Getting to Net Zero Energy Buildings: A Holistic Techno-ecological Modeling Approach

Buildings in the United States are responsible for more than 40% of the primary energy and 70% of electricity usage and greatly in CO2 emission by about 39%, more than any other sector including transportation and industry sectors. This energy consumption is expected to grow mainly due to increasing trends in new buildings construction. Rising energy prices alongside with energy independencies, limited resources, and climate change have made the current situation even worse. An Energy Efficient (EE) building is able to reduce the heating and cooling load significantly compared with a code compliant building. Furthermore, integrating renewable energy sources in the building energy portfolio could drive the building\u27s grid reliance further down. Such buildings that are able to passively save and actively produce energy are called Net Zero Energy Buildings (NZEB). Despite all new energy efficient technologies, reaching NZEB is challenging due to high first cost of super-efficient measures and renewable energy sources as well as integration of the newly on-site generated electricity to the grid. Achieving NZEB without looking at its surrounding environment may result in sub-optimal solutions. Currently, 95% of American households own a car, and with the help of newly introduced Vehicle to Home (V2H) technologies, building, vehicle, renewable energy sources, and ecological environment can work together as a techno-ecological system to fulfill the requirement of an NZEB ecosystem. Due to the great flexibility of electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEVs) in interacting with the power grid, they will play a significant role in the future of the power system. In a large scale, an organized fleet of EVs can be considered as reliable and flexible power storage for a set of building blocks or in a smaller scale, individual EV owners can use their own vehicles as a source of power alongside with other sources of power. To this end, V2H technologies can utilize idle EV battery power as an electricity storage tool to mitigate fluctuations in renewable electric power supply, to provide electricity for the building during the peak time, and to help in supplying electricity during emergency situation and power outage. V2H is said to be the solution to a successful integration of renewables and at the same time maintaining the integrity of the grid. This happens through depleting the stored power in the battery of EV and then charging the battery when the demand is low, using the electricity provided by grid or renewables. Government incentives can play an important role in employing this technology by buying out the high first time cost request. According to Energy Information Administration (EIA), U.S. residential utility customers consume 29.95 kWh electricity on average per household-day. With the current technology, EV batteries could store up to 30 kWh electricity. As a result, even for a code compliant house, a family could use EV battery as a source of energy for one normal day operation. For an energy efficient home, there could even be a surplus of energy that could be transferred to the grid. In summary, Achieving NZEB is facing various obstacles and removing these barriers require a more holistic view on a greater system and environment, where a building interacts with on-site renewable energy sources, EV, and its surrounded ecological environment. This dissertation aims to utilize the application of Vehicle to Home technology to reach NZEB by developing two new models in two phases; the macro based excel model (NZEB-VBA) and agent based model (NZEB-ABM). Using these two models, homeowners can calculate the savings through implementing abovementioned technologies which can be considered as a motivation to move toward greener buildings. In the first step, an optimization analysis is performed first to select the best design alternatives for an energy-efficient building under the relevant economic and environmental constraints. Next, solar photovoltaic sources are used to supply the building\u27s remaining energy demand and thereby minimize the building\u27s grid reliance. Finally, Vehicle to Home technology is coupled with the renewable energy source as a substitute for power from the grid. The whole algorithm for this process will be running in the visual basic environment. In the second phase of the study, the focus is more on the dynamic interaction of different components of the system with each other. Although the general procedure is the same, the modeling will take place in a different environment. Showing the status of different parts of the system at any specific time, changing the values of different parameters of the system and observing the results, and investigating the impact of each parameter\u27s on overall behavior of the system are among the advantages of the agent based model. Having real time data can greatly enhance the capabilities of this system. The results indicate that, with the help of energy-efficient design features and a properly developed algorithm to draw electricity from EV and solar energy, it is possible to reduce the required electricity from the power grid by 59% when compared to a standard energy-efficient building and by as much as 90% when compared to a typical code-compliant building. This thereby reduces the electricity cost by 1.55 times the cost of the conventional method of drawing grid electricity. This savings can compensate the installation costs of solar panels and other technologies necessary for a Net Zero Energy Building. In the last phase of the study, a regional analysis will be performed to investigate the effect of different weather conditions, traffic situation and driving behavior on the behavior of this system

Circular Update Directional Virtual Coordinate Routing Protocol In Sensor Networks

In a wireless sensor network, virtual coordinates provide most of the advantages of geographic routing strategies without actually relying on the location information of the nodes. Using a mobile sink provides advantages such as distributing energy consumption throughout the network. However, nodes need to be updated about the new virtual coordinate of the mobile sink as it moves. In this paper, we propose Circular Update-Directional Virtual Coordinate Routing (CU-DVCR), an algorithm specialized in routing towards a mobile sink in virtual coordinates. Through a set of experimental studies we show that CU-DVCR consumes less energy compared to alternative algorithms while providing comparable performance

Decision-making for Vehicle Path Planning

This dissertation presents novel algorithms for vehicle path planning in scenarios where the environment changes. In these dynamic scenarios the path of the vehicle needs to adapt to changes in the real world. In these scenarios, higher performance paths can be achieved if we are able to predict the future state of the world, by learning the way it evolves from historical data. We are relying on recent advances in the field of deep learning and reinforcement learning to learn appropriate world models and path planning behaviors. There are many different practical applications that map to this model. In this dissertation we propose algorithms for two applications that are very different in domain but share important formal similarities: the scheduling of taxi services in a large city and tracking wild animals with an unmanned aerial vehicle. The first application models a centralized taxi dispatch center in a big city. It is a multivariate optimization problem for taxi time scheduling and path planning. The first goal here is to balance the taxi service demand and supply ratio in the city. The second goal is to minimize passenger waiting time and taxi idle driving distance. We design different learning models that capture taxi demand and destination distribution patterns from historical taxi data. The predictions are evaluated with real-world taxi trip records. The predicted taxi demand and destination is used to build a taxi dispatch model. The taxi assignment and re-balance is optimized by solving a Mixed Integer Programming (MIP) problem. The second application concerns animal monitoring using an unmanned aerial vehicle (UAV) to search and track wild animals in a large geographic area. We propose two different path planing approaches for the UAV. The first one is based on the UAV controller solving Markov decision process (MDP). The second algorithms relies on the past recorded animal appearances. We designed a learning model that captures animal appearance patterns and predicts the distribution of future animal appearances. We compare the proposed path planning approaches with traditional methods and evaluated them in terms of collected value of information (VoI), message delay and percentage of events collected