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With the development of economy and society, the energy demand increasing day by day. Building is one of the biggest energy consumption sectors, the park is composited of buildings. The build of zero carbon park is important to the decarbonizing in the building area. Based on the current status of the lack of smart energy system application in zero-carbon park, this paper mainly focuses on the implementation path of low-carbon smart energy systems in the parks, through the process of load prediction, energy demand analysis, system configuration, optimization and the development of energy management platform. A case study of zero -carbon park is analyzed to provide theory guide for future study.

In industry 4.0 environment, intelligent technology is almost applied to all parts of the manufacturing process, such as process design, job shop scheduling, etc.. This paper presents an efficient approach to job shop scheduling actively by using RFID to collect real-time manufacturing data. Identified the workpiece by RFID which needs to be machined, it can “ask for” the resource actively for the following process. With these active asking-for strategy, a double genetically encoded improved genetic algorithm is proposed for achieving active job shop scheduling solution during the actual manufacturing process. A case was used to evaluate its effectiveness. Meanwhile, , it can effectively and actively carry out job shop scheduling and has much better convergence effect comparing with basic genetic algorithm. And the job shop scheduler in management center can use the proposed algorithm to get the satisfied scheduling result timely by reducing waiting time and making begin time earlier during transmission between manufacturing process, which makes the scheduling result feasible and accurate.