Commercial buildings in the United States account for 19% of the total energy consumption annually. Commercial Building Energy Consumption Survey (CBECS), which serves as the benchmark for all the commercial buildings provides critical input for EnergyStar models. Smart energy management technologies, sensors, innovative demand response programs, and updated versions of certification programs elevate the opportunity to mitigate energy-related problems (blackouts and overproduction) and guides energy managers to optimize the consumption characteristics. With increasing advancements in technologies relying on the ‘Big Data,' codes and certification programs such as the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE), and the Leadership in Energy and Environmental Design (LEED) evaluates during the pre-construction phase. It is mostly carried out with the assumed quantitative and qualitative values calculated from energy models such as Energy Plus and E-quest. However, the energy consumption analysis through Knowledge Discovery in Databases (KDD) is not commonly used by energy managers to perform complete implementation, causing the need for better energy analytic framework.

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.

In the United States, buildings account for 20–40% of the total energy consumption based on their operation and maintenance, which consume nearly 80% of their energy during their lifecycle. In order to reduce building energy consumption and related problems (i.e. global warming, air pollution, and energy shortages), numerous building technology programs, codes, and standards have been developed such as net-zero energy buildings, Leadership in Energy and Environmental Design (LEED), and the American Society of Heating, Refrigerating, and Air-Conditioning Engineers 90.1. However, these programs, codes, and standards are typically utilized before or during the design and construction phases. Subsequently, it is difficult to track whether buildings could still reduce energy consumption post construction. This dissertation fills the gap in knowledge of analytical methods for building energy analysis studies for LEED buildings. It also focuses on the use of green space for reducing atmospheric temperature, which contributes the most to building energy consumption. The three primary objectives of this research are to: 1) find the relationship between building energy consumption, outside atmospheric temperature, and LEED Energy and Atmosphere credits (OEP); 2) examine the use of different green space layouts for reducing the atmospheric temperature of high-rise buildings; and 3) use data mining techniques (i.e. clustering, isolation, and anomaly detection) to identify data anomalies in the energy data set and evaluate LEED Energy and Atmosphere credits based on building energy patterns. The results found that buildings with lower OEP used the highest amount of energy. LEED OEP scores tended to increase the energy saving potential of buildings, thereby reducing the need for renovation and maintenance. The results also revealed that the shade and evaporation effects of green spaces around buildings were more effective for lowering the daytime atmospheric temperature in the range of 2°C to 6.5°C. Additionally, abnormal energy consumption patterns were found in LEED buildings that used anomaly detection methodology analysis. Overall, LEED systems should be evaluated for energy performance to ensure that buildings continue to save energy after construction.