Energy demand forecasting plays an important role in decision making. A mathematical model known as grey model GM(1,1) has been, herewith, employed successfully in the estimation of energy demand. In order to improve the forecast accuracy, the original GM(1,1) models are improved by using three methodologies of the 3-points average technology and the residual modification. This method takes into account the general trend series and random fluctuations about this trend. Two experiments were carried out respectively on the electricity demand and energy production from 1984 to 2006 in China to demonstrate the effectiveness of our approach. Furthermore, this improved grey forecasting model was used to forecast China's electricity demand and energy production, which shows that the modified forecasting model is more reliable and has a higher forecast accuracy than the GM (1,1). The forecasted results indicate that China's final energy demand will increase rapidly in the period 2007-2015. The results provide scientific basis for the planned development of energy supply in China.
Abstract.One of the tasks of smart city construction is intelligent traffic management. The building of intelligent traffic signal system is the infrastructure project. It has the functions of the real-time perception of traffic flow in the urban road, and taking the traffic signals connect to the Internet for the service of the real-time road condition information to user. The building is consisted of the sensor module, the control center and the controller module. Through the data from the sensor module detected on the road traffic situation, the control center can determine the most suitable scheme to dynamically regulation and send instructions to the traffic lights to automate the process. So it can achieve the intelligence shunt and ease the congestion in the road.
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With the rapid development and spread of information technology, mobile (M)-healthcare is emerging as a feasible option for improved monitoring and treatment of patients with chronic diseases such as diabetes, particularly for patients with limited access to medical institutions. Our study group has developed and evaluated a M-healthcare system that provides remote guidance for diabetic patients. This paper introduces the diabetic M-healthcare system and describes progress in implementation for remote and mobile healthcare in China. We provide evidence that this system improves diabetic patient management and can contribute to the establishment of a new model for patient-centered mobile healthcare.
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