Intelligent Prediction Method of Building Energy Consumption Based on Deep Learning

Abstract

Building energy consumption prediction plays an important role in realizing building energy conservation control. Limited by some external factors such as temperature, there are some problems in practical applications, such as complex operation and low prediction accuracy. Aiming at the problem of low prediction accuracy caused by poor timing of existing building energy consumption prediction methods, a building energy consumption prediction and analysis method based on the deep learning network is proposed in this paper. Before establishing the energy consumption prediction model, the building energy consumption data source is preprocessed and analyzed. Then, based on the Keras deep learning framework, an improved long short-term memory (ILSTM) prediction model is built to support the accurate analysis of the whole cycle of the prediction network. At the same time, the adaptive moment (Adam) estimation algorithm is used to update and optimize the weight parameters of the model to realize the adaptive and rapid update and matching of network parameters. The simulation experiment is based on the actual dataset collected by a university in Southwest China. The experimental results show that the evaluation indexes MAE and RMSE of the proposed method are 0.015 and 0.109, respectively, which are better than the comparison method. The simulation experiment proves that the proposed method is feasible.

Prediction Model of Wastewater Pollutant Indicators Based on Combined Normalized Codec

“…The constructed CNC model was used to predict the four pollutant indicators of COD, SS, TN, and TP in brewery wastewater treatment and compared with the classical prediction model. The proposed model's RMSE [47], MAE [48], and MAPE [49] indicators were 4.355, 3.113, and 1.007, and the R [50] index reached 0.975, which is better than the comparison model. The experimental results show that the model is more suitable for managing and applying wastewater treatment.…”

“…compares the predicted and actual values of each model. We can see that the RMSE[48], MAE[49], and MAPE[50] of the CNC model proposed in this paper are reduced by 1.5%, 3.2%, and 0.5%, respectively, and the R[51] indicator is increased by 0.1% compared with the suboptimal Codec model. The comparison results show that the model proposed in this paper has better performance indicators, and the prediction results are closer to the actual situation.…”

“…In this paper, four evaluation indicators are used to evaluate the experimental results: root mean square error (RMSE) [48], mean absolute error (MAE) [49], mean absolute percentage error (MAPE) [50], and Pearson correlation coefficient (R) [51]. All four evaluation indicators can measure the difference between the prediction value given by the model and the actual value and evaluate the model's performance.…”

“…All four evaluation indicators can measure the difference between the prediction value given by the model and the actual value and evaluate the model's performance. The smaller RMSE [48], MAE [49], and MAPE [50] values represent the minor difference between the prediction value given by the model and the actual value. In comparison, the larger R [51] values represent the model's better-fitting ability.…”

Prediction of Building Energy Consumption Based on BP Neural Network

“…Artificial neuron simply and abstractly describes the information transmission process of biological neurons. It is the smallest unit for neural network to control and process information [18]. Many artificial neurons with simple functions are associated through the topological structure to form a neural network.…”