John Kim

How can microgrids enable cheaper and faster integration of renewables to meet energy demand?

Started June 13, 2025

Abstract or project description

While staying in the scope of the residential-scale applications, the project investigates how microgrids can enable cheaper and faster integration of renewable energy to meet growing energy demand. Through a deep research analysis, Shomik and I went through a deep technical and economic examination of microgrid development, while also comparing them to traditional grid systems. A greater emphasis was put on interconnection costs, regulatory challenges, and system complexity, as well as the potential for microgrids to accelerate renewable adoption. Through the use of modeling tools, such as SAMA and Python, we were able to evaluate energy flows, cost breakdowns, and operational performance.

A web app for monitoring energy consumption in smart homes

Started June 25, 2025

Abstract or project description

In this project, a web app is developed to monitor energy consumption in smart homes. This includes building software to emulate smart sensors readings. Then, sending these sensor readings to servers to store the information and process them. And a web interface design to respond to web requests to show the readings of the sensors and make actions in the smart homes such as turning on/off appliances.

Energy Consumption Prediction Model

Started June 12, 2024

Abstract or project description

In the past century, energy usage has been spiking rapidly, leaving many under its influence to be negatively affected (Boston University). From an economic standpoint, people who utilize a mass of their energy at peak hours are subjected to higher costs, due to the higher demand for electricity during those times. From an environmental standpoint, an uncontrolled use of energy can lead to a spike in various types of pollution and climate change (ScienceDaily). Hence, these periodic energy fluctuations need to be properly analyzed to predict future energy usage and understand how much more it needs to be reduced over time. To yield an accurate set of predictive data, our machine-learning model will utilize supervised learning to take in past energy usage data from various energy factors to predict future energy usage. This model will maintain the energy consumption rate by analyzing the historical energy usage from January to June of 2007, a time range that encapsulates over 240,000 household energy records. In order to maintain the quantity and quality of the data collection we will utilize data discovery by searching and sharing datasets among a variety of sources on the web. We tested linear regression, polynomial regression, K-nearest neighbors, and neural networks to predict energy usage based on a variety of factors. In this case, we analyzed the global active power, global reactive power, global intensity, and voltage coming from the three submeterings. More specifically, these submeterings focused on the power coming from the kitchen, laundry room, and small portable appliances.