With growing urbanization, it has become necessary to manage this growth smartly. Specifically, increased electrical energy consumption has become a rapid urbanization trend in China. A building model based on a neural network was proposed to overcome the difficulties of analytical modelling. However, increased amounts of data, repetitive computation, and training time become a limitation of this approach. A simplified model can be used instead of the full order model if the performance is acceptable. In order to select effective data, Mean Impact Value (MIV) has been applied to select meaningful data. To verify this neural network method, we used real electricity consumption data of a shopping mall in China as a case study. In this paper, a Bayesian Regularization Neural Network (BRNN) is utilized to avoid overfitting due to the small amount of data. With the simplified data set, the building model showed reasonable performance. The mean of Root Mean Square Error achieved is around 10% with respect to the actual consumption and the standard deviation is low, which reflects the model’s reliability. We also compare the results with our previous approach using the Levenberg–Marquardt back propagation (LM-BP) method. The main difference is the output reliability of the two methods. LM-BP shows higher error than BRNN due to overfitting. BRNN shows reliable prediction results when the simplified neural network model is applied.

One of key technologies for future large-scale location-aware services in access is a scalable indoor localization technique. In this paper, we report preliminary results from our investigation on the use of deep neural networks (DNNs) for hierarchical building/floor classification and floor-level location estimation based on Wi-Fi fingerprinting, which we carried out as part of a feasibility study project on Xi'an Jiaotong-Liverpool University (XJTLU) Campus Information and Visitor Service System. To take into account the hierarchical nature of the building/floor classification problem, we propose a new DNN architecture based on a stacked autoencoder for the reduction of feature space dimension and a feed-forward classifier for multi-label classification with arg max functions to convert multi-label classification results into multi-class classification ones. We also describe the demonstration of a prototype DNN-based indoor localization system for floor-level location estimation using real received signal strength (RSS) data collected at one of the buildings on the XJTLU campus. The preliminary results for both building/floor classification and floor-level location estimation clearly show the strengths of DNN-based approaches, which can provide near state-of-the-art performance with less parameter tuning and higher scalability.

<p> In this work, we proposed multi-scale screening, which employs both molecular and process-level models, to identify high-performing MOFs for energy-efficient separation of SF6 from SF₆ and N₂ mixture. Grand canonical Monte Carlo (GCMC) simulations were combined with ideal adsorption process simulation to computationally screen 14,000 metal-organic frameworks (MOFs) for adsorptive separation of SF6/N₂. More than 150 high-performing MOFs were identified based on the results from GCMC simulations at the pressure and vacuum swing conditions, and subsequently evaluated using the ideal adsorption process simulation. High-performing MOFs selected for the VSA conditions are able to achieve the 90% target purity level of SF6 but none of the selected MOFs for PSA conditions could. Cascade PSA configuration was proposed and adopted to improve the purity level of the separated SF6. Cascade PSA configuration was also adopted to improve the purity. In the pump efficiency scenarios of 80, 20, and 10%, the VSA and cascade PSA cases were compared, which concluded 10% scenario prefers the PSA case whereas the VSA case is favored in the others. Top-performing MOFs identified from the multi-scale computational approach were found to be able to produce 90% purity SF₆ with 0.10 - 0.4 and 0.5 - 1.4 MJ per kg of SF6 for VSA and PSA, respectively.</p>

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