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At the present time brownfield sites do not have a common definition yet. Each nation has already defined the term Brownfield based on their prioritized targets due to their diverse organizational, geographical, social and economic statuses. In Iranian context, any types of previously developed land—whether contaminated or not—and old urban property that its existence has perceived negative influences on the coherence of the surrounding land are defined as a brownfield site. These sites are mostly recognized as inner-city urban fabrics affected by former industrial and military actions that have considerable potential for regeneration practice. Mehrabad airport, as the third oldest airport in Iran, is currently dealing with various problems and constraints that not only put this immense urban space through adverse condition but also caused considerable difficulties for the surrounding residential neighborhoods. This article is basically intended to explore the recent environmental, physical and socio-economic problems of Mehrabad airport in order to raise the question as to whether this airport can be viewed as being a brownfield site or not.
Energy management is being highly regarded throughout the world. High-energy consumption in residential buildings is one of the dominant reasons of excessive energy consumption. There are many recent works on the demand-side management (DSM) and smart homes to keep control on electricity consumption. The paper is an intelligence to modify patterns, by proposing a time scheduling consumers, such that they can maintain their welfare while saving benefits from time varying tariffs; a model of household loads is proposed; constraints, including daily energy requirements and consumer preferences are considered in the framework, and the model is solved using mixed integer linear programming. The model is developed for three scenarios, and the results are compared: the 1st scenario aims Peak Shaving; the 2nd minimizes Electricity Cost, and the 3rd one, which distinguishes this study from the other related works, is a combination of the 1st and 2nd Scenarios. Goal programming is applied to solve the 3rd scenario. Finally, the best schedules for household loads are presented by analyzing power distribution curves and comparing results obtained by these scenarios. It is shown that for the case study of this paper with the implementation of 3rd scenario, it is possible to gain 7% saving in the electricity cost without any increasing in the lowest peak power consumption.