Browsing by Author "Alfadda, Abdullah Ibrahim A."

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    Strategies for Managing Cool Thermal Energy Storage with Day-ahead PV and Building Load Forecasting at a District Level
    Alfadda, Abdullah Ibrahim A. (Virginia Tech, 2019-09-09)
    In hot climate areas, the electrical load in a building spikes, but not by the same amount daily due to various conditions. In order to cover the hottest day of the year, large cooling systems are installed, but are not fully utilized during all hot summer days. As a result, the investments in these cooling systems cannot be fully justified. A solution for more optimal use of the building cooling system is presented in this dissertation using Cool Thermal Energy Storage (CTES) deployed at a district level. Such CTES systems are charged overnight and the cool charge is dispatched as cool air during the day. The integration of the CTES helps to downsize the otherwise large cooling systems designed for the hottest day of the year. This reduces the capital costs of installing large cooling systems. However, one important question remains - how much of the CTES should be charged during the night, such that the cooling load for the next day is fully met and at the same time the CTES charge is fully utilized during the day. The solution presented in this dissertation integrated the CTES with Photovoltaics (PV) power forecasting and building load forecasting at a district level for a more optimal charge/discharge management. A district comprises several buildings of different load profiles, all connected to the same cooling system with central CTES. The use of forecasting for both the PV and the building cooling load allows the building operator to more accurately determine how much of the CTES should be charged during the night, such that the cooling system and CTES can meet the cooling demand for the next day. Using this approach, the CTES would be optimally sized, and utilized more efficiently during the day. At the same time, peak load savings are achieved, thus benefiting an electric utility company. The district presented in this dissertation comprises PV panels and three types of buildings – a mosque, a clinic and an office building. In order to have a good estimation for the required CTES charge for the next day, reliable forecasts for the PV panel outputs and the electrical load of the three buildings are required. In the model developed for the current work, dust was introduced as a new input feature in all of the forecasting models to improve the models' accuracy. Dust levels play an important role in PV output forecasts in areas with high and variable dust values. The overall solution used both the PV panel forecasts and the building load forecasts to estimate the CTES charge for the next day. The presented method was tested against the baseline method with no forecasting system. Multiple scenarios were conducted with different cooling system sizes and different CTES capacities. Research findings indicated that the presented method utilized the CTES charge more efficiently than the baseline method. This led to more savings in the energy consumption at the district level.
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    Temporal Frame Difference Using Averaging Filter for Maritime Surveillance
    Alfadda, Abdullah Ibrahim A. (Virginia Tech, 2015-09-04)
    Video surveillance is an active research area in Computer Vision and Machine Learning. It received a lot of attention in the last few decades. Maritime surveillance is the act of effective detection/recognition of all maritime activities that have impact on economy, security or the environment. The maritime environment is a dynamic environment. Factors such as constant moving of waves, sun reflection over the sea surface, rapid change in lightning due to the sun reflection over the water surface, movement of clouds and presence of moving objects such as airplanes or birds, makes the maritime environment very challenging. In this work, we propose a method for detecting a motion generated by a maritime vehicle and then identifying the type of this vehicle using classification methods. A new maritime video database was created and tested. Classifying the type of vehicles have been tested by comparing 13 image features, and two SVM solving algorithms. In motion detection part, multiple smoothing filters were tested in order to minimize the false positive rate generated by the water surface movement, the results have been compared to optical flow, a well known method for motion detection.