Research on energy consumption prediction based on fast attribute reduction of weighted neighborhood rough set with moving horizon
Jun Tan, Qun Hou, Xin Liu, Yunke Xiong · 2023
In actual prediction scenarios, attribute features include time data, weather data, and energy consumption data. The relationship between attribute features is very complex. Exploring the relationship between feature attributes and decision sets with fast attribute reduction can reduce the amount of model training data. Since seasonal and temporal variations greatly influence weather and energy consumption data, the moving horizon method is used to update the features and improve the accuracy of energy consumption prediction.From above, an energy consumption prediction model based on fast attribute reduction of weighted neighborhood rough set with moving horizon long short-term memory neural network(LSTM) is proposed in this paper. In the experiment of predicting the actual energy consumption of a building, the model evaluation results show that 20% of training data is reduced at the expense of 0.4% classification accuracy. Compared with the traditional non-rolling method, the Root Mean Square Error (RMSE) of the moving horizon LSTM prediction method is reduced by 33.08% on average, and the training speed is increased by 5.25% on average. The prediction effect is better. Therefore, this prediction model can be used to predict building energy consumption quickly and accurately and has strong robustness and generalization ability, which provides the theoretical basis and method support for fine management of building energy consumption, building energy conservation, and emission reduction.