Muhammad Sharif

Relationship between extreme temperature and electricity demand in Pakistan

Nowadays, different sectors of the economy are being significantly affected by the weather vagaries. Electricity market is one of the most sensitive sectors, due to the fact that electricity demand is connected to the numerous climatic variables, especially the atmospheric temperature. In this paper we have deduced the link between electricity consumption and mean monthly maximum temperature index in Pakistan, as a case study. ARIMA time series forecast model is developed for the temperature index. The forecast values of mean monthly maximum temperature shows an increasing trend. Linear trend model for electricity consumption is also developed as a function of temperature. Electricity consumption reveals a significant trend due to increase in temperature and socio- economic factors. The monthly behavior of our forecast values depicts that the electricity consumption is more for summer season, and this demand will be highest (6785.6 GWh) in July 2020, due to rise in temperature. Forecast model reveals that the electricity consumption (EC) and mean monthly maximum temperature are increasing with the passage of time.

Optimized Resource Allocation in Fog-Cloud Environment Using Insert Select

Energy management in modern way is done using cloud computing services to fulfill the energy demands of the users. These amenities are used in smart buildings to manage the energy demands. Entertaining maximum requests in minimum time is the main goal of our proposed system. To achieve this goal, in this paper, a scheme for resource distribution is proposed for cloud-fog based system. When the request is made by the user, the allocation of Virtual Machines (VMs) to the Data Centers (DCs) is required to be done timely for DSM. This model helps the DCs in managing the VMs in such a way that the request entertainment take minimum Response Time (RT). The proposed Insert Select Technique (IST) tackle this problem very effectively. Simulation results depicts the cost effectiveness and effective response time (RT) achievement.