Filtering by
- All Subjects: Energy consumption
- All Subjects: High speed trains
- Creators: Barnes, Elizabeth
- Creators: Chong, Oswald W
The dissertation utilizes Interval Data (ID) and establishes three different frameworks to identify electricity losses, predict electricity consumption and detect anomalies using data mining, deep learning, and mathematical models. The process of energy analytics integrates with the computational science and contributes to several objectives which are to
1. Develop a framework to identify both technical and non-technical losses using clustering and semi-supervised learning techniques.
2. Develop an integrated framework to predict electricity consumption using wavelet based data transformation model and deep learning algorithms.
3. Develop a framework to detect anomalies using ensemble empirical mode decomposition and isolation forest algorithms.
With a thorough research background, the first phase details on performing data analytics on the demand-supply database to determine the potential energy loss reduction potentials. Data preprocessing and electricity prediction framework in the second phase integrates mathematical models and deep learning algorithms to accurately predict consumption. The third phase employs data decomposition model and data mining techniques to detect the anomalies of institutional buildings.
This LCA used data from a previous LCA done by Chester and Horvath (2012) on the proposed California High Speed Rail, and furthered the LCA to look into potential changes that can be made to the proposed CAHSR to be more resilient to climate change. This LCA focused on the energy, cost, and GHG emissions associated with raising the track, adding fly ash to the concrete mixture in place of a percentage of cement, and running the HSR on solar electricity rather than the current electricity mix. Data was collected from a variety of sources including other LCAs, research studies, feasibility studies, and project information from companies, agencies, and researchers in order to determine what the cost, energy requirements, and associated GHG emissions would be for each of these changes. This data was then used to calculate results of cost, energy, and GHG emissions for the three different changes. The results show that the greatest source of cost is the raised track (Design/Construction Phase), and the greatest source of GHG emissions is the concrete (also Design/Construction Phase).