A kind of new combined modeling method with GM(1,1) and RBNN (Radial Basis Neural Network) is brought forward, according to the idea that the method of neural network can bring grey prediction model a good modified effect. Based on the analysis of the energy consumption data of the existing and the annually-increased building area, the GM(1,1) model was then constructed. And the RBF neural network was used for the model residual error revising. The simulation and experiment results show that the novel model is more effective than the common grey model.

In the cloud-based vehicular ad-hoc network (VANET), massive vehicle information is stored on the cloud, and a large amount of data query, calculation, monitoring, and management are carried out at all times. The secure spatial query methods in VANET allow authorized users to convert the original spatial query to encrypted spatial query, which is called query token and will be processed in ciphertext mode by the service provider. Thus, the service provider learns which encrypted records are returned as the result of a query, which is defined as the access pattern. Since only the correct query results that match the query tokens are returned, the service provider can observe which encrypted data are accessed and returned to the client when a query is launched clearly, and it leads to the leakage of data access pattern. In this paper, a reconstruction attack scheme is proposed, which utilizes the access patterns in the secure query processes, and then it reconstructs the index of outsourced spatial data that are collected from the vehicles. The proposed scheme proves the security threats in the VANET. Extensive experiments on real-world datasets demonstrate that our attack scheme can achieve quite a high reconstruction rate.

In a VAV system the static pressure can be reseted based on the change of load. At low load conditions the duct static pressure can be adjusted lower, that make VAV terminal air valves in large opening as far as possible.And lower fan outlet pressure can effectively reduce fan energy consumption. This paper presents a method using iterative algorithm to control the fan frequency. It can improve the dynamic quality of the control system. Satisfying the requirements under lower static pressure so as to achieve the purpose of energy-saving.

In this paper, a new prediction model named RBNN-GM(1,1) (Radial Basis Neural Network-Grey Model) model was constructed and used for the analysis of building subsidence prediction for the Palms Together Dagoba in Famen Temple in Shaanxi Province in China. The constructed model can make full use of the advantages of few samples and little information predicting in Grey Theory and swift and self-learning in RBNN. The prediction results show that the combined model is more effective than the common grey model. The proposed combined model for building subsidence prediction may offer scientific rationale for estimating whether the building transmutation exceeds the criterion and provide reference for taking the corresponding safety measures.

The variable air volume (VAV) air-conditioning system with its superior energy saving effect has been applied in many developed countries, also drawn more and more attention in China. Due to the characteristics of VAV air-conditioning system are nonlinear, large time lag, strong coupling, multi-variable and multi-disturbance, the control complexity of the VAV system increase greatly. The energy conservation and comfort of the VAV air-conditioning system can not fully been embodied in application. The research on the VAV system has become the hot spot in the science and technology workers, many new methods and strategies have emerged. From the three aspects: the air system, the water system and the large scale system, the paper describes the control strategies, the control methods and the research progress about the VAV air-conditioning system.