimization Algorithm (ROA) is proposed in this paper. This method can explore data on building energy usage, assess prediction accuracy, and address concerns that building energy usage research must address. The proposed model first uses an improved self-organizing map with a new neighborhood function to select important features. After that, it uses ensemble deep learning to anticipate the building’s energy usage accurately. In addition, the ROA is used to fine-tune parameters for feature selection and ensemble deep learning. This research uses the dataset of the American Society of Heating, Refrigerating, and Air Conditioning Engineers to compare the performance of several modeling approaches. It identifies the top five most important features based on the model’s results. Furthermore, the proposed model can also be successfully applied to a real-world application. Both datasets show the lowest RMSE and the highest R2 for our model. The proposed model indeed provides the benefits of feature selection and ensemble deep learning with the ROA for improving the prediction of building energy consumption.