The incorporation of renewable energies and power storage at distribution facilities are one of the important features in the smart grid. In this paper, a hybridized intelligent home renewable energy management system (HIHREM) that combines solar energy and energy storage services with the smart home is planned based on the demand response and time of consumption pricing is applied to programs that offer discounts to consumers that reduce their energy consumption during high demand periods. The system is designed and handled with minimal energy requirements at home through installation of renewable energy, preparation, and arrangement of power stream during peak and off-peak periods. The best energy utilization of residential buildings with various overlapping purposes is one of the most difficult issues correlated with the implementation of intelligent micro-network systems. A major component of the smart grid, the domestic energy control system (HIHREM) provides many benefits, such as power bill reductions, reduction in wind generation, and demand compliance. This showed that the proposed energy scheduling method minimizes the energy consumption by 48% and maximizes the renewable energy consumed at the rate 65% of the total energy generated. A new model for smart homes with renewable energies is introduced in this report. The proposed HIHREM method achieves high performance and reduces cost-utility.

The location selection of warehouses is considered to be a strategic decision for logistics enterprises in improving the quality of logistic services. In order to improve the science and accuracy of enterprise warehouse location...

University-industry collaboration networks consist of heterogeneous subjects; in fact, the interactions among these subjects lead to the emergence of the complex characteristics of university-industry collaboration networks and even to the emergence of the networks themselves. Thus, understanding the behavior that generates university-industry collaboration networks is essential to their construction and optimization. To more fully comprehend this generating behavior, we considered a university-industry collaboration network in the ocean energy industry in China as a case study. Employing the motif analysis method, we analyze the network’s generating behavior from four perspectives: motif characteristics, motif classification, motif combination and motif clustering. Furthermore, we explore agents’ modes of adaptive collaboration — and the causes of such collaborations — and summarize the characteristics and modes of networks’ generating behavior. The results indicate that universities and industries tend to form highly stable and close cooperative relationships. The constraints of the network’s generating behavior — which influences the subjects’ interactions through relational and structural embeddedness — include asymmetric information, resource and cost constraints, risk aversion and geographical constraints. Our research presents the benefits of using the motif analysis method to study the network’s generating behavior and its implications in constructing and optimizing a university-industry collaboration network.