The government in Maharashtra are trying to rehabilitate the informal settlements in Mumbai through the slum rehabilitation authority and its rehabilitation scheme and we wanted to learn more about how it worked and how it affects living conditions for former residents of an informal settlement and how it can differ from those still living there. This study has a focus on a specific informal settlement in Mumbai, called Gilbert Hill. Through observations and interviews with people living in the area, the research investigates resident’s opinions and experiences. Interviews with officials, involved in the planning of Mumbai were conducted, in order to learn how the interaction between formal and informal structures looks like. The results will be presented in this report and will give an insight of the life in an informal settlement in Mumbai as well as the thoughts and efforts with the rehabilitation of said settings. Our findings have been analysed and discussed in the light of David Harvey’s The right to city, displacement theories, theories about the public private partnership and what it is that makes informal settlements to persist. We have discovered that it is not as easy as to just redevelop an informal settlement according to a uniform plan. Residents have special bonds to the neighbourhood, both between each other and to the place itself. Even though the residents might be viewed as impoverished and underprivileged they still should have a right to form their future as well as their surroundings. They certainly have a will to do so.

Indoor air quality is a measure of clean air with comfort conditions and depiction of lower concentration of air pollutants. It is tedious task to achieve all quality measures at a time with smart energy consumption. This research aims to come up with a solution of how to improve smart ventilation system in order to get clean indoor air with less consumption of electric energy. Many studies showed that scheduled ventilation system has proven to be a good solution to this problem. For this purpose, a long-term sensor data of smart ventilation system Renson healthbox and Luvians data is studied which is operated in Dalarnas villa. This research investigates how this system works in two modes and to improve it by customized scheduling.A regression model is constructed in which the relationship between airflow and CO2 is shown. For this purpose, correlation analysis is used in which the connection of bonds between each data features are analyzed. After the feature selection, as a result from correlation matrix, regression analysis is used to find out whether the selected features are linearly related or not. Regression analysis also used for the intent to quantify a model to estimate the flowrate and CO2. A mathematical model is also build to simulate the flowrate and CO2 with energy consumption.The results showed that, in order to provide better indoor air quality with efficient energy consumption, a necessary modification of the fan schedule should be done in a way that fan must be started little bit earlier to avoid harmful particles reach their upper threshold limits. This can result in reduction of fan’s maximum speed hence consumption of less energy is achieved.

It is a dilemma problem to achieve both these two goals: a) to maintain a best indoor air quality and b) to use a most efficient energy for a house at the same time. One of the outstanding components involving these goals is a smart ventilation system in the house. Smart ventilation strategies, including demand-controlled ventilation (DCV), have been of great interests and some studies believe that DCV strategies have the potential for energy reductions for all ventilation systems. This research aims to improve smart ventilation system, in aspects of energy consumption, indoor CO2 concentrations and living comfortness, by analyzing long-term sensor data. Based on a case study on an experimental house -- Dalarnas Villa, this research investigates how the current two ventilations modes work in the house and improves its ventilation system by developing customized ventilation schedules. A variety of data analysis methods were used in this research. Clustering analysis is used to identify the CO2 patterns and hence determine the residents living patterns; correlation analysis and regression analysis are used to quantify a model to estimate fan energy consumption; a mathematical model is built to simulation the CO2 decreasing when the house is under 0 occupancy. And finally, two customized schedules are created for a typical workday and holiday, respectively, which show advantages in all aspects of energy consumption, CO2 concentrations and living comfortness, compared with the current ventilation modes.