Spatiotemporal Data Augmentation of MODIS-LANDSAT Water Bodies Using Generative Adversarial Networks

The monitoring of the shape and area of a water body is an essential component for many Earth science and Hydrological applications. For this purpose, these applications require remote sensing data which provides accurate analysis of the water bodies. In this thesis the same is being attempted, first, a model is created that can map the information from one kind of satellite that captures the data from a distance of 500m to another data that is captured by a different satellite at a distance of 30m. To achieve this, we first collected the data from both of the satellites and translated the data from one satellite to another using our proposed Hydro-GAN model. This translation gives us the accurate shape, boundary, and area of the water body. We evaluated the method by using several different similarity metrics for the area and the shape of the water body. The second part of this thesis involves augmenting the data that we obtained from the Hydro-GAN model with the original data and using this enriched data to predict the area of a water body in the future. We used the case study of Great Salt lake for this purpose. The results indicated that our proposed model was creating accurate area and shape of the water bodies. When we used our proposed model to generate data at a resolution of 30m it gave us better areal and shape accuracy. If we get more data at this resolution, we can use that data to better predict coastal lines, boundaries, as well as erosion monitoring

Load Forecasting Analysis Using Contextual Data and Integration With Microgrids Used For Off Grid EV Charging Stations

Electricity is an essential component of the smooth working of every sector. If a successful prediction of how much electricity will be required for say the next 24 hours or 48 hours can be made, it will not only help in efficiently planning the activities and operations but also help in minimizing the cost incurred. In this thesis the same is being attempted, first, a model is created that can predict the energy consumption of households using various tools available. To achieve this, historical data of the past 5 years that has been recorded in London has been used. Secondly, a model is created that can forecast future energy requirements of EV charging stations, which will help in optimizing their working in off-grid areas. To achieve this, data that has been recorded for more than 100 charging stations across the Salt Lake area for around 4 years has been used. The second part of this thesis involves integrating the load forecasting model with the solar energy forecasting model and microgrid optimization. Since microgrid can disconnect from the energy source and work autonomously, solar energy can be used to generate power that can be used instead of the energy that is bought from the grid. This will help in building an efficient model that maximizes the profit by making sure energy is bought at a minimum price and the same energy is being utilized when the demand is high