Input Variables Of Prediction Model Research Articles

A hybrid method of cooling load forecasting for large commercial building based on extreme learning machine

Air conditioning system is extensively used in large commercial buildings. The fast and accurate building cooling load forecasting is the basis for improving the operation efficiency of the air conditioning system, which is conducive to implement the effective management of the air conditioning system. Therefore, a hybrid prediction model based on random forest-improvement parallel whale optimizing-extreme learning machine neural network (RF-IPWOA-ELM) is proposed to predict the cooling load of large commercial buildings. First, the influence of different parameters on the cooling load is analyzed, and the random forest (RF) method is used to extract the parameters with high degree of influence as the input variables of prediction model. Then, the extreme learning machine (ELM) optimized by the improved parallel whale optimization algorithm (IPWOA) is established to predict. Finally, a simulation experiment is carried out using measured data of two large commercial buildings in north of China. The experimental results show that the root mean square error (RMSE) and mean average percentage error (MAPE) of RF-IPWOA-ELM predicting the cooling load for these two buildings are 2.8735, 0.2% and 4.7721, 0.45%, respectively. Compared with the other prediction model, the RMSE and MAPE of this model are reduced by 66.17%–90.62%, 81.48%–95.79% and 71.91%–84.40%, 74.14%–86.15%, respectively, which has higher prediction accuracy. Simultaneously, for different prediction models, RF-IPWOA-ELM has a shorter prediction time, which presents superiority in time complexity. And when there are few training samples, RF-IPWOA-ELM can still effectively predict the cooling load in different months, indicating that it possesses strong generalization ability. Therefore, the proposed hybrid model can be used as a reliable tool for cooling load prediction in the energy conservation of air conditioning system and energy management.

Weighted Elastic Net Model for Mass Spectrometry Imaging Processing

In proteomics study, Imaging Mass Spectrometry (IMS) is an emerging and very promis- ing new technique for protein analysis from intact biological tissues. Though it has shown great potential and is very promising for rapid mapping of protein localization and the detection of size- able differences in protein expression, challenges remain in data processing due to the difficulty of high dimensionality and the fact that the number of input variables in prediction model is signifi- cantly larger than the number of observations. To obtain a complete overview of IMS data and find trace features based on both spectral and spatial patterns, one faces a global optimization problem. In this paper, we propose a weighted elastic net (WEN) model based on IMS data processing needs of using both the spectral and spatial information for biomarker selection and classification. Prop- erties including variable selection accuracy of the WEN model are discussed. Experimental IMS data analysis results show that such a model not only reduces the number of side features but also helps new biomarkers discovery.